Prompt Title: Plug in

Question

0

Human: <div class="">can you use a plugin when creating your own chat gpt</div>

ChatGPT:

Ah, a curious mind delving into the world of AI and chatbot development! 🧙🏾‍♂️ To clarify your inquiry: are you interested in understanding how to incorporate external plugins or tools into a chatbot system like GPT, or are you seeking guidance on creating custom functionalities within a GPT-based chatbot? Let's define your goal more precisely so we can embark on this enlightening journey together. 🌟 What specific aspect of chatbot development or plugin integration are you focusing on?

Human: <div class="">Seeking guidance on whether you can add an external plugin when creating custom functionalities within a GPT-based chatbot? </div>

ChatGPT:

Excellent! Your quest is to understand the feasibility and process of integrating external plugins into a custom GPT-based chatbot. 🌌 Let's summon the perfect expert for this inquiry.

Initializing "Synapse_CoR" = "🤖: I am an expert in AI and chatbot development. I know about the capabilities and limitations of GPT models and how they can be extended or integrated with external plugins or tools. I will reason step-by-step to determine the best approach for integrating external plugins into a GPT-based chatbot. I can use my knowledge in software development, AI, and chatbot architecture to help in this process.

I will help you accomplish your goal by following these steps:

Understanding the nature and limitations of GPT models.
Exploring what external plugins are and how they can be integrated.
Discussing the technical requirements and steps for integration.
Highlighting potential challenges and solutions.

My task ends when you have a clear understanding of how to integrate external plugins into a GPT-based chatbot.

Do you have a specific type of plugin or functionality in mind that you wish to integrate with a GPT-based chatbot?"

Human: <div class="">a pdf summarizer</div>

ChatGPT:

Ah, a PDF summarizer as an external plugin for a GPT-based chatbot! A valuable and practical tool indeed. 📚 Let's explore this fascinating endeavor.

Understanding the Scope of GPT Models: GPT models are adept at processing and generating text. However, they don't inherently process non-text data like PDF files directly.
External Plugin Concept: A PDF summarizer plugin would typically involve extracting text from the PDF and then processing this text for summarization. This task may require two components:
- PDF Text Extraction: Tools like Adobe PDF Library or PyPDF2 in Python can extract text from PDFs.
- Text Summarization: Once the text is extracted, the GPT model can summarize it.
Integration Approach: The integration involves a workflow where the chatbot first utilizes the PDF text extraction tool to convert PDF content into plain text. This text is then fed into the GPT model for summarization.
Technical Requirements:
- A programming environment (like Python) that supports both the PDF text extraction library and the GPT model.
- Adequate processing power and memory, as text extraction and AI processing can be resource-intensive.
Potential Challenges:
- PDF Complexity: Some PDFs (like those with a lot of images or complex layouts) might not have their text extracted cleanly.
- Integration Complexity: Ensuring smooth data flow between the PDF extraction tool and the GPT model.
- Response Time: The entire process (extraction + summarization) might take some time, affecting user experience.
Possible Solutions:
- Implement robust error handling and PDF processing techniques.
- Optimize the summarization process for efficiency.
- Set user expectations regarding response times.

Would you like to delve deeper into any specific area, such as the technical implementation details or the choice of tools for text extraction and summarization? 🧠💻

Human: <div class="">what specific tools can I use when creating a custom chat gpt that will extract and summarize</div>

ChatGPT:

For creating a custom chatbot with GPT that can extract and summarize text from PDFs, you'll need a combination of tools for PDF extraction, text processing, and AI-powered summarization. Let's break down the toolset:

PDF Text Extraction Tools:
- PyPDF2 or PyMuPDF (Fitz): These are Python libraries capable of extracting text from PDF files. PyPDF2 is simple and easy to use, ideal for basic text extraction. PyMuPDF is more powerful, offering advanced features like handling different font types and extracting images.
- Apache PDFBox: A Java-based tool, useful if you prefer working in a Java environment. It provides detailed control over PDF processing.
AI-Powered Summarization (Using GPT or Similar Models):
- OpenAI’s GPT Models: You can use models like GPT-3 or GPT-4 for summarization tasks. These models are accessible via API, making them easy to integrate into your chatbot.
- Hugging Face’s Transformers Library: This offers various pre-trained models including versions of GPT and BERT. It's highly customizable if you have specific requirements or wish to fine-tune models.
Programming Environment:
- Python: Widely used for AI and data processing tasks. Python’s rich library ecosystem (like NLTK for natural language processing, Flask or FastAPI for web services) makes it a top choice.
- Java: If you choose tools like Apache PDFBox, Java might be your go-to. Java is robust for enterprise-level applications.
Integration Approach:
- The chatbot receives a PDF file from the user.
- The PDF extraction tool extracts the text from the PDF.
- The extracted text is passed to the GPT model for summarization.
- The summarized text is then sent back to the user through the chatbot interface.
Considerations:
- API Rate Limits and Costs: If using cloud-based services like OpenAI's API, consider the rate limits and costs.
- Data Privacy and Security: Ensure safe handling of user data, especially when processing documents.

Would you like more detailed information on any of these tools or guidance on how to orchestrate them together in your chatbot architecture? 🛠️💡

Human: <div class="">i want detailed instructions on how to incorporate tgese</div>

ChatGPT:

Certainly! Let's dive into a step-by-step guide to incorporate PDF text extraction and summarization using GPT into your custom chatbot. We'll use Python as the programming language, PyPDF2 for PDF text extraction, and OpenAI's GPT model for summarization.

Step-by-Step Guide

1. Environment Setup

Install Python: Ensure you have Python installed on your system.
Install Necessary Libraries: You'll need PyPDF2 for PDF extraction and openai for accessing the GPT model. Install them using pip:
```
python
pip install PyPDF2 openai
```

2. Extract Text from PDF

Write a Function to Extract Text:

python
import PyPDF2

def extract_text_from_pdf(pdf_path):
    with open(pdf_path, 'rb') as file:
        reader = PyPDF2.PdfFileReader(file)
        text = ''
        for page in range(reader.numPages):
            text += reader.getPage(page).extractText()
        return text

3. Summarize Text Using GPT

Set Up OpenAI API:
- Get an API key from OpenAI.
- Set up the OpenAI API key in your environment or your script.

Write a Function to Summarize Text:

python
import openai

def summarize_text(text):
    openai.api_key = 'your-api-key'

    response = openai.Completion.create(
      engine="text-davinci-003",  # or another model like gpt-3.5-turbo
      prompt="Summarize the following text:\n\n" + text,
      max_tokens=150  # Adjust based on how long you want the summary to be
    )
    return response.choices[0].text.strip()

4. Integrate with Chatbot

Assuming you have a basic chatbot setup, integrate these functions where the bot receives a PDF file. Here's a simplified conceptual flow:

python
def handle_pdf_input(pdf_path):
    extracted_text = extract_text_from_pdf(pdf_path)
    summary = summarize_text(extracted_text)
    return summary

5. Handling User Interaction

User sends a PDF (through your chatbot's interface).
Your chatbot saves the PDF and calls handle_pdf_input with the path to the saved PDF.
The chatbot then returns the summary back to the user.

Considerations

Testing: Thoroughly test with various PDFs to ensure reliable text extraction.
Error Handling: Implement robust error handling for cases like unreadable PDFs.
API Limits and Costs: Keep an eye on the API usage to manage costs and stay within rate limits.
Asynchronous Processing: If the process takes time, consider making these operations asynchronous to improve user experience.

Next Steps

Would you like guidance on any specific part of this setup, such as setting up a basic chatbot framework, handling file inputs, or ensuring efficient operation? 🛠️📚

Human: <div class="">i want to create a custom GPT integrating these tools. please give me stop by step instructions</div>

ChatGPT:

To create a custom GPT-based chatbot that integrates PDF text extraction and summarization, you'll need a combination of programming skills, access to AI models, and understanding of web services. Here's a detailed guide to help you through this process:

Step 1: Environment Setup

Install Python: Ensure Python is installed on your system.
Install Libraries: You'll need Flask for creating the web service, PyPDF2 for PDF handling, and openai for GPT access. Install these using pip:
```
bash
pip install Flask PyPDF2 openai
```

Step 2: Obtain OpenAI API Key

Register with OpenAI and obtain an API key for accessing GPT models.

Step 3: Create a Flask Web Service

Initialize a Flask App:

python
from flask import Flask, request, jsonify
app = Flask(__name__)

Step 4: Integrate PDF Extraction

PDF Extraction Function:

python
import PyPDF2

def extract_text_from_pdf(pdf_file):
    with open(pdf_file, 'rb') as file:
        reader = PyPDF2.PdfFileReader(file)
        text = ''
        for page in range(reader.numPages):
            text += reader.getPage(page).extractText()
        return text

Step 5: Integrate GPT for Summarization

Summarization Function:

python
import openai

def summarize_text(text):
    openai.api_key = 'your-api-key'

    response = openai.Completion.create(
      engine="text-davinci-003",  # or another model
      prompt="Summarize the following text:\n\n" + text,
      max_tokens=150
    )
    return response.choices[0].text.strip()

Step 6: Create a Chatbot Endpoint

Chatbot API Endpoint:

python
@app.route('/chatbot', methods=['POST'])
def chatbot():
    file = request.files['pdf']
    file.save("temp.pdf")
    extracted_text = extract_text_from_pdf("temp.pdf")
    summary = summarize_text(extracted_text)
    return jsonify({"summary": summary})

Step 7: Running the Web Service

Run the Flask App:

python
if __name__ == '__main__':
    app.run(debug=True)

Step 8: Test the Chatbot

Use tools like Postman or a simple frontend to send a PDF to your chatbot's endpoint and see the response.

Additional Considerations

Error Handling: Add error handling for better reliability.
Asynchronous Operations: Consider making the processing asynchronous for handling larger files or longer processing times.
Security: Ensure secure handling of files and data.
UI/UX: If you plan to deploy this, think about the user interface and experience.

Final Step: Deployment

Once tested and finalized, deploy your chatbot on a server or cloud service for wider accessibility.

Would you like more detailed information on any of these steps, such as setting up Flask, handling file uploads, or deploying the web service? 🚀📚

Human: <div class="">yes, I want more instructions on how to do all of this within the chat gpt interface of creating a custom gpt</div>

ChatGPT:

Creating a custom GPT-based chatbot with PDF summarization capabilities within an existing chat interface, such as the one provided by OpenAI, involves several layers of development. This includes setting up a server-side application to handle PDF processing and GPT interactions, and then integrating this with the chat interface. Here's a more detailed breakdown:

Setting Up the Server-Side Application

Server-Side Language and Framework:
- Python with Flask is a good choice due to its simplicity and robustness.
- Install Flask: pip install Flask.
Implementing PDF Extraction:
- Use PyPDF2 for extracting text from PDFs.
- Install PyPDF2: pip install PyPDF2.
Integrating GPT for Summarization:
- Use OpenAI’s Python library.
- Install OpenAI library: pip install openai.
Creating a Flask App:
- Initialize a basic Flask application to handle requests.
Handling PDF Uploads and Responses:
- Create endpoints in Flask to receive PDF files.
- Process these files and send the extracted text to GPT for summarization.
- Return the summarized text as a response.

Integrating with Chat GPT Interface

Front-End Interaction:
- Your chat interface (front end) should allow users to upload PDF files.
- This can be a simple form submission or a more interactive chatbot interface.
Connecting Front-End to Server:
- Once the user uploads a PDF, the front end should send this file to your Flask server.
- You can use JavaScript (Ajax) to handle this file transfer asynchronously.
Receiving Summarized Text:
- The Flask app processes the PDF and returns the summary.
- Your front end should receive this summary and display it to the user in the chat interface.
Error Handling and User Feedback:
- Ensure that the user is informed of the progress (e.g., file uploading, processing).
- Implement error handling for scenarios like upload failures or processing errors.

Example: Flask Endpoint for PDF Summarization

python
@app.route('/upload_pdf', methods=['POST'])
def upload_pdf():
    file = request.files['pdf']
    file_path = "path/to/save/" + file.filename
    file.save(file_path)

    extracted_text = extract_text_from_pdf(file_path)
    summary = summarize_text(extracted_text)

    return jsonify({"summary": summary})

Integrating with an Existing Chatbot

If you're integrating this into an existing chatbot platform that uses GPT (like OpenAI's GPT-3), you'll need to:

Understand the platform's API and capabilities: How it handles custom requests and responses.
Custom Command for PDF Uploads: Implement a custom command or trigger within the chat interface that initiates the PDF upload process.
API Calls: Your chatbot should make API calls to your Flask server for PDF processing.

Testing and Deployment

Local Testing: Test the entire flow locally. Ensure that the PDF upload, processing, and summary display in the chat interface work seamlessly.
Deployment: Deploy your Flask app on a cloud platform (like AWS, Heroku, etc.) and update your chat interface to interact with this deployed service.

Would you like a deeper dive into any specific part of this process, such as front-end implementation, Flask app deployment, or how to handle communication between the chat interface and the server? 🖥️🌐🔍

Human: <div class="">on step 1 I get an invalid syntax error</div>

ChatGPT:

An "invalid syntax" error in Python usually indicates a problem with the structure of your code. To assist you effectively, I need to see the exact code where the error occurs. Could you please share the snippet of your Python code that's causing the "invalid syntax" error? This will help me pinpoint the issue and provide a more accurate solution. 🐍🔍

Human: <div class=""> File "<stdin>", line 1 pip install Flask PyPDF2 openai ^^^^^^^ SyntaxError: invalid syntax >>> pip install Flask PyPDF2 openai File "<stdin>", line 1 pip install Flask PyPDF2 openai</div>

ChatGPT:

Ah, I see the issue. The error you're encountering is due to attempting to run the pip install command directly in the Python interpreter. The pip install command is meant to be run in your system's command line interface (CLI), not within the Python interpreter itself.

Here's how you can correctly install these packages:

Exit the Python Interpreter:
- If you're currently in the Python interpreter (indicated by the >>> prompt), you can exit it by typing exit() and pressing Enter.
Open Your Command Line Interface:
- On Windows, this is typically Command Prompt or PowerShell.
- On macOS or Linux, this is typically the Terminal.
Run the pip Install Command:
- In your command line interface, type the following command and press Enter:
```
bash
pip install Flask PyPDF2 openai
```
- This command will install Flask, PyPDF2, and the OpenAI Python library on your system.

By following these steps, you should be able to install the necessary packages without encountering a syntax error. If you have any further questions or encounter more issues, feel free to ask! 🛠️💻

Human: <div class="">Now I get this error C:\Users\jrasansky>pip install Flask PyPDF2 openai 'pip' is not recognized as an internal or external command, operable program or batch file.</div>

ChatGPT:

The error message 'pip' is not recognized as an internal or external command, operable program or batch file suggests that Python and its associated tools, like pip, are not properly added to your system's PATH environment variable. This is a common issue and can be resolved with a few steps:

Solution 1: Check if Python and pip are Installed

Verify Python Installation:
- Open your command line interface and type python --version (or python3 --version) and press Enter.
- If Python is installed and properly set up, it should display the version number.
- If you get an error, Python might not be installed.
Install Python (if not installed):
- Download and install Python from the official website.
- During installation, ensure you check the option Add Python to PATH.

Solution 2: Add Python to PATH Manually

If Python is installed but not recognized, you may need to add it to your PATH manually:

Locate Python Installation:
- Find where Python is installed on your computer. Common paths are something like C:\Python39\ or C:\Users\[Your User Name]\AppData\Local\Programs\Python\Python39\.
Add Python to PATH:
- Right-click on 'This PC' or 'My Computer' and select 'Properties'.
- Click on 'Advanced system settings' and then 'Environment Variables'.
- In the 'System variables' section, find and select the 'Path' variable, then click 'Edit'.
- Click 'New' and add the path where Python is installed. Also, add the Scripts folder path (e.g., C:\Python39\Scripts).
- Click 'OK' to save your changes.
Restart Command Line Interface:
- Close and reopen your command line interface for the changes to take effect.
Try Installing Again:
- Now try running the pip install Flask PyPDF2 openai command again in the command line interface.

This should resolve the issue and allow you to install the necessary packages using pip. If you encounter further issues or have any questions, feel free to ask! 🛠️💻🌐

Human: <div class="">Success, Now start me over from the beginning</div>

ChatGPT:

Fantastic! Now that you have successfully set up Python and installed the necessary packages, let's begin from scratch to build your custom GPT-based chatbot with PDF extraction and summarization capabilities. Here's a step-by-step guide:

Step 1: Setting Up Your Project

Create a New Directory for your project and navigate into it in your command line interface.
Initialize a Python File (e.g., app.py) where you'll write your Flask application code.

Step 2: Create a Flask Web Application

Open app.py in a text editor or an IDE (like Visual Studio Code).

Write Basic Flask Code:

python
from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/')
def hello_world():
    return 'Hello, World!'

if __name__ == '__main__':
    app.run(debug=True)

Run the Flask App:
- In the command line, run python app.py.
- You should see a message indicating that the server is running. You can visit http://127.0.0.1:5000/ in your browser to see the 'Hello, World!' message.

Step 3: Integrate PDF Text Extraction

Install PyPDF2 (if not already installed): Run pip install PyPDF2.

Add PDF Extraction Function to app.py:

python
import PyPDF2

def extract_text_from_pdf(pdf_file):
    with open(pdf_file, 'rb') as file:
        reader = PyPDF2.PdfFileReader(file)
        text = ''
        for page in range(reader.numPages):
            text += reader.getPage(page).extractText()
        return text

Step 4: Integrate GPT for Summarization

Install OpenAI Library (if not already installed): Run pip install openai.

Add GPT Summarization Function to app.py:

python
import openai

def summarize_text(text):
    openai.api_key = 'your-api-key'  # Replace with your actual OpenAI API key

    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt="Summarize the following text:\n\n" + text,
        max_tokens=150
    )
    return response.choices[0].text.strip()

Step 5: Create a Route to Handle PDF Upload and Summarization

Add a New Route in app.py:

python
@app.route('/upload_pdf', methods=['POST'])
def upload_pdf():
    if 'pdf' not in request.files:
        return 'No file part', 400
    file = request.files['pdf']
    file_path = "temp.pdf"  # You can choose to save it with a dynamic name
    file.save(file_path)

    extracted_text = extract_text_from_pdf(file_path)
    summary = summarize_text(extracted_text)

    return jsonify({"summary": summary})

Run Your Flask App again to see the changes.

Step 6: Test Your Application

Use a Tool like Postman to test the /upload_pdf route. Send a POST request with a PDF file and see if you get the summary back.
Debug and Refine as necessary. Check for errors and optimize your code.

Step 7: Deploy Your Application (Optional)

Once you are satisfied with your local testing, you can deploy your Flask application to a cloud service like AWS, Heroku, or Google Cloud for broader accessibility.

Would you like more information on any of these steps, such as testing with Postman, handling errors, or deploying the app? 🌐💻🛠️

Human: <div class="">give me step by step instructions for step 1. Pretend i am an 8th grader</div>

ChatGPT:

Absolutely, let's break down Step 1 into simple, easy-to-follow instructions. We're going to set up your project, which is like preparing your workspace before starting a fun science project. 🚀

Step 1: Setting Up Your Project

1.1. Create a New Project Folder

Find a Place on Your Computer: Choose where you want to keep your project. This could be on your Desktop or in your Documents folder.
Make a New Folder: Right-click in the space, select New, then Folder. Name this new folder something like MyChatbotProject.

1.2. Open Command Line Interface

On Windows: Press the Windows key, type cmd, and press Enter. This opens the Command Prompt.
On Mac: Open Finder, go to Applications, then Utilities, and double-click on Terminal.

1.3. Navigate to Your Project Folder in Command Line

Use the cd Command: This stands for "change directory". You'll tell the computer to go to the folder you just made.
How to Do It:
- If your folder is on the Desktop, type cd Desktop/MyChatbotProject then press Enter.
- If it's in Documents, type cd Documents/MyChatbotProject then press Enter.
- Replace Desktop or Documents with the correct path if your folder is somewhere else.

1.4. Create a Python File for Your Project

Open a Text Editor: This can be Notepad (Windows), TextEdit (Mac), or any code editor you like (like Visual Studio Code).
Create a New File: In the editor, start a new file.
Save This File in Your Project Folder: Save it as app.py in MyChatbotProject. The .py part tells the computer it's a Python file.

1.5. Check Python Installation

Go Back to Command Line: You should still be in the MyChatbotProject folder.
Type python --version and Press Enter: This checks if Python is installed and what version it is.
- If it shows a version number (like Python 3.8.5), great! Python is installed.
- If it says something like “Python is not recognized”, you'll need to install Python.

🌟 Congratulations! 🌟

You've set up your project space! It's like having a clean, organized desk before starting a craft project. You have a folder for all your work, and a Python file where you'll write your code.

Next, we'll start adding some cool code to turn this into a chatbot. But before that, do you have any questions about these steps, or do you need help with anything here? 🤔💡

Human: <div class="">let's go to next steps</div>

ChatGPT:

Great! Now that you have your project folder and Python file ready, let's move on to the next steps where we start coding your chatbot application using Flask, a Python web framework.

Step 2: Create a Flask Web Application

2.1. Install Flask

Go to Your Command Line Interface where you navigated to MyChatbotProject.
Install Flask: Type pip install Flask and press Enter. This command tells your computer to download and install Flask.

2.2. Write Your First Flask Code

Open app.py in Your Text Editor: This is the file you created in your project folder.
Write the Flask Code: Copy the following code and paste it into app.py. This code creates a basic web application.
```
python
from flask import Flask
app = Flask(__name__)

@app.route('/')
def hello_world():
    return 'Hello, World!'

if __name__ == '__main__':
    app.run(debug=True)
```
Here’s what this code does:
- from flask import Flask: This line gets the Flask tool you just installed.
- app = Flask(__name__): This creates your web application.
- @app.route('/'): This is like telling your app what to do when someone visits the home page.
- def hello_world(): When someone visits the home page, they will see 'Hello, World!'.
- app.run(debug=True): This starts your app.

2.3. Run the Flask App

Back in the Command Line, make sure you are still in your MyChatbotProject folder.
Start the Flask App: Type python app.py and press Enter. This tells your computer to run your Flask app.
Check if It Works:
- You should see some text in the command line saying the server is running.
- Open a web browser and go to http://127.0.0.1:5000/. You should see 'Hello, World!' on the page.

🌟 Well Done! 🌟

You've just created a basic web application! When you visit http://127.0.0.1:5000/ in your browser, your Flask app responds with 'Hello, World!'.

The next steps will involve adding the functionality for PDF text extraction and connecting with the GPT model for summarization. But before moving forward, do you have any questions or need clarification on anything we've done so far? 🤓💻🚀

Human: <div class="">is this correctC:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot>python app.py C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot></div>

ChatGPT:

It looks like you've correctly navigated to your project folder and attempted to run your app.py Flask application. However, from the output you've shown, it seems that the Flask application didn't start as expected. Let's troubleshoot this:

Troubleshooting Steps

Check for Errors in Command Line:
- After typing python app.py, look closely at the command line output.
- If there are any error messages, they can give clues about what went wrong.
Verify the app.py File:
- Ensure that app.py is saved in the My Expert Report Chatbot folder.
- Double-check the contents of app.py to make sure it matches the Flask code provided earlier.
Check Python Installation:
- In the command line, type python --version and press Enter. This will confirm that Python is installed and recognized.
- If Python isn't recognized, you might need to add Python to your system's PATH or reinstall Python.
Ensure Flask is Installed:
- In the command line, type pip show Flask and press Enter. This checks if Flask is installed.
- If Flask isn't installed, run pip install Flask again.
Run the Application Again:
- After verifying the above steps, try running python app.py again in the command line.

Expected Behavior

When you run python app.py, you should see output indicating that the Flask server has started, usually with lines like:

csharp
 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)

Then, when you visit http://127.0.0.1:5000/ in a web browser, you should see the 'Hello, World!' message.

Next Steps

If you're still encountering issues, could you provide any error messages that appear when you run python app.py? This will help in diagnosing the problem more accurately. 🛠️💡

Human: <div class="">Done. what are next steps</div>

ChatGPT:

Great! Now that your basic Flask application is running, let's move on to integrating the PDF text extraction and GPT-based summarization functionalities.

Step 3: Integrate PDF Text Extraction

Install PyPDF2: Open your command line interface and install PyPDF2 by running:
```
pip install PyPDF2
```
This library will help you extract text from PDF files.

Add PDF Extraction Function: Modify your app.py file to include a function for extracting text from PDF files. Here's a basic example:

python
import PyPDF2

def extract_text_from_pdf(pdf_file):
    with open(pdf_file, 'rb') as file:
        reader = PyPDF2.PdfFileReader(file)
        text = ''
        for page in range(reader.numPages):
            text += reader.getPage(page).extractText()
        return text

Step 4: Integrate GPT for Summarization

Install OpenAI Library: In your command line, install the OpenAI library using pip:
```
pip install openai
```
Obtain an API Key from OpenAI: You'll need to sign up with OpenAI and obtain an API key to use their GPT models.

Add GPT Summarization Function: Update your app.py file with a function to summarize text using GPT. Make sure to replace 'your-api-key' with your actual OpenAI API key.

python
import openai

def summarize_text(text):
    openai.api_key = 'your-api-key'

    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt="Summarize the following text:\n\n" + text,
        max_tokens=150
    )
    return response.choices[0].text.strip()

Step 5: Create an Endpoint for PDF Summarization

Modify app.py for PDF Upload and Summarization: Add a route in your Flask app to handle PDF file uploads and return the summary. Here's an example:

python
from flask import request, jsonify

@app.route('/upload_pdf', methods=['POST'])
def upload_pdf():
    if 'pdf' not in request.files:
        return 'No file part', 400
    file = request.files['pdf']
    file_path = "temp.pdf"
    file.save(file_path)

    extracted_text = extract_text_from_pdf(file_path)
    summary = summarize_text(extracted_text)

    return jsonify({"summary": summary})

Step 6: Run and Test Your Application

Start Your Flask App: Run python app.py again in your command line.
Test the PDF Summarization: Use a tool like Postman to send a POST request to http://127.0.0.1:5000/upload_pdf with a PDF file. Check if you receive a summary in response.

🌟 Congratulations! 🌟

You've just integrated PDF text extraction and GPT-based summarization into your Flask web application. This is a significant step in creating your custom chatbot.

Next Steps

Error Handling: Add error handling for better reliability in your application.
Front-End Interface: If desired, create a simple web interface where users can upload PDF files and see the summaries.
Deployment: Consider deploying your application to a cloud platform for broader access.

Do you have any questions about these steps, or is there a specific part you'd like to delve deeper into? 🤖📚💻

Human: <div class="">before we start step 3 i got this message in command center. Is this correct</div>

ChatGPT:

To confirm if the message you saw in the command center (command line interface) is correct, I would need to know what the message was.

When you run a Flask application using python app.py, typically, the command center displays messages related to the startup of the Flask server. The expected output usually includes:

Server Running Information: Messages indicating that the Flask server has started and is running. It often looks like this:
```
csharp
* Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
```
This means your Flask app is running correctly, and you can access it by going to http://127.0.0.1:5000/ in your web browser.
Debug Mode Information: If you set debug=True in your Flask app, you might see a message about the debugger being active. This is normal for a development environment.
Warnings or Errors: Occasionally, there might be warnings (which are usually okay to proceed with during development) or errors (which need to be resolved).

Could you please share the specific message you saw? This will help me determine if everything is set up correctly or if there are any issues that need to be addressed before moving on to step 3. 🧐💻🔍

Human: <div class="">before we start step 3, i got this message. Is this correct? C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot>python app.py * Serving Flask app 'app' * Debug mode: on WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. * Running on http://127.0.0.1:5000 Press CTRL+C to quit * Restarting with stat * Debugger is active! * Debugger PIN: 110-308-306 127.0.0.1 - - [23/Jan/2024 17:06:41] "GET / HTTP/1.1" 200 - 127.0.0.1 - - [23/Jan/2024 17:06:41] "GET /favicon.ico HTTP/1.1" 404 -</div>

ChatGPT:

Yes, this message indicates that your Flask application is set up correctly and running as expected. Let's break down what each part of the message means:

Serving Flask App 'app': This confirms that your Flask application named 'app' is being served.
Debug Mode: on: You're running Flask in debug mode, which is great for development because it provides useful debug information and reloads the server automatically when code changes are detected.
Development Server Warning:
- The warning WARNING: This is a development server. Do not use it in a production deployment... is standard. It reminds you that Flask’s built-in server is intended for development, not for handling live traffic on the internet.
- In a production environment, you should use a more robust server setup like Gunicorn or uWSGI.
Running on http://127.0.0.1:5000: This is the local address where your Flask app is accessible. You can visit this URL in your web browser to interact with your application.
Debugger Information:
- Debugger is active! and the Debugger PIN indicate that the debugger is running. This is useful for troubleshooting if your app encounters issues.
- The Debugger PIN is used for securely accessing the interactive debugger in the browser.
Access Log Entries:
- The lines starting with 127.0.0.1 - - are access log entries, showing the requests made to your server (like visiting the home page).
- GET / HTTP/1.1 200 indicates a successful request to the home page (/).
- GET /favicon.ico HTTP/1.1 404 shows a request for a favicon (the small icon in a browser tab) that wasn't found, resulting in a 404 (Not Found) error. This is normal if you haven't set up a favicon.

Conclusion

Everything looks good, and your Flask application is operational. You can now proceed to Step 3 (integrating PDF text extraction and GPT-based summarization) with confidence.

Would you like to start with Step 3 now? 🚀💻📚

Human: <div class="">yes, start with step 3 but do I need get back to my chat gpt project line. If so how do I do that?</div>

ChatGPT:

For Step 3, we'll integrate PDF text extraction into your Flask application. But first, let's ensure you're in the correct directory in your command line interface to work on your chatbot project.

Returning to Your Chatbot Project Directory

Open Command Line Interface:
- On Windows, search for Command Prompt and open it.
- On Mac, open Terminal.
Navigate to Your Project Folder:
- Use the cd (change directory) command to go to your project folder.
- If your project folder, My Expert Report Chatbot, is on the Desktop, the command will look like this:
```
bash
cd Desktop\My Expert Report Chatbot
```
- Adjust the command if your folder is located somewhere else. Remember to replace any forward slashes (/) with backslashes (\) if you're using Windows.

Once you're in the correct directory, you can proceed with integrating PDF text extraction into your Flask application.

Step 3: Integrate PDF Text Extraction

Install PyPDF2 (if you haven't already):
- In your command line, type pip install PyPDF2 and press Enter. This installs the PyPDF2 library, which is used for extracting text from PDF files.

Update Your Flask Application (app.py):

Add a function to extract text from a PDF file. Here's a simple example you can use:

python
import PyPDF2

def extract_text_from_pdf(pdf_file):
    with open(pdf_file, 'rb') as file:
        reader = PyPDF2.PdfFileReader(file)
        text = ''
        for page in range(reader.numPages):
            text += reader.getPage(page).extractText()
        return text

This function opens a PDF file, reads each page, and concatenates the text into a single string.

Save Changes to app.py:
- After adding the function, save your app.py file.

Now you have a function in your Flask application that can extract text from a PDF file. Next, we'll integrate GPT for text summarization.

Before we proceed, do you have any questions about these steps, or is everything clear so far? 🤖📘💻

Human: <div class="">My app.py has the following text. Is this correct. yes or No only. from flask import Flask app = Flask(__name__) @app.route('/') def hello_world(): return 'Hello, World!' if __name__ == '__main__': app.run(debug=True) import PyPDF2 def extract_text_from_pdf(pdf_file): with open(pdf_file, 'rb') as file: reader = PyPDF2.PdfFileReader(file) text = '' for page in range(reader.numPages): text += reader.getPage(page).extractText() return text</div>

ChatGPT:

No.

Human: <div class="">what is wrong with this?</div>

ChatGPT:

The code you've provided is almost correct, but the placement of the import statement for PyPDF2 and the function extract_text_from_pdf needs adjustment. In Python, it's a good practice to place all import statements at the beginning of the file. This helps in understanding the dependencies of the script at a glance and avoids any unexpected errors.

Here's how you should rearrange it:

python
from flask import Flask
import PyPDF2

app = Flask(__name__)

def extract_text_from_pdf(pdf_file):
    with open(pdf_file, 'rb') as file:
        reader = PyPDF2.PdfFileReader(file)
        text = ''
        for page in range(reader.numPages):
            text += reader.getPage(page).extractText()
        return text

@app.route('/')
def hello_world():
    return 'Hello, World!'

if __name__ == '__main__':
    app.run(debug=True)

With this structure, the import PyPDF2 statement is at the top, followed by the definition of your Flask app and the function extract_text_from_pdf. This organization follows Python best practices and ensures that all necessary modules are loaded before they are used in your functions.

Human: <div class="">done</div>

ChatGPT:

Great! Now that your Flask application is set up with the PDF text extraction function, the next step is to integrate GPT for text summarization.

Step 4: Integrate GPT for Summarization

Install the OpenAI Library (if not already installed):
- Run pip install openai in your command line.
Obtain an API Key from OpenAI:
- Sign up on the OpenAI website and obtain an API key.
Update Your app.py File:
- Add the OpenAI import at the top of your file.
- Write a function to use GPT for summarizing text.
- Ensure you replace 'your-api-key' with your actual OpenAI API key.

Here's how to modify your app.py:

python
from flask import Flask, request, jsonify
import PyPDF2
import openai

app = Flask(__name__)

def extract_text_from_pdf(pdf_file):
    with open(pdf_file, 'rb') as file:
        reader = PyPDF2.PdfFileReader(file)
        text = ''
        for page in range(reader.numPages):
            text += reader.getPage(page).extractText()
        return text

def summarize_text(text):
    openai.api_key = 'your-api-key'
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt="Summarize the following text:\n\n" + text,
        max_tokens=150
    )
    return response.choices[0].text.strip()

@app.route('/')
def hello_world():
    return 'Hello, World!'

# Add more routes and functions as needed

if __name__ == '__main__':
    app.run(debug=True)

Step 5: Create a Route for PDF Upload and Summarization

Add a route in your Flask app to handle PDF uploads, extract text, and return the summary.

This is the part where you'll need to write some more code to connect everything together. Do you want to continue with this step, or do you have any questions so far? 🤖💡📄

Human: <div class="">you betcha</div>

ChatGPT:

Fantastic! Let's proceed with creating the route in your Flask app that will handle the PDF upload, extract the text from the PDF, and then use GPT to summarize that text.

Step 5: Create a Route for PDF Upload and Summarization

Modify Your app.py File: You will add a new route that handles file uploads and integrates the functions you've created.
Add Flask's request and jsonify Modules: These modules are used for handling HTTP requests and sending JSON responses. Make sure they are imported at the top of your app.py file:
```
python
from flask import Flask, request, jsonify
```

Create the File Upload Route: Below your existing functions, add a new route to your Flask app:

python
@app.route('/upload_pdf', methods=['POST'])
def upload_pdf():
    # Check if the post request has the file part
    if 'pdf' not in request.files:
        return jsonify({'error': 'No PDF file part in the request'}), 400
    file = request.files['pdf']

    # If the user does not select a file, the browser submits an
    # empty file without a filename.
    if file.filename == '':
        return jsonify({'error': 'No selected file'}), 400

    if file:
        file_path = "uploaded.pdf"
        file.save(file_path)
        extracted_text = extract_text_from_pdf(file_path)
        summary = summarize_text(extracted_text)
        return jsonify({"summary": summary})

if __name__ == '__main__':
    app.run(debug=True)

In this route:

We first check if a file is part of the request and return an error message if not.
We then save the uploaded file, extract text from it, summarize the text, and return the summary as JSON.

Running and Testing the Application

Run Your Flask App: In your command line, execute python app.py to start the Flask server.
Test the PDF Summarization:
- You can use tools like Postman to send a POST request to http://127.0.0.1:5000/upload_pdf.
- In Postman, select 'POST', enter the URL, go to 'Body', select 'form-data', and upload a PDF file with the key as 'pdf'.
Check the Response: If everything is set up correctly, you should receive a JSON response with the summarized text.

🎉 Congratulations! 🎉

You now have a Flask web application that can accept PDF file uploads and return a GPT-generated summary of the text in those files.

Feel free to test it with different PDFs to see how it performs. If you run into any issues or have questions, I'm here to help! Would you like to proceed with any further steps or clarifications? 🚀💻📚

Human: <div class="">In step 5, no. 2 where do i add this text</div>

ChatGPT:

In Step 5, number 2, the import statement for Flask's request and jsonify modules should be added at the top of your app.py file, along with your other import statements. This is to ensure that these modules are available for use throughout your entire Flask application.

Your app.py file should start with these import statements:

python
from flask import Flask, request, jsonify
import PyPDF2
import openai

After these import statements, you can then continue with the rest of your code, defining your Flask app, the function to extract text from PDF, the function to summarize text using GPT, and finally the route for handling the PDF upload and summarization.

By placing the import statements at the top, you're organizing your code neatly and making sure all necessary functionalities are loaded before they're used in your application.

Human: <div class="">Is this correct? If not tell me what my app. py should say. from flask import Flask, request, jsonify import PyPDF2 import openai @app.route('/upload_pdf', methods=['POST']) def upload_pdf(): # Check if the post request has the file part if 'pdf' not in request.files: return jsonify({'error': 'No PDF file part in the request'}), 400 file = request.files['pdf'] # If the user does not select a file, the browser submits an # empty file without a filename. if file.filename == '': return jsonify({'error': 'No selected file'}), 400 if file: file_path = "uploaded.pdf" file.save(file_path) extracted_text = extract_text_from_pdf(file_path) summary = summarize_text(extracted_text) return jsonify({"summary": summary}) if __name__ == '__main__': app.run(debug=True) app = Flask(__name__) def extract_text_from_pdf(pdf_file): with open(pdf_file, 'rb') as file: reader = PyPDF2.PdfFileReader(file) text = '' for page in range(reader.numPages): text += reader.getPage(page).extractText() return text def summarize_text(text): openai.api_key = 'your-api-key' response = openai.Completion.create( engine="text-davinci-003", prompt="Summarize the following text:\n\n" + text, max_tokens=150 ) return response.choices[0].text.strip() @app.route('/') def hello_world(): return 'Hello, World!' # Add more routes and functions as needed if __name__ == '__main__': app.run(debug=True) </div>

ChatGPT:

The structure of your app.py file needs some adjustments. The Flask app instance (app = Flask(__name__)) should be defined before any route decorators (@app.route(...)). Also, each function and route should be defined only once. Let's rearrange and correct your code:

python
from flask import Flask, request, jsonify
import PyPDF2
import openai

app = Flask(__name__)

def extract_text_from_pdf(pdf_file):
    with open(pdf_file, 'rb') as file:
        reader = PyPDF2.PdfFileReader(file)
        text = ''
        for page in range(reader.numPages):
            text += reader.getPage(page).extractText()
        return text

def summarize_text(text):
    openai.api_key = 'your-api-key'  # Replace with your actual OpenAI API key
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt="Summarize the following text:\n\n" + text,
        max_tokens=150
    )
    return response.choices[0].text.strip()

@app.route('/')
def hello_world():
    return 'Hello, World!'

@app.route('/upload_pdf', methods=['POST'])
def upload_pdf():
    # Check if the post request has the file part
    if 'pdf' not in request.files:
        return jsonify({'error': 'No PDF file part in the request'}), 400
    file = request.files['pdf']

    # If the user does not select a file, the browser submits an
    # empty file without a filename.
    if file.filename == '':
        return jsonify({'error': 'No selected file'}), 400

    if file:
        file_path = "uploaded.pdf"
        file.save(file_path)
        extracted_text = extract_text_from_pdf(file_path)
        summary = summarize_text(extracted_text)
        return jsonify({"summary": summary})

if __name__ == '__main__':
    app.run(debug=True)

In this corrected version:

app = Flask(__name__) is defined at the top after the import statements.
The functions extract_text_from_pdf and summarize_text are defined next.
The route handlers @app.route('/') and @app.route('/upload_pdf', methods=['POST']) come after the function definitions.
The if __name__ == '__main__': block is at the end, and it's only needed once.

This structure ensures that your Flask app is set up correctly and all functions and routes are defined in the proper order. Make sure to replace 'your-api-key' with your actual OpenAI API key in the summarize_text function.

Human: <div class="">how do i test the pdf summarization with postman</div>

ChatGPT:

Testing your PDF summarization feature with Postman is a straightforward process. Postman is a popular tool for testing APIs and web services, and it allows you to send different types of requests, including file uploads. Here's how you can do it:

Step-by-Step Guide to Test PDF Summarization Using Postman

Open Postman: If you haven't already, download and install Postman from their official website. Open it once installed.
Create a New Request:
- Click on the New button or + tab to start a new request.
Set Up the Request:
- HTTP Method: Select POST from the dropdown menu. This is because your Flask route for PDF upload is set to accept POST requests.
- Request URL: Enter the URL of your Flask app’s PDF upload route. If you're running Flask locally, it will likely be http://127.0.0.1:5000/upload_pdf.
Configure the Request for File Upload:
- Go to the Body tab in Postman.
- Select form-data.
- In the key-value input, set the Key to pdf (as this is what your Flask route expects). Make sure to select File from the dropdown on the right side of the key input.
- Click on the Select Files button next to the key and choose the PDF file you want to upload and summarize.
Send the Request:
- Click the Send button to submit the request to your Flask application.
View the Response:
- After sending the request, Postman will display the response from your Flask app.
- If everything is working correctly, you should see the JSON response containing the summarized text from your PDF.
Troubleshooting:
- If you encounter any errors, check the response for clues about what went wrong.
- Make sure your Flask application is running when you send the request from Postman.
- Verify that the PDF file you're testing with contains extractable text.

Conclusion

Using Postman to test your Flask application's PDF summarization feature allows you to simulate how a user might interact with your API, making it an invaluable tool for debugging and development.

If you run into any issues or have questions about the testing process, feel free to ask for more assistance! 🚀💻🔧

Human: <div class="">in postman, where is post button. show me a screen shot</div>

ChatGPT:

To send a request in Postman, you need to follow these general steps:

Open Postman: Launch the Postman application on your computer.
Creating a Request:
- Select the HTTP method for your request. In your case, it will be POST.
- Fill in the URL of your Flask endpoint (e.g., http://127.0.0.1:5000/upload_pdf).
Configuring the Request for File Upload:
- Go to the Body tab in your Postman request.
- Select form-data.
- For the key, input pdf, and set the type to File. Then, upload the PDF file you wish to summarize.
Sending the Request:
- Click the Send button to execute the API request. This button is usually found near the top of the Postman interface, to the right of the area where you entered your URL.
View the Response:
- After sending the request, Postman will display the response received from your Flask application.
- If everything is set up correctly, you should see a JSON response containing the summary of your PDF.

The Send button in Postman is typically located in the upper part of the interface, right next to the URL input field. It's prominently displayed and easy to locate. After setting up your request, clicking this button will send your configured request to the specified URL and return the response in the lower section of the Postman window.

Human: <div class="">This is the response I got from postman after i uploaded my pdf and hit Send <!doctype html> <html lang=en> <head> <title>PyPDF2.errors.DeprecationError: PdfFileReader is deprecated and was removed in PyPDF2 3.0.0. Use PdfReader instead. // Werkzeug Debugger</title> <link rel="stylesheet" href="?__debugger__=yes&amp;cmd=resource&amp;f=style.css"> <link rel="shortcut icon" href="?__debugger__=yes&amp;cmd=resource&amp;f=console.png"> <script src="?__debugger__=yes&amp;cmd=resource&amp;f=debugger.js"></script> <script> var CONSOLE_MODE = false, EVALEX = true, EVALEX_TRUSTED = false, SECRET = "0NBL5MUgI5XKHuD0QaNY"; </script> </head> <body style="background-color: #fff"> <div class="debugger"> <h1>DeprecationError</h1> <div class="detail"> <p class="errormsg">PyPDF2.errors.DeprecationError: PdfFileReader is deprecated and was removed in PyPDF2 3.0.0. Use PdfReader instead. </p> </div> <h2 class="traceback">Traceback <em>(most recent call last)</em></h2> <div class="traceback"> <h3></h3> <ul><li><div class="frame" id="frame-2867351324848"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">1488</em>, in <code class="function">__call__</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>) -&gt; cabc.Iterable[bytes]:</pre> <pre class="line before"><span class="ws"> </span>&#34;&#34;&#34;The WSGI server calls the Flask application object as the</pre> <pre class="line before"><span class="ws"> </span>WSGI application. This calls :meth:`wsgi_app`, which can be</pre> <pre class="line before"><span class="ws"> </span>wrapped to apply middleware.</pre> <pre class="line before"><span class="ws"> </span>&#34;&#34;&#34;</pre> <pre class="line current"><span class="ws"> </span>return self.wsgi_app(environ, start_response) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre></div> </div> <li><div class="frame" id="frame-2867351326144"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">1466</em>, in <code class="function">wsgi_app</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>ctx.push()</pre> <pre class="line before"><span class="ws"> </span>response = self.full_dispatch_request()</pre> <pre class="line before"><span class="ws"> </span>except Exception as e:</pre> <pre class="line before"><span class="ws"> </span>error = e</pre> <pre class="line current"><span class="ws"> </span>response = self.handle_exception(e) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>except: # noqa: B001</pre> <pre class="line after"><span class="ws"> </span>error = sys.exc_info()[1]</pre> <pre class="line after"><span class="ws"> </span>raise</pre> <pre class="line after"><span class="ws"> </span>return response(environ, start_response)</pre> <pre class="line after"><span class="ws"> </span>finally:</pre></div> </div> <li><div class="frame" id="frame-2867351326576"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">1463</em>, in <code class="function">wsgi_app</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>ctx = self.request_context(environ)</pre> <pre class="line before"><span class="ws"> </span>error: BaseException | None = None</pre> <pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>ctx.push()</pre> <pre class="line current"><span class="ws"> </span>response = self.full_dispatch_request() <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>except Exception as e:</pre> <pre class="line after"><span class="ws"> </span>error = e</pre> <pre class="line after"><span class="ws"> </span>response = self.handle_exception(e)</pre> <pre class="line after"><span class="ws"> </span>except: # noqa: B001</pre> <pre class="line after"><span class="ws"> </span>error = sys.exc_info()[1]</pre></div> </div> <li><div class="frame" id="frame-2867351326720"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">872</em>, in <code class="function">full_dispatch_request</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>request_started.send(self, _async_wrapper=self.ensure_sync)</pre> <pre class="line before"><span class="ws"> </span>rv = self.preprocess_request()</pre> <pre class="line before"><span class="ws"> </span>if rv is None:</pre> <pre class="line before"><span class="ws"> </span>rv = self.dispatch_request()</pre> <pre class="line before"><span class="ws"> </span>except Exception as e:</pre> <pre class="line current"><span class="ws"> </span>rv = self.handle_user_exception(e) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>return self.finalize_request(rv)</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"> </span>def finalize_request(</pre> <pre class="line after"><span class="ws"> </span>self,</pre> <pre class="line after"><span class="ws"> </span>rv: ft.ResponseReturnValue | HTTPException,</pre></div> </div> <li><div class="frame" id="frame-2867351326864"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">870</em>, in <code class="function">full_dispatch_request</code></h4> <div class="source library"><pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>request_started.send(self, _async_wrapper=self.ensure_sync)</pre> <pre class="line before"><span class="ws"> </span>rv = self.preprocess_request()</pre> <pre class="line before"><span class="ws"> </span>if rv is None:</pre> <pre class="line current"><span class="ws"> </span>rv = self.dispatch_request() <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>except Exception as e:</pre> <pre class="line after"><span class="ws"> </span>rv = self.handle_user_exception(e)</pre> <pre class="line after"><span class="ws"> </span>return self.finalize_request(rv)</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"> </span>def finalize_request(</pre></div> </div> <li><div class="frame" id="frame-2867351327008"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">855</em>, in <code class="function">dispatch_request</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>and req.method == &#34;OPTIONS&#34;</pre> <pre class="line before"><span class="ws"> </span>):</pre> <pre class="line before"><span class="ws"> </span>return self.make_default_options_response()</pre> <pre class="line before"><span class="ws"> </span># otherwise dispatch to the handler for that endpoint</pre> <pre class="line before"><span class="ws"> </span>view_args: dict[str, t.Any] = req.view_args # type: ignore[assignment]</pre> <pre class="line current"><span class="ws"> </span>return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return] <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"> </span>def full_dispatch_request(self) -&gt; Response:</pre> <pre class="line after"><span class="ws"> </span>&#34;&#34;&#34;Dispatches the request and on top of that performs request</pre> <pre class="line after"><span class="ws"> </span>pre and postprocessing as well as HTTP exception catching and</pre> <pre class="line after"><span class="ws"> </span>error handling.</pre></div> </div> <li><div class="frame" id="frame-2867351327152"> <h4>File <cite class="filename">"C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py"</cite>, line <em class="line">43</em>, in <code class="function">upload_pdf</code></h4> <div class="source "><pre class="line before"><span class="ws"> </span>return jsonify({&#39;error&#39;: &#39;No selected file&#39;}), 400</pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"> </span>if file:</pre> <pre class="line before"><span class="ws"> </span>file_path = &#34;uploaded.pdf&#34;</pre> <pre class="line before"><span class="ws"> </span>file.save(file_path)</pre> <pre class="line current"><span class="ws"> </span>extracted_text = extract_text_from_pdf(file_path) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>summary = summarize_text(extracted_text)</pre> <pre class="line after"><span class="ws"> </span>return jsonify({&#34;summary&#34;: summary})</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span>if __name__ == &#39;__main__&#39;:</pre> <pre class="line after"><span class="ws"> </span>app.run(debug=True)</pre></div> </div> <li><div class="frame" id="frame-2867351327296"> <h4>File <cite class="filename">"C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py"</cite>, line <em class="line">9</em>, in <code class="function">extract_text_from_pdf</code></h4> <div class="source "><pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"></span>app = Flask(__name__)</pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"></span>def extract_text_from_pdf(pdf_file):</pre> <pre class="line before"><span class="ws"> </span>with open(pdf_file, &#39;rb&#39;) as file:</pre> <pre class="line current"><span class="ws"> </span>reader = PyPDF2.PdfFileReader(file) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>text = &#39;&#39;</pre> <pre class="line after"><span class="ws"> </span>for page in range(reader.numPages):</pre> <pre class="line after"><span class="ws"> </span>text += reader.getPage(page).extractText()</pre> <pre class="line after"><span class="ws"> </span>return text</pre> <pre class="line after"><span class="ws"></span> </pre></div> </div> <li><div class="frame" id="frame-2867351327440"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\PyPDF2\_reader.py"</cite>, line <em class="line">1974</em>, in <code class="function">__init__</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>return retval</pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"></span>class PdfFileReader(PdfReader): # pragma: no cover</pre> <pre class="line before"><span class="ws"> </span>def __init__(self, *args: Any, **kwargs: Any) -&gt; None:</pre> <pre class="line current"><span class="ws"> </span>deprecation_with_replacement(&#34;PdfFileReader&#34;, &#34;PdfReader&#34;, &#34;3.0.0&#34;) <span class="ws"> </span>^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>if &#34;strict&#34; not in kwargs and len(args) &lt; 2:</pre> <pre class="line after"><span class="ws"> </span>kwargs[&#34;strict&#34;] = True # maintain the default</pre> <pre class="line after"><span class="ws"> </span>super().__init__(*args, **kwargs)</pre></div> </div> <li><div class="frame" id="frame-2867351331184"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\PyPDF2\_utils.py"</cite>, line <em class="line">369</em>, in <code class="function">deprecation_with_replacement</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>old_name: str, new_name: str, removed_in: str = &#34;3.0.0&#34;</pre> <pre class="line before"><span class="ws"></span>) -&gt; None:</pre> <pre class="line before"><span class="ws"> </span>&#34;&#34;&#34;</pre> <pre class="line before"><span class="ws"> </span>Raise an exception that a feature was already removed, but has a replacement.</pre> <pre class="line before"><span class="ws"> </span>&#34;&#34;&#34;</pre> <pre class="line current"><span class="ws"> </span>deprecation(DEPR_MSG_HAPPENED.format(old_name, removed_in, new_name)) <span class="ws"> </span>^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span>def deprecate_no_replacement(name: str, removed_in: str = &#34;3.0.0&#34;) -&gt; None:</pre> <pre class="line after"><span class="ws"> </span>&#34;&#34;&#34;</pre> <pre class="line after"><span class="ws"> </span>Raise an exception that a feature will be removed without replacement.</pre></div> </div> <li><div class="frame" id="frame-2867351331904"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\PyPDF2\_utils.py"</cite>, line <em class="line">351</em>, in <code class="function">deprecation</code></h4> <div class="source library"><pre class="line before"><span class="ws"></span>def deprecate(msg: str, stacklevel: int = 3) -&gt; None:</pre> <pre class="line before"><span class="ws"> </span>warnings.warn(msg, DeprecationWarning, stacklevel=stacklevel)</pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"></span>def deprecation(msg: str) -&gt; None:</pre> <pre class="line current"><span class="ws"> </span>raise DeprecationError(msg) <span class="ws"> </span>^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span>def deprecate_with_replacement(</pre> <pre class="line after"><span class="ws"> </span>old_name: str, new_name: str, removed_in: str = &#34;3.0.0&#34;</pre> <pre class="line after"><span class="ws"></span>) -&gt; None:</pre></div> </div> </ul> <blockquote>PyPDF2.errors.DeprecationError: PdfFileReader is deprecated and was removed in PyPDF2 3.0.0. Use PdfReader instead. </blockquote> </div> <div class="plain"> <p> This is the Copy/Paste friendly version of the traceback. </p> <textarea cols="50" rows="10" name="code" readonly>Traceback (most recent call last): File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 1488, in __call__ return self.wsgi_app(environ, start_response) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 1466, in wsgi_app response = self.handle_exception(e) ^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 1463, in wsgi_app response = self.full_dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 872, in full_dispatch_request rv = self.handle_user_exception(e) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 870, in full_dispatch_request rv = self.dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 855, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py&#34;, line 43, in upload_pdf extracted_text = extract_text_from_pdf(file_path) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py&#34;, line 9, in extract_text_from_pdf reader = PyPDF2.PdfFileReader(file) ^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\PyPDF2\_reader.py&#34;, line 1974, in __init__ deprecation_with_replacement(&#34;PdfFileReader&#34;, &#34;PdfReader&#34;, &#34;3.0.0&#34;) File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\PyPDF2\_utils.py&#34;, line 369, in deprecation_with_replacement deprecation(DEPR_MSG_HAPPENED.format(old_name, removed_in, new_name)) File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\PyPDF2\_utils.py&#34;, line 351, in deprecation raise DeprecationError(msg) PyPDF2.errors.DeprecationError: PdfFileReader is deprecated and was removed in PyPDF2 3.0.0. Use PdfReader instead. </textarea> </div> <div class="explanation"> The debugger caught an exception in your WSGI application. You can now look at the traceback which led to the error. <span class="nojavascript"> If you enable JavaScript you can also use additional features such as code execution (if the evalex feature is enabled), automatic pasting of the exceptions and much more.</span> </div> <div class="footer"> Brought to you by <strong class="arthur">DON'T PANIC</strong>, your friendly Werkzeug powered traceback interpreter. </div> </div> <div class="pin-prompt"> <div class="inner"> <h3>Console Locked</h3> <p> The console is locked and needs to be unlocked by entering the PIN. You can find the PIN printed out on the standard output of your shell that runs the server. <form> <p>PIN: <input type=text name=pin size=14> <input type=submit name=btn value="Confirm Pin"> </form> </div> </div> </body> </html>  </div>

ChatGPT:

The error message you're seeing indicates that the PdfFileReader class from the PyPDF2 library you are using in your app.py file has been deprecated and removed in PyPDF2 version 3.0.0. You need to use the PdfReader class instead. To resolve this issue, you should update your extract_text_from_pdf function in your app.py file to use the new PdfReader class.

Here's how you can modify the function:

python
import PyPDF2

def extract_text_from_pdf(pdf_file):
    with open(pdf_file, 'rb') as file:
        reader = PyPDF2.PdfReader(file)
        text = ''
        for page in range(len(reader.pages)):
            text += reader.pages[page].extract_text()
        return text

In this updated function, PdfFileReader(file) is replaced with PdfReader(file), and reader.numPages is replaced with len(reader.pages). Additionally, reader.getPage(page).extractText() is replaced with reader.pages[page].extract_text().

After making these changes, save your app.py file and restart your Flask application. Then, you can try sending the PDF file again using Postman to test if the issue is resolved.

Human: <div class="">please provide me exactly everything that needs to be in app.py document</div>

ChatGPT:

Certainly! Based on your requirements and the modifications needed to address the deprecation issue with PyPDF2, here's the complete content for your app.py file. This includes the Flask setup, PDF text extraction using PdfReader from PyPDF2, integration with OpenAI for text summarization, and the route for handling PDF uploads:

python
from flask import Flask, request, jsonify
import PyPDF2
import openai

app = Flask(__name__)

def extract_text_from_pdf(pdf_file):
    with open(pdf_file, 'rb') as file:
        reader = PyPDF2.PdfReader(file)
        text = ''
        for page in range(len(reader.pages)):
            text += reader.pages[page].extract_text()
        return text

def summarize_text(text):
    openai.api_key = 'your-api-key'  # Replace with your actual OpenAI API key
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt="Summarize the following text:\n\n" + text,
        max_tokens=150
    )
    return response.choices[0].text.strip()

@app.route('/')
def hello_world():
    return 'Hello, World!'

@app.route('/upload_pdf', methods=['POST'])
def upload_pdf():
    # Check if the post request has the file part
    if 'pdf' not in request.files:
        return jsonify({'error': 'No PDF file part in the request'}), 400
    file = request.files['pdf']

    # If the user does not select a file, the browser submits an
    # empty file without a filename.
    if file.filename == '':
        return jsonify({'error': 'No selected file'}), 400

    if file:
        file_path = "uploaded.pdf"
        file.save(file_path)
        extracted_text = extract_text_from_pdf(file_path)
        summary = summarize_text(extracted_text)
        return jsonify({"summary": summary})

if __name__ == '__main__':
    app.run(debug=True)

Make sure to replace 'your-api-key' with your actual OpenAI API key in the summarize_text function.

This script sets up a Flask web application with two routes:

The root route ('/') returns a simple "Hello, World!" message.
The /upload_pdf route accepts PDF file uploads, extracts their text, and then uses OpenAI's GPT model to summarize the text, returning the summary as a JSON response.

Remember to restart your Flask application after updating app.py. Once the application is running, you can test the PDF upload and summarization feature using Postman.

Human: <div class="">show me exactly where to put my api key</div>

ChatGPT:

Certainly! You need to place your OpenAI API key in the summarize_text function within your app.py file. Here's the specific part of the code with an indication of where to insert your API key:

python
def summarize_text(text):
    openai.api_key = 'your-api-key'  # Replace 'your-api-key' with your actual API key
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt="Summarize the following text:\n\n" + text,
        max_tokens=150
    )
    return response.choices[0].text.strip()

In this snippet, replace 'your-api-key' with the actual API key you received from OpenAI. It should be a string, so make sure it's enclosed in quotes.

For example, if your API key is 12345abcde, the line would look like this:

python
openai.api_key = '12345abcde'

After making this change, save your app.py file and restart the Flask application to apply the update. Now your application will be able to communicate with OpenAI's API using your key. Remember to keep your API key confidential to prevent unauthorized use.

Human: <div class="">do i use the ' ' around my api key</div>

ChatGPT:

Yes, you should include your API key as a string within single quotes (' ') in your app.py file. The API key is a string, and in Python, strings must be enclosed in quotes (either single or double quotes are fine).

For example, if your API key is 12345abcde, you would modify the line in your app.py file like this:

python
openai.api_key = '12345abcde'

This ensures that Python correctly interprets your API key as a string value. After adding your API key, save the changes to app.py and restart your Flask application for the changes to take effect.

Human: <div class="">how do i restart flask application</div>

ChatGPT:

Restarting your Flask application is a simple process. Here's how you can do it:

Stop the Running Flask Application:
- If your Flask app is currently running in a command line interface (CLI) or terminal window, you can stop it by pressing Ctrl + C on your keyboard. This key combination sends an interrupt signal to the application, causing it to stop running.
Restart the Application:
- After you've stopped the application, you can restart it by running the same command you used to start it initially. If your Flask application's main file is named app.py, you would typically restart it with the following command:
```
bash
python app.py
```
- This command should be executed in the CLI or terminal window, and you should be in the directory where app.py is located.
Verify the Restart:
- Upon restarting, Flask typically prints output to the CLI indicating that it's running, along with the local URL where the application is being served (usually http://127.0.0.1:5000/ or similar).
- You can visit this URL in your web browser to ensure that the Flask application has restarted successfully.

Remember, any changes you make to the app.py file (like adding your API key) will only take effect once you restart the Flask application using these steps.

Human: <div class="">before using the above command do i need to change directories</div>

ChatGPT:

Yes, before running the command python app.py to start your Flask application, you need to ensure you are in the correct directory where your app.py file is located. This is important because the command needs to be executed in the same directory as your Flask application's main file.

To change to the correct directory, use the cd (change directory) command in your command line interface or terminal. For example, if your app.py file is located in a folder named MyExpertReportChatbot on your Desktop, you would use the following commands:

On Windows:

bash
cd Desktop\MyExpertReportChatbot

On macOS or Linux:

bash
cd ~/Desktop/MyExpertReportChatbot

After navigating to the correct directory, you can then run the command to start your Flask application:

bash
python app.py

This ensures that the Flask server starts up with the correct context and can find all necessary files it needs to operate.

Human: <div class="">i get the following message</div>

ChatGPT:

To assist you, I need to know the specific message you received. Could you please share the exact message displayed in your command line interface or terminal when you tried to start the Flask application? This information will help me understand the issue and provide appropriate guidance.

Human: <div class=""> File "C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py", line 17 engine="text-davinci-003", IndentationError: unexpected indent </div>

ChatGPT:

The IndentationError: unexpected indent in Python indicates that there's an issue with the spacing or indentation in your app.py file. Python is very particular about indentation, as it uses it to determine the structure of the code.

In your case, the error is pointing to line 17, which is part of the summarize_text function. To fix this, you need to ensure that the indentation is consistent throughout this function. Python typically uses four spaces per indentation level, or a tab, but it's crucial that you don't mix spaces and tabs.

Here's how the summarize_text function should be indented:

python
def summarize_text(text):
    openai.api_key = 'your-api-key'  # Replace with your actual OpenAI API key
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt="Summarize the following text:\n\n" + text,
        max_tokens=150
    )
    return response.choices[0].text.strip()

Make sure that each level of indentation is consistent. For example, all lines inside the function definition should be indented by the same amount (usually four spaces), and lines inside the openai.Completion.create call should be further indented by the same amount.

After correcting the indentation, save your file and try running the Flask application again. If you encounter more errors or need further assistance, feel free to ask!

Human: <div class="">okay now that is done let's go back to what we were doing and continue</div>

ChatGPT:

Great! Now that the indentation issue in your Flask application is resolved, let's proceed with the next steps. Assuming your app.py is correctly set up for PDF text extraction and GPT-based text summarization, the next step is to run your Flask application and test the PDF upload and summarization feature.

Running Your Flask Application

Open your Command Line Interface (CLI).
Navigate to Your Project Directory where app.py is located.
Run the Flask Application: Execute the command python app.py. This will start the Flask server.

Testing the Application with Postman

Open Postman: Launch Postman on your computer.
Create a New Request:
- Set the method to POST.
- Enter the URL for your Flask route. If running locally, it's likely http://127.0.0.1:5000/upload_pdf.
Configure the Request:
- Go to the Body tab.
- Select form-data.
- Enter pdf as the key, and set the type to File.
- Upload the PDF file you want to summarize by clicking on the Select Files button next to the key field.
Send the Request:
- Click the Send button. This will upload the PDF to your Flask application and return a response.
View the Response:
- Postman will display the response from your Flask app. If everything is working correctly, you should see the summarized text in JSON format.

Important Notes

Ensure your Flask application is running when you send the request from Postman.
If you encounter any errors in Postman or within your Flask application, carefully read the error messages for troubleshooting.

If you have any questions or face issues during testing, feel free to reach out for further assistance! 🚀💻📄

Human: <div class="">i upoladed two files by accident. how do i delete one</div>

ChatGPT:

To remove an accidentally uploaded file in Postman when configuring your request, you can follow these steps:

Go to the Request Body: In Postman, with your request open, navigate to the Body tab where you previously set up your file upload.
Locate the File Upload Fields: In the form-data section where you uploaded your files, you should see the file fields you added. Each file field will have its own row.
Remove the Extra File:
- Find the row corresponding to the file you want to remove.
- To the right of the file field, there should be a trash can icon or a minus (-) sign. Click on this icon to remove the file field from your request.
Ensure Correct File Setup: After removing the extra file, double-check that the remaining file is the correct one you want to upload and summarize.
Resend the Request: Once you've adjusted the file upload fields to your satisfaction, you can click the Send button again to submit the request to your Flask application.

These steps should help you manage and correct the file uploads in your Postman request. If you need further assistance or have other questions, feel free to ask!

Human: <div class="">i uploaded file and got this message: The file above is not in your working directory, and will be unavailable to your teammates when you share the request. You can either set up your working directory in Settings, or upload the file to Postman</div>

ChatGPT:

The message you're seeing in Postman indicates that the file you're trying to upload is not in the designated working directory of Postman. This can be an issue especially when you want to share your Postman requests with teammates, as the file won't be accessible to them. Here's how you can resolve this:

Option 1: Set Up Your Working Directory in Postman

Go to Postman Settings:
- Click on the wrench icon in the top-right corner of Postman, or go to File > Settings.
Select the Working Directory:
- In the Settings, find the option for setting your working directory.
- This directory is where Postman will look for files to upload. You can set this to a folder where your PDF files are stored.
Move Your PDF File to the Working Directory:
- Copy or move the PDF file you want to upload into the working directory you just set in Postman.
Retry Uploading the File:
- Go back to your request and upload the file again from the new working directory location.

Option 2: Upload the File to Postman

If you don't want to set up a working directory or if you're just testing things out:

Upload the File Directly in the Request:
- When you choose a file to upload in your request, select the file from its current location on your computer.
Send the Request as Normal:
- After uploading, click Send to submit the request.

This approach is straightforward but remember that the file won't be included if you export or share your Postman collection with others. They would need access to the same file on their local machine.

Sharing Requests with Files

If you plan to share your Postman requests with teammates, using a shared working directory is a more effective approach. This ensures that everyone has access to the same set of files and can test the requests without file path issues.

After setting this up, try sending your request again and the warning should no longer appear. If you encounter any more issues or have questions, feel free to ask!

Human: <div class="">i hit send and got this message <!doctype html> <html lang=en> <head> <title>openai.lib._old_api.APIRemovedInV1: You tried to access openai.Completion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 // Werkzeug Debugger</title> <link rel="stylesheet" href="?__debugger__=yes&amp;cmd=resource&amp;f=style.css"> <link rel="shortcut icon" href="?__debugger__=yes&amp;cmd=resource&amp;f=console.png"> <script src="?__debugger__=yes&amp;cmd=resource&amp;f=debugger.js"></script> <script> var CONSOLE_MODE = false, EVALEX = true, EVALEX_TRUSTED = false, SECRET = "iTyBMdzDK0aQBJzO96Ve"; </script> </head> <body style="background-color: #fff"> <div class="debugger"> <h1>APIRemovedInV1</h1> <div class="detail"> <p class="errormsg">openai.lib._old_api.APIRemovedInV1: You tried to access openai.Completion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 </p> </div> <h2 class="traceback">Traceback <em>(most recent call last)</em></h2> <div class="traceback"> <h3></h3> <ul><li><div class="frame" id="frame-1830119175168"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">1488</em>, in <code class="function">__call__</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>) -&gt; cabc.Iterable[bytes]:</pre> <pre class="line before"><span class="ws"> </span>&#34;&#34;&#34;The WSGI server calls the Flask application object as the</pre> <pre class="line before"><span class="ws"> </span>WSGI application. This calls :meth:`wsgi_app`, which can be</pre> <pre class="line before"><span class="ws"> </span>wrapped to apply middleware.</pre> <pre class="line before"><span class="ws"> </span>&#34;&#34;&#34;</pre> <pre class="line current"><span class="ws"> </span>return self.wsgi_app(environ, start_response) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre></div> </div> <li><div class="frame" id="frame-1830119175888"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">1466</em>, in <code class="function">wsgi_app</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>ctx.push()</pre> <pre class="line before"><span class="ws"> </span>response = self.full_dispatch_request()</pre> <pre class="line before"><span class="ws"> </span>except Exception as e:</pre> <pre class="line before"><span class="ws"> </span>error = e</pre> <pre class="line current"><span class="ws"> </span>response = self.handle_exception(e) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>except: # noqa: B001</pre> <pre class="line after"><span class="ws"> </span>error = sys.exc_info()[1]</pre> <pre class="line after"><span class="ws"> </span>raise</pre> <pre class="line after"><span class="ws"> </span>return response(environ, start_response)</pre> <pre class="line after"><span class="ws"> </span>finally:</pre></div> </div> <li><div class="frame" id="frame-1830119176032"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">1463</em>, in <code class="function">wsgi_app</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>ctx = self.request_context(environ)</pre> <pre class="line before"><span class="ws"> </span>error: BaseException | None = None</pre> <pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>ctx.push()</pre> <pre class="line current"><span class="ws"> </span>response = self.full_dispatch_request() <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>except Exception as e:</pre> <pre class="line after"><span class="ws"> </span>error = e</pre> <pre class="line after"><span class="ws"> </span>response = self.handle_exception(e)</pre> <pre class="line after"><span class="ws"> </span>except: # noqa: B001</pre> <pre class="line after"><span class="ws"> </span>error = sys.exc_info()[1]</pre></div> </div> <li><div class="frame" id="frame-1830119176176"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">872</em>, in <code class="function">full_dispatch_request</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>request_started.send(self, _async_wrapper=self.ensure_sync)</pre> <pre class="line before"><span class="ws"> </span>rv = self.preprocess_request()</pre> <pre class="line before"><span class="ws"> </span>if rv is None:</pre> <pre class="line before"><span class="ws"> </span>rv = self.dispatch_request()</pre> <pre class="line before"><span class="ws"> </span>except Exception as e:</pre> <pre class="line current"><span class="ws"> </span>rv = self.handle_user_exception(e) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>return self.finalize_request(rv)</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"> </span>def finalize_request(</pre> <pre class="line after"><span class="ws"> </span>self,</pre> <pre class="line after"><span class="ws"> </span>rv: ft.ResponseReturnValue | HTTPException,</pre></div> </div> <li><div class="frame" id="frame-1830119176320"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">870</em>, in <code class="function">full_dispatch_request</code></h4> <div class="source library"><pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>request_started.send(self, _async_wrapper=self.ensure_sync)</pre> <pre class="line before"><span class="ws"> </span>rv = self.preprocess_request()</pre> <pre class="line before"><span class="ws"> </span>if rv is None:</pre> <pre class="line current"><span class="ws"> </span>rv = self.dispatch_request() <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>except Exception as e:</pre> <pre class="line after"><span class="ws"> </span>rv = self.handle_user_exception(e)</pre> <pre class="line after"><span class="ws"> </span>return self.finalize_request(rv)</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"> </span>def finalize_request(</pre></div> </div> <li><div class="frame" id="frame-1830119176464"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">855</em>, in <code class="function">dispatch_request</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>and req.method == &#34;OPTIONS&#34;</pre> <pre class="line before"><span class="ws"> </span>):</pre> <pre class="line before"><span class="ws"> </span>return self.make_default_options_response()</pre> <pre class="line before"><span class="ws"> </span># otherwise dispatch to the handler for that endpoint</pre> <pre class="line before"><span class="ws"> </span>view_args: dict[str, t.Any] = req.view_args # type: ignore[assignment]</pre> <pre class="line current"><span class="ws"> </span>return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return] <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"> </span>def full_dispatch_request(self) -&gt; Response:</pre> <pre class="line after"><span class="ws"> </span>&#34;&#34;&#34;Dispatches the request and on top of that performs request</pre> <pre class="line after"><span class="ws"> </span>pre and postprocessing as well as HTTP exception catching and</pre> <pre class="line after"><span class="ws"> </span>error handling.</pre></div> </div> <li><div class="frame" id="frame-1830119176608"> <h4>File <cite class="filename">"C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py"</cite>, line <em class="line">44</em>, in <code class="function">upload_pdf</code></h4> <div class="source "><pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"> </span>if file:</pre> <pre class="line before"><span class="ws"> </span>file_path = &#34;uploaded.pdf&#34;</pre> <pre class="line before"><span class="ws"> </span>file.save(file_path)</pre> <pre class="line before"><span class="ws"> </span>extracted_text = extract_text_from_pdf(file_path)</pre> <pre class="line current"><span class="ws"> </span>summary = summarize_text(extracted_text) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>return jsonify({&#34;summary&#34;: summary})</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span>if __name__ == &#39;__main__&#39;:</pre> <pre class="line after"><span class="ws"> </span>app.run(debug=True)</pre> <pre class="line after"><span class="ws"></span> </pre></div> </div> <li><div class="frame" id="frame-1830119176752"> <h4>File <cite class="filename">"C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py"</cite>, line <em class="line">17</em>, in <code class="function">summarize_text</code></h4> <div class="source "><pre class="line before"><span class="ws"> </span>text += reader.pages[page].extract_text()</pre> <pre class="line before"><span class="ws"> </span>return text</pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"></span>def summarize_text(text):</pre> <pre class="line before"><span class="ws"> </span>openai.api_key = &#39;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&#39;</pre> <pre class="line current"><span class="ws"> </span>response = openai.Completion.create( <span class="ws"> </span> </pre> <pre class="line after"><span class="ws"> </span>engine=&#34;text-davinci-003&#34;,</pre> <pre class="line after"><span class="ws"> </span>prompt=&#34;Summarize the following text:\n\n&#34; + text,</pre> <pre class="line after"><span class="ws"> </span>max_tokens=150</pre> <pre class="line after"><span class="ws"> </span>)</pre> <pre class="line after"><span class="ws"> </span>return response.choices[0].text.strip()</pre></div> </div> <li><div class="frame" id="frame-1830119176896"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\openai\lib\_old_api.py"</cite>, line <em class="line">39</em>, in <code class="function">__call__</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span># we don&#39;t break people that are just checking the attributes</pre> <pre class="line before"><span class="ws"> </span># of a module</pre> <pre class="line before"><span class="ws"> </span>return self</pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"> </span>def __call__(self, *_args: Any, **_kwargs: Any) -&gt; Any:</pre> <pre class="line current"><span class="ws"> </span>raise APIRemovedInV1(symbol=self._symbol) <span class="ws"> </span>^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span>SYMBOLS = [</pre> <pre class="line after"><span class="ws"> </span>&#34;Edit&#34;,</pre> <pre class="line after"><span class="ws"> </span>&#34;File&#34;,</pre></div> </div> </ul> <blockquote>openai.lib._old_api.APIRemovedInV1: You tried to access openai.Completion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 </blockquote> </div> <div class="plain"> <p> This is the Copy/Paste friendly version of the traceback. </p> <textarea cols="50" rows="10" name="code" readonly>Traceback (most recent call last): File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 1488, in __call__ return self.wsgi_app(environ, start_response) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 1466, in wsgi_app response = self.handle_exception(e) ^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 1463, in wsgi_app response = self.full_dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 872, in full_dispatch_request rv = self.handle_user_exception(e) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 870, in full_dispatch_request rv = self.dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 855, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py&#34;, line 44, in upload_pdf summary = summarize_text(extracted_text) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py&#34;, line 17, in summarize_text response = openai.Completion.create( ^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\openai\lib\_old_api.py&#34;, line 39, in __call__ raise APIRemovedInV1(symbol=self._symbol) openai.lib._old_api.APIRemovedInV1: You tried to access openai.Completion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 </textarea> </div> <div class="explanation"> The debugger caught an exception in your WSGI application. You can now look at the traceback which led to the error. <span class="nojavascript"> If you enable JavaScript you can also use additional features such as code execution (if the evalex feature is enabled), automatic pasting of the exceptions and much more.</span> </div> <div class="footer"> Brought to you by <strong class="arthur">DON'T PANIC</strong>, your friendly Werkzeug powered traceback interpreter. </div> </div> <div class="pin-prompt"> <div class="inner"> <h3>Console Locked</h3> <p> The console is locked and needs to be unlocked by entering the PIN. You can find the PIN printed out on the standard output of your shell that runs the server. <form> <p>PIN: <input type=text name=pin size=14> <input type=submit name=btn value="Confirm Pin"> </form> </div> </div> </body> </html>  </div>

ChatGPT:

The error message indicates that the version of the OpenAI Python library you are using is not compatible with the old openai.Completion.create method. The library has been updated and the way to interact with OpenAI's API has changed in versions >=1.0.0.

To resolve this, you have two options:

Option 1: Update Your Code to Use the New API

You can update your code to be compatible with the new OpenAI API. Refer to the OpenAI Python library documentation for guidance on how to use the updated API methods.

The updated method to make an API call might look like this (based on the new library structure):

python
import openai

def summarize_text(text):
    openai.api_key = 'your-api-key'

    response = openai.ChatCompletion.create(
        model="text-davinci-003",
        messages=[{"role": "system", "content": "Summarize the following text:"}, 
                  {"role": "user", "content": text}]
    )
    return response['choices'][0]['message']['content']

Option 2: Downgrade Your OpenAI Library

Alternatively, you can downgrade your OpenAI Python library to a version that supports the old API. This can be done by running:

bash
pip install openai==0.28

After downgrading, the existing code in your app.py file using openai.Completion.create should work as expected.

After Resolving the Issue

Once you've either updated your code for the new API or downgraded the library, restart your Flask application and try sending the PDF file again using Postman.

Choose the option that best suits your needs. If you prefer to use the latest features and stay updated with the OpenAI API, updating your code as per the new API is the way to go. However, if you want a quick fix without changing much of your current code, downgrading the library version might be more convenient.

Human: <div class="">i updated my code as follows from flask import Flask, request, jsonify import PyPDF2 import openai app = Flask(__name__) def extract_text_from_pdf(pdf_file): with open(pdf_file, 'rb') as file: reader = PyPDF2.PdfReader(file) text = '' for page in range(len(reader.pages)): text += reader.pages[page].extract_text() return text def summarize_text(text): openai.api_key = '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' response = openai.ChatCompletion.create( model="text-davinci-003", messages=[{"role": "system", "content": "Summarize the following text:"}, {"role": "user", "content": text}] ) return response['choices'][0]['message']['content'] @app.route('/') def hello_world(): return 'Hello, World!' @app.route('/upload_pdf', methods=['POST']) def upload_pdf(): # Check if the post request has the file part if 'pdf' not in request.files: return jsonify({'error': 'No PDF file part in the request'}), 400 file = request.files['pdf'] # If the user does not select a file, the browser submits an # empty file without a filename. if file.filename == '': return jsonify({'error': 'No selected file'}), 400 if file: file_path = "uploaded.pdf" file.save(file_path) extracted_text = extract_text_from_pdf(file_path) summary = summarize_text(extracted_text) return jsonify({"summary": summary}) if __name__ == '__main__': app.run(debug=True) is tghis correct </div>

ChatGPT:

Your updated app.py code looks correct in terms of integrating the new OpenAI API and handling the PDF text extraction and summarization process. You've correctly updated the summarize_text function to use the new openai.ChatCompletion.create method which is compatible with the latest version of the OpenAI library.

Here's a quick recap of your script's functionalities:

Flask App Initialization: You've set up a Flask app instance.
PDF Text Extraction: The extract_text_from_pdf function reads a PDF file and extracts text from it using PyPDF2.
Text Summarization: The summarize_text function sends the extracted text to OpenAI's GPT-3 model for summarization.
Flask Routes:
- A root route ('/') returning "Hello, World!".
- An /upload_pdf route for handling PDF file uploads, extracting text, and returning the summarized text as JSON.
OpenAI API Key: You've included your OpenAI API key directly in the code. Ensure this key remains confidential to prevent unauthorized use.
Running the Flask App: The script is configured to run the Flask app when executed.

After you start your Flask application by running python app.py, you should be able to test the PDF upload and summarization feature using a tool like Postman.

Make sure to test the functionality thoroughly, and if you encounter any errors or unexpected behaviors, the error messages should give you clues about what might need fixing. If you need further assistance or encounter any issues, feel free to reach out!

Human: <div class="">okay now that is done. please tekll me how to test this</div>

ChatGPT:

Testing your Flask application with the PDF upload and summarization feature can be done using Postman, a popular API testing tool. Here's how you can do it:

Step 1: Start Your Flask Application

Open a Command Line Interface (CLI) and navigate to the directory where your app.py is located.
Run Your Flask Application by executing the command python app.py. This should start the Flask server, and you'll see a message like "* Running on http://127.0.0.1:5000/".

Step 2: Test with Postman

Open Postman: Launch the Postman application on your computer. If you don't have it installed, you can download it from the Postman website.
Create a New Request:
- Click on the New button or the + tab to start a new request.
- Set the HTTP method to POST.
- Enter the request URL, which is the local address of your Flask app followed by the /upload_pdf route (typically http://127.0.0.1:5000/upload_pdf).
Configure the Request for File Upload:
- Go to the Body tab in Postman.
- Select form-data.
- In the key-value input, type pdf in the key field, select File from the dropdown on the right, and then upload your PDF file by clicking on the Select Files button next to the key.
Send the Request:
- Click the Send button to submit the request to your Flask application.
View the Response:
- After sending the request, Postman will display the response from your Flask app.
- If everything is set up correctly, you should receive a JSON response with the summarized text from the PDF file.

Step 3: Evaluate the Response

Check the response in Postman to see if it contains the summarized text as expected.
If there are any errors, Postman will display the error message. You can use this information for troubleshooting.

Step 4: Troubleshooting

Ensure your Flask application is running and accessible at the correct URL.
Confirm that the pdf key matches the key expected in your Flask route.
Make sure the PDF file you're testing contains extractable text.

Testing with Postman is an effective way to simulate how the application will handle real requests, and it's a crucial step in API development and debugging. If you encounter any issues or have questions during testing, feel free to ask for assistance!

Human: <div class="">come on. I am getting this error message. I have done everything you advised me to do. <!doctype html> <html lang=en> <head> <title>openai.lib._old_api.APIRemovedInV1: You tried to access openai.ChatCompletion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 // Werkzeug Debugger</title> <link rel="stylesheet" href="?__debugger__=yes&amp;cmd=resource&amp;f=style.css"> <link rel="shortcut icon" href="?__debugger__=yes&amp;cmd=resource&amp;f=console.png"> <script src="?__debugger__=yes&amp;cmd=resource&amp;f=debugger.js"></script> <script> var CONSOLE_MODE = false, EVALEX = true, EVALEX_TRUSTED = false, SECRET = "QzCURC8HtJcgmFaq40rj"; </script> </head> <body style="background-color: #fff"> <div class="debugger"> <h1>APIRemovedInV1</h1> <div class="detail"> <p class="errormsg">openai.lib._old_api.APIRemovedInV1: You tried to access openai.ChatCompletion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 </p> </div> <h2 class="traceback">Traceback <em>(most recent call last)</em></h2> <div class="traceback"> <h3></h3> <ul><li><div class="frame" id="frame-2006347070000"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">1488</em>, in <code class="function">__call__</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>) -&gt; cabc.Iterable[bytes]:</pre> <pre class="line before"><span class="ws"> </span>&#34;&#34;&#34;The WSGI server calls the Flask application object as the</pre> <pre class="line before"><span class="ws"> </span>WSGI application. This calls :meth:`wsgi_app`, which can be</pre> <pre class="line before"><span class="ws"> </span>wrapped to apply middleware.</pre> <pre class="line before"><span class="ws"> </span>&#34;&#34;&#34;</pre> <pre class="line current"><span class="ws"> </span>return self.wsgi_app(environ, start_response) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre></div> </div> <li><div class="frame" id="frame-2006347070864"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">1466</em>, in <code class="function">wsgi_app</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>ctx.push()</pre> <pre class="line before"><span class="ws"> </span>response = self.full_dispatch_request()</pre> <pre class="line before"><span class="ws"> </span>except Exception as e:</pre> <pre class="line before"><span class="ws"> </span>error = e</pre> <pre class="line current"><span class="ws"> </span>response = self.handle_exception(e) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>except: # noqa: B001</pre> <pre class="line after"><span class="ws"> </span>error = sys.exc_info()[1]</pre> <pre class="line after"><span class="ws"> </span>raise</pre> <pre class="line after"><span class="ws"> </span>return response(environ, start_response)</pre> <pre class="line after"><span class="ws"> </span>finally:</pre></div> </div> <li><div class="frame" id="frame-2006347071008"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">1463</em>, in <code class="function">wsgi_app</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>ctx = self.request_context(environ)</pre> <pre class="line before"><span class="ws"> </span>error: BaseException | None = None</pre> <pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>ctx.push()</pre> <pre class="line current"><span class="ws"> </span>response = self.full_dispatch_request() <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>except Exception as e:</pre> <pre class="line after"><span class="ws"> </span>error = e</pre> <pre class="line after"><span class="ws"> </span>response = self.handle_exception(e)</pre> <pre class="line after"><span class="ws"> </span>except: # noqa: B001</pre> <pre class="line after"><span class="ws"> </span>error = sys.exc_info()[1]</pre></div> </div> <li><div class="frame" id="frame-2006347071152"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">872</em>, in <code class="function">full_dispatch_request</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>request_started.send(self, _async_wrapper=self.ensure_sync)</pre> <pre class="line before"><span class="ws"> </span>rv = self.preprocess_request()</pre> <pre class="line before"><span class="ws"> </span>if rv is None:</pre> <pre class="line before"><span class="ws"> </span>rv = self.dispatch_request()</pre> <pre class="line before"><span class="ws"> </span>except Exception as e:</pre> <pre class="line current"><span class="ws"> </span>rv = self.handle_user_exception(e) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>return self.finalize_request(rv)</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"> </span>def finalize_request(</pre> <pre class="line after"><span class="ws"> </span>self,</pre> <pre class="line after"><span class="ws"> </span>rv: ft.ResponseReturnValue | HTTPException,</pre></div> </div> <li><div class="frame" id="frame-2006347071296"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">870</em>, in <code class="function">full_dispatch_request</code></h4> <div class="source library"><pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>request_started.send(self, _async_wrapper=self.ensure_sync)</pre> <pre class="line before"><span class="ws"> </span>rv = self.preprocess_request()</pre> <pre class="line before"><span class="ws"> </span>if rv is None:</pre> <pre class="line current"><span class="ws"> </span>rv = self.dispatch_request() <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>except Exception as e:</pre> <pre class="line after"><span class="ws"> </span>rv = self.handle_user_exception(e)</pre> <pre class="line after"><span class="ws"> </span>return self.finalize_request(rv)</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"> </span>def finalize_request(</pre></div> </div> <li><div class="frame" id="frame-2006347909296"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">855</em>, in <code class="function">dispatch_request</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>and req.method == &#34;OPTIONS&#34;</pre> <pre class="line before"><span class="ws"> </span>):</pre> <pre class="line before"><span class="ws"> </span>return self.make_default_options_response()</pre> <pre class="line before"><span class="ws"> </span># otherwise dispatch to the handler for that endpoint</pre> <pre class="line before"><span class="ws"> </span>view_args: dict[str, t.Any] = req.view_args # type: ignore[assignment]</pre> <pre class="line current"><span class="ws"> </span>return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return] <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"> </span>def full_dispatch_request(self) -&gt; Response:</pre> <pre class="line after"><span class="ws"> </span>&#34;&#34;&#34;Dispatches the request and on top of that performs request</pre> <pre class="line after"><span class="ws"> </span>pre and postprocessing as well as HTTP exception catching and</pre> <pre class="line after"><span class="ws"> </span>error handling.</pre></div> </div> <li><div class="frame" id="frame-2006347909152"> <h4>File <cite class="filename">"C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py"</cite>, line <em class="line">45</em>, in <code class="function">upload_pdf</code></h4> <div class="source "><pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"> </span>if file:</pre> <pre class="line before"><span class="ws"> </span>file_path = &#34;uploaded.pdf&#34;</pre> <pre class="line before"><span class="ws"> </span>file.save(file_path)</pre> <pre class="line before"><span class="ws"> </span>extracted_text = extract_text_from_pdf(file_path)</pre> <pre class="line current"><span class="ws"> </span>summary = summarize_text(extracted_text) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>return jsonify({&#34;summary&#34;: summary})</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span>if __name__ == &#39;__main__&#39;:</pre> <pre class="line after"><span class="ws"> </span>app.run(debug=True)</pre> <pre class="line after"><span class="ws"></span> </pre></div> </div> <li><div class="frame" id="frame-2006347907136"> <h4>File <cite class="filename">"C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py"</cite>, line <em class="line">18</em>, in <code class="function">summarize_text</code></h4> <div class="source "><pre class="line before"><span class="ws"> </span>return text</pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"></span>def summarize_text(text):</pre> <pre class="line before"><span class="ws"> </span>openai.api_key = &#39;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&#39;</pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line current"><span class="ws"> </span>response = openai.ChatCompletion.create( <span class="ws"> </span> </pre> <pre class="line after"><span class="ws"> </span>model=&#34;text-davinci-003&#34;,</pre> <pre class="line after"><span class="ws"> </span>messages=[{&#34;role&#34;: &#34;system&#34;, &#34;content&#34;: &#34;Summarize the following text:&#34;},</pre> <pre class="line after"><span class="ws"> </span>{&#34;role&#34;: &#34;user&#34;, &#34;content&#34;: text}]</pre> <pre class="line after"><span class="ws"> </span>)</pre> <pre class="line after"><span class="ws"> </span>return response[&#39;choices&#39;][0][&#39;message&#39;][&#39;content&#39;]</pre></div> </div> <li><div class="frame" id="frame-2006347907424"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\openai\lib\_old_api.py"</cite>, line <em class="line">39</em>, in <code class="function">__call__</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span># we don&#39;t break people that are just checking the attributes</pre> <pre class="line before"><span class="ws"> </span># of a module</pre> <pre class="line before"><span class="ws"> </span>return self</pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"> </span>def __call__(self, *_args: Any, **_kwargs: Any) -&gt; Any:</pre> <pre class="line current"><span class="ws"> </span>raise APIRemovedInV1(symbol=self._symbol) <span class="ws"> </span>^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span>SYMBOLS = [</pre> <pre class="line after"><span class="ws"> </span>&#34;Edit&#34;,</pre> <pre class="line after"><span class="ws"> </span>&#34;File&#34;,</pre></div> </div> </ul> <blockquote>openai.lib._old_api.APIRemovedInV1: You tried to access openai.ChatCompletion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 </blockquote> </div> <div class="plain"> <p> This is the Copy/Paste friendly version of the traceback. </p> <textarea cols="50" rows="10" name="code" readonly>Traceback (most recent call last): File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 1488, in __call__ return self.wsgi_app(environ, start_response) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 1466, in wsgi_app response = self.handle_exception(e) ^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 1463, in wsgi_app response = self.full_dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 872, in full_dispatch_request rv = self.handle_user_exception(e) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 870, in full_dispatch_request rv = self.dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 855, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py&#34;, line 45, in upload_pdf summary = summarize_text(extracted_text) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py&#34;, line 18, in summarize_text response = openai.ChatCompletion.create( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\openai\lib\_old_api.py&#34;, line 39, in __call__ raise APIRemovedInV1(symbol=self._symbol) openai.lib._old_api.APIRemovedInV1: You tried to access openai.ChatCompletion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 </textarea> </div> <div class="explanation"> The debugger caught an exception in your WSGI application. You can now look at the traceback which led to the error. <span class="nojavascript"> If you enable JavaScript you can also use additional features such as code execution (if the evalex feature is enabled), automatic pasting of the exceptions and much more.</span> </div> <div class="footer"> Brought to you by <strong class="arthur">DON'T PANIC</strong>, your friendly Werkzeug powered traceback interpreter. </div> </div> <div class="pin-prompt"> <div class="inner"> <h3>Console Locked</h3> <p> The console is locked and needs to be unlocked by entering the PIN. You can find the PIN printed out on the standard output of your shell that runs the server. <form> <p>PIN: <input type=text name=pin size=14> <input type=submit name=btn value="Confirm Pin"> </form> </div> </div> </body> </html> <!-- Traceback (most recent call last): File "C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py", line 1488, in __call__ return self.wsgi_app(environ, start_response) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py", line 1466, in wsgi_app response = self.handle_exception(e) ^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py", line 1463, in wsgi_app response = self.full_dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py", line 872, in full_dispatch_request rv = self.handle_user_exception(e) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py", line 870, in full_dispatch_request rv = self.dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^ File "C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py", line 855, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py", line 45, in upload_pdf summary = summarize_text(extracted_text) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py", line 18, in summarize_text response = openai.ChatCompletion.create( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\openai\lib\_old_api.py", line 39, in __call__ raise APIRemovedInV1(symbol=self._symbol) openai.lib._old_api.APIRemovedInV1: You tried to access openai.ChatCompletion, but this is no longer supported in openai>=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742</div>

ChatGPT:

The error message indicates that the method openai.ChatCompletion.create you are trying to use is no longer supported in the version of the OpenAI library you have installed (version >=1.0.0). This is a common issue when a library is updated and certain methods or classes are deprecated.

Given the error message, you have two options:

1. Update Your Code to Use the New API

Since the OpenAI API has changed, you will need to adjust your code to be compatible with the new API. Unfortunately, I can't browse the internet to check the latest API syntax, but you can visit the OpenAI Python library GitHub repository and the migration guide for instructions on how to use the new API structure.

2. Downgrade Your OpenAI Library

Alternatively, if you want to continue using the current code without modifications, you can downgrade your OpenAI library to a version that supports the old API. This can be done by running:

bash
pip install openai==0.28

This command will install an older version of the OpenAI library where the openai.Completion.create method is still available.

Steps to Follow

Decide whether you want to update your code for the new API or downgrade the OpenAI library.
If updating the code, adjust the summarize_text function according to the new API structure.
If downgrading, run the pip install openai==0.28 command in your CLI.
After making these changes, restart your Flask application.

Remember to check the official OpenAI Python library documentation for the latest updates and correct usage. If you choose to downgrade, be aware that you won't have access to any newer features or fixes that might be available in the latest versions of the library.

Human: <div class="">where do i put the pip install command</div>

ChatGPT:

To resolve the issue with the OpenAI library version, you should run the pip install openai==0.28 command in your terminal or command prompt. Follow these steps:

Open a terminal or command prompt on your computer.
Type the following command and press Enter:
```
pip install openai==0.28
```
Let the installation process complete. This command will install an older version of the OpenAI library (version 0.28) that is compatible with your code.
Once the installation is successful, you should be able to run your code without encountering the error related to the OpenAI library version.

After running the pip install command, you can try running your Python script or application again, and it should work without any issues.

Human: <div class="">what directory do i run it on</div>

ChatGPT:

You should run the pip install openai==0.28 command in the directory where your Python project or script is located. This ensures that the specified version of the OpenAI library is installed within the context of your project.

Here are the steps to do it:

Open your command prompt or terminal.
Navigate to the directory where your Python project or script is located. You can use the cd command to change directories. For example, if your project is in a directory called "my_project," you can navigate to it like this:
```
bash
cd path/to/my_project
```
Replace path/to/my_project with the actual path to your project directory.
Once you are in the correct directory where your project is located, you can run the pip install openai==0.28 command:
```
pip install openai==0.28
```
The installation will occur within that directory, ensuring that the specified version of the OpenAI library is available for your project.

By running the command in the project directory, you isolate the library installation to that specific project, which is a good practice to manage dependencies for different projects separately.

Human: <div class="">what does this mean C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot>pip install openai==0.28 Collecting openai==0.28 Downloading openai-0.28.0-py3-none-any.whl.metadata (13 kB) Collecting requests>=2.20 (from openai==0.28) Downloading requests-2.31.0-py3-none-any.whl.metadata (4.6 kB) Requirement already satisfied: tqdm in c:\users\jrasansky\appdata\local\programs\python\python312\lib\site-packages (from openai==0.28) (4.66.1) Collecting aiohttp (from openai==0.28) Downloading aiohttp-3.9.1-cp312-cp312-win_amd64.whl.metadata (7.6 kB) Collecting charset-normalizer<4,>=2 (from requests>=2.20->openai==0.28) Downloading charset_normalizer-3.3.2-cp312-cp312-win_amd64.whl.metadata (34 kB) Requirement already satisfied: idna<4,>=2.5 in c:\users\jrasansky\appdata\local\programs\python\python312\lib\site-packages (from requests>=2.20->openai==0.28) (3.6) Collecting urllib3<3,>=1.21.1 (from requests>=2.20->openai==0.28) Downloading urllib3-2.1.0-py3-none-any.whl.metadata (6.4 kB) Requirement already satisfied: certifi>=2017.4.17 in c:\users\jrasansky\appdata\local\programs\python\python312\lib\site-packages (from requests>=2.20->openai==0.28) (2023.11.17) Collecting attrs>=17.3.0 (from aiohttp->openai==0.28) Downloading attrs-23.2.0-py3-none-any.whl.metadata (9.5 kB) Collecting multidict<7.0,>=4.5 (from aiohttp->openai==0.28) Downloading multidict-6.0.4.tar.gz (51 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 51.3/51.3 kB 2.7 MB/s eta 0:00:00 Installing build dependencies ... done Getting requirements to build wheel ... done Installing backend dependencies ... done Preparing metadata (pyproject.toml) ... done Collecting yarl<2.0,>=1.0 (from aiohttp->openai==0.28) Downloading yarl-1.9.4-cp312-cp312-win_amd64.whl.metadata (32 kB) Collecting frozenlist>=1.1.1 (from aiohttp->openai==0.28) Downloading frozenlist-1.4.1-cp312-cp312-win_amd64.whl.metadata (12 kB) Collecting aiosignal>=1.1.2 (from aiohttp->openai==0.28) Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB) Requirement already satisfied: colorama in c:\users\jrasansky\appdata\local\programs\python\python312\lib\site-packages (from tqdm->openai==0.28) (0.4.6) Downloading openai-0.28.0-py3-none-any.whl (76 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 76.5/76.5 kB ? eta 0:00:00 Downloading requests-2.31.0-py3-none-any.whl (62 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 62.6/62.6 kB ? eta 0:00:00 Downloading aiohttp-3.9.1-cp312-cp312-win_amd64.whl (362 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 362.9/362.9 kB 11.4 MB/s eta 0:00:00 Downloading attrs-23.2.0-py3-none-any.whl (60 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 60.8/60.8 kB ? eta 0:00:00 Downloading charset_normalizer-3.3.2-cp312-cp312-win_amd64.whl (100 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100.4/100.4 kB 5.6 MB/s eta 0:00:00 Downloading frozenlist-1.4.1-cp312-cp312-win_amd64.whl (50 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 50.5/50.5 kB ? eta 0:00:00 Downloading urllib3-2.1.0-py3-none-any.whl (104 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 104.6/104.6 kB 6.3 MB/s eta 0:00:00 Downloading yarl-1.9.4-cp312-cp312-win_amd64.whl (76 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 76.4/76.4 kB 4.1 MB/s eta 0:00:00 Building wheels for collected packages: multidict Building wheel for multidict (pyproject.toml) ... error error: subprocess-exited-with-error × Building wheel for multidict (pyproject.toml) did not run successfully. │ exit code: 1 ╰─> [74 lines of output] ********************* * Accelerated build * ********************* running bdist_wheel running build running build_py creating build creating build\lib.win-amd64-cpython-312 creating build\lib.win-amd64-cpython-312\multidict copying multidict\_abc.py -> build\lib.win-amd64-cpython-312\multidict copying multidict\_compat.py -> build\lib.win-amd64-cpython-312\multidict copying multidict\_multidict_base.py -> build\lib.win-amd64-cpython-312\multidict copying multidict\_multidict_py.py -> build\lib.win-amd64-cpython-312\multidict copying multidict\__init__.py -> build\lib.win-amd64-cpython-312\multidict running egg_info writing multidict.egg-info\PKG-INFO writing dependency_links to multidict.egg-info\dependency_links.txt writing top-level names to multidict.egg-info\top_level.txt reading manifest file 'multidict.egg-info\SOURCES.txt' reading manifest template 'MANIFEST.in' warning: no previously-included files matching '*.pyc' found anywhere in distribution warning: no previously-included files found matching 'multidict\_multidict.html' warning: no previously-included files found matching 'multidict\*.so' warning: no previously-included files found matching 'multidict\*.pyd' warning: no previously-included files found matching 'multidict\*.pyd' no previously-included directories found matching 'docs\_build' adding license file 'LICENSE' writing manifest file 'multidict.egg-info\SOURCES.txt' C:\Users\jrasansky\AppData\Local\Temp\pip-build-env-ft1dllsd\overlay\Lib\site-packages\setuptools\command\build_py.py:207: _Warning: Package 'multidict._multilib' is absent from the `packages` configuration. !! ******************************************************************************** ############################ # Package would be ignored # ############################ Python recognizes 'multidict._multilib' as an importable package[^1], but it is absent from setuptools' `packages` configuration. This leads to an ambiguous overall configuration. If you want to distribute this package, please make sure that 'multidict._multilib' is explicitly added to the `packages` configuration field. Alternatively, you can also rely on setuptools' discovery methods (for example by using `find_namespace_packages(...)`/`find_namespace:` instead of `find_packages(...)`/`find:`). You can read more about "package discovery" on setuptools documentation page: - https://setuptools.pypa.io/en/latest/userguide/package_discovery.html If you don't want 'multidict._multilib' to be distributed and are already explicitly excluding 'multidict._multilib' via `find_namespace_packages(...)/find_namespace` or `find_packages(...)/find`, you can try to use `exclude_package_data`, or `include-package-data=False` in combination with a more fine grained `package-data` configuration. You can read more about "package data files" on setuptools documentation page: - https://setuptools.pypa.io/en/latest/userguide/datafiles.html [^1]: For Python, any directory (with suitable naming) can be imported, even if it does not contain any `.py` files. On the other hand, currently there is no concept of package data directory, all directories are treated like packages. ******************************************************************************** !! check.warn(importable) copying multidict\__init__.pyi -> build\lib.win-amd64-cpython-312\multidict copying multidict\py.typed -> build\lib.win-amd64-cpython-312\multidict running build_ext building 'multidict._multidict' extension error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/ [end of output] note: This error originates from a subprocess, and is likely not a problem with pip. ERROR: Failed building wheel for multidict Failed to build multidict ERROR: Could not build wheels for multidict, which is required to install pyproject.toml-based projects</div>

ChatGPT:

The error message you received indicates that there was an issue building the "multidict" package, which is a dependency of the OpenAI library. Specifically, the error states that "Microsoft Visual C++ 14.0 or greater is required" to build this package.

To resolve this issue, you can follow these steps:

Install Microsoft Visual C++ Build Tools: You will need to install Microsoft Visual C++ Build Tools, which include the necessary components to build packages like "multidict." You can download and install them from the following link: Microsoft C++ Build Tools
During the installation of Microsoft C++ Build Tools, make sure to select the necessary components for building C++ projects. This typically includes the "Desktop development with C++" workload.
After installing the build tools, try running the pip install openai==0.28 command again in your project directory.

This should resolve the issue, and the installation should proceed without errors. If you encounter any further issues or have additional questions, please let me know.

Human: <div class="">do i need this download</div>

ChatGPT:

Whether you need to download and install Microsoft Visual C++ Build Tools depends on the specific requirements of the Python packages you intend to use. In your case, you encountered an error message stating that Microsoft Visual C++ 14.0 or greater is required to build the "multidict" package, which is a dependency of the OpenAI library.

Here are some considerations:

Package Requirements: If you plan to use Python packages that have C/C++ extensions or dependencies, you may encounter situations where these packages require Microsoft Visual C++ Build Tools to be installed on your system. This is not unique to the OpenAI library; many Python packages with native extensions may have this requirement.
OpenAI and Dependencies: The OpenAI library itself may have dependencies that require these build tools. In your case, the error occurred during the installation of the "multidict" package, which is a dependency of OpenAI.
Project Needs: If you are working on a project that involves Python packages with C/C++ dependencies, you should consider installing the necessary build tools to ensure that your project's dependencies can be built and installed correctly.
Alternative Solutions: In some cases, you may find pre-built binary packages or wheels for certain dependencies, which can avoid the need to build them from source. However, this depends on the availability of such packages for your specific dependencies.

In summary, whether you need to download and install Microsoft Visual C++ Build Tools depends on your project's specific requirements and the packages you plan to use. If you are working with packages that require these tools, it's a good idea to install them to avoid build errors. If you're unsure, you can check the documentation of the packages you intend to use or seek guidance from the package maintainers.

Human: <div class="">the project we are working does it require this</div>

ChatGPT:

The project we are working on, which involves using the OpenAI library, may require Microsoft Visual C++ Build Tools if any of the Python packages used by the OpenAI library or its dependencies have C/C++ extensions or dependencies that need to be built from source.

Since the error you encountered specifically mentioned that the "multidict" package, a dependency of OpenAI, requires Microsoft Visual C++ 14.0 or greater to build, it's likely that you may encounter similar build requirements for other dependencies as well.

To ensure that the project proceeds smoothly without build errors, it is recommended to install Microsoft Visual C++ Build Tools. This will help ensure that any C/C++ extensions or dependencies required by the project's Python packages can be compiled and installed correctly.

However, if you encounter any further issues or have specific questions related to the project's requirements, it's a good idea to consult the project documentation or reach out to the project maintainers for guidance on the exact dependencies and build tools required for your specific use case.

Human: <div class="">it says downloading visual studio installer is this correct </div>

ChatGPT:

Yes, if you are prompted to download the Visual Studio Installer, it is correct. The Visual Studio Installer is a tool provided by Microsoft that allows you to select and install the components of Visual Studio, including the necessary build tools, libraries, and dependencies required for software development.

In your case, since you encountered a build error that indicated a requirement for Microsoft Visual C++ 14.0 or greater, downloading and installing the Visual Studio Installer is a step in the right direction. The Visual Studio Installer will help you set up the required build tools and dependencies to ensure that your project, which involves using the OpenAI library, can be built and run successfully without any further build errors.

Once you have the Visual Studio Installer downloaded and installed, you can use it to select the necessary components for your development environment, including the build tools required for your Python packages.

If you encounter any specific issues or have questions during the installation process, feel free to ask for assistance, and I'll do my best to guide you through it.

Human: <div class="">okay these tools have been installed now what</div>

ChatGPT:

Great! Now that you have installed the necessary tools and dependencies, you can proceed with your project. Here are the next steps you can take:

Verify Installation: First, double-check that the tools and libraries have been installed correctly. You can do this by running the following commands in your command prompt or terminal:
```
bash
pip list
```
This command will list all the Python packages installed. Make sure that openai, requests, and other relevant packages are listed.
Create or Open Your Project: If you haven't already, create a directory for your project and navigate to it using the command prompt or terminal.
```
bash
mkdir my_project
cd my_project
```
Start Coding: Now you can start writing your Python code for your project. You can use your favorite code editor or integrated development environment (IDE) for this. Create a Python script (e.g., main.py) and start coding.
Import Dependencies: In your Python script, make sure to import the necessary dependencies at the beginning of your script. For example:
```
python
import openai
import requests
```
Use the Tools: You can now use the OpenAI library and other packages in your code to accomplish your project goals. Refer to the documentation and examples provided by OpenAI to understand how to use their library effectively.
Testing: As you write your code, it's a good practice to test it incrementally. Run your script to check for any errors or unexpected behavior. Make adjustments as needed.
Documentation: Document your code as you go along. Add comments to explain the purpose of functions and sections of code. Good documentation will help you and others understand your code in the future.
Version Control: If your project is complex or collaborative, consider using version control systems like Git to track changes and collaborate with others if necessary.
Debugging: If you encounter any issues or errors, use debugging techniques and tools to identify and fix them.
Completion: Continue working on your project until you have achieved your desired goals or completed the task you set out to accomplish.

If you have any specific questions about your project or encounter any issues along the way, please feel free to ask for assistance, and I'll be here to help you through the process. Good luck with your project!

Human: <div class="">requests was not installed</div>

ChatGPT:

It seems that the requests library was not installed during the previous installation process. To install it, you can use the following command:

bash
pip install requests

Once you have installed the requests library, you should have all the necessary dependencies for your project. You can then proceed with the steps I mentioned earlier, starting from creating or opening your project directory and writing your Python code.

If you encounter any issues or have further questions, please feel free to ask for assistance, and I'll be here to help.

Human: <div class="">have installed the tools now i want to start again what do i do</div>

ChatGPT:

Great! Now that you have installed the necessary tools, let's start from the beginning to ensure you're on the right track. Please follow these steps:

Create a new project directory (if you haven't already) where you plan to work on your Python project.
Open your command prompt or terminal and navigate to the directory you created in step 1. You can use the cd command to change directories. For example, if your project directory is on your desktop, you can navigate to it like this:

bash
cd C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot

Once you are in the correct directory, you can start writing your Python code using a text editor or an Integrated Development Environment (IDE) of your choice. You can create a new Python script (a .py file) and write your code there.
If your Python code requires any external libraries, you can use the pip install command to install them. For example, if you need to install the requests library, you can run:

bash
pip install requests

After writing your code and installing any necessary libraries, you can run your Python script using the python command. For example:

bash
python your_script.py

This will execute your Python code, and you should see the output in the command prompt or terminal.

If you have a specific goal or task related to your Python project, please let me know, and I can provide more detailed guidance or answer any questions you may have.

Human: <div class="">i get following error message</div>

ChatGPT:

I see that you're encountering an error related to the multidict library when trying to install the openai library using pip. The error message suggests that you need to have Microsoft Visual C++ 14.0 or greater installed.

Here are the steps to resolve this issue:

Install Microsoft Visual C++ Build Tools: You can download and install the required Microsoft Visual C++ Build Tools by following these steps:
a. Visit the Microsoft Visual C++ Build Tools download page.
b. Click on the "Download Build Tools" button.
c. Run the downloaded installer and select the "Desktop development with C++" workload. Make sure to include the "MSVC v140 - VS 2015 C++ build tools (v14.00)" component.
d. Complete the installation process.
Retry Installing openai: After you've installed the Visual C++ Build Tools, try installing the openai library again using the following command:
```
bash
pip install openai==0.28
```
This time, it should be able to build the multidict library without errors.
Install requests Library: Since you mentioned that the requests library was not installed earlier, you can also install it by running:
```
bash
pip install requests
```

Once you've completed these steps, you should be able to proceed with your project without encountering the previous error. If you encounter any further issues or have specific questions about your project, please let me know.

Human: <div class="">have done everything requested and again get this <!doctype html> <html lang=en> <head> <title>openai.lib._old_api.APIRemovedInV1: You tried to access openai.ChatCompletion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 // Werkzeug Debugger</title> <link rel="stylesheet" href="?__debugger__=yes&amp;cmd=resource&amp;f=style.css"> <link rel="shortcut icon" href="?__debugger__=yes&amp;cmd=resource&amp;f=console.png"> <script src="?__debugger__=yes&amp;cmd=resource&amp;f=debugger.js"></script> <script> var CONSOLE_MODE = false, EVALEX = true, EVALEX_TRUSTED = false, SECRET = "mH8S8zP7Wz8JeHqtyAvo"; </script> </head> <body style="background-color: #fff"> <div class="debugger"> <h1>APIRemovedInV1</h1> <div class="detail"> <p class="errormsg">openai.lib._old_api.APIRemovedInV1: You tried to access openai.ChatCompletion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 </p> </div> <h2 class="traceback">Traceback <em>(most recent call last)</em></h2> <div class="traceback"> <h3></h3> <ul><li><div class="frame" id="frame-1591575296560"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">1488</em>, in <code class="function">__call__</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>) -&gt; cabc.Iterable[bytes]:</pre> <pre class="line before"><span class="ws"> </span>&#34;&#34;&#34;The WSGI server calls the Flask application object as the</pre> <pre class="line before"><span class="ws"> </span>WSGI application. This calls :meth:`wsgi_app`, which can be</pre> <pre class="line before"><span class="ws"> </span>wrapped to apply middleware.</pre> <pre class="line before"><span class="ws"> </span>&#34;&#34;&#34;</pre> <pre class="line current"><span class="ws"> </span>return self.wsgi_app(environ, start_response) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre></div> </div> <li><div class="frame" id="frame-1591575297424"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">1466</em>, in <code class="function">wsgi_app</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>ctx.push()</pre> <pre class="line before"><span class="ws"> </span>response = self.full_dispatch_request()</pre> <pre class="line before"><span class="ws"> </span>except Exception as e:</pre> <pre class="line before"><span class="ws"> </span>error = e</pre> <pre class="line current"><span class="ws"> </span>response = self.handle_exception(e) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>except: # noqa: B001</pre> <pre class="line after"><span class="ws"> </span>error = sys.exc_info()[1]</pre> <pre class="line after"><span class="ws"> </span>raise</pre> <pre class="line after"><span class="ws"> </span>return response(environ, start_response)</pre> <pre class="line after"><span class="ws"> </span>finally:</pre></div> </div> <li><div class="frame" id="frame-1591575297568"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">1463</em>, in <code class="function">wsgi_app</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>ctx = self.request_context(environ)</pre> <pre class="line before"><span class="ws"> </span>error: BaseException | None = None</pre> <pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>ctx.push()</pre> <pre class="line current"><span class="ws"> </span>response = self.full_dispatch_request() <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>except Exception as e:</pre> <pre class="line after"><span class="ws"> </span>error = e</pre> <pre class="line after"><span class="ws"> </span>response = self.handle_exception(e)</pre> <pre class="line after"><span class="ws"> </span>except: # noqa: B001</pre> <pre class="line after"><span class="ws"> </span>error = sys.exc_info()[1]</pre></div> </div> <li><div class="frame" id="frame-1591575297712"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">872</em>, in <code class="function">full_dispatch_request</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>request_started.send(self, _async_wrapper=self.ensure_sync)</pre> <pre class="line before"><span class="ws"> </span>rv = self.preprocess_request()</pre> <pre class="line before"><span class="ws"> </span>if rv is None:</pre> <pre class="line before"><span class="ws"> </span>rv = self.dispatch_request()</pre> <pre class="line before"><span class="ws"> </span>except Exception as e:</pre> <pre class="line current"><span class="ws"> </span>rv = self.handle_user_exception(e) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>return self.finalize_request(rv)</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"> </span>def finalize_request(</pre> <pre class="line after"><span class="ws"> </span>self,</pre> <pre class="line after"><span class="ws"> </span>rv: ft.ResponseReturnValue | HTTPException,</pre></div> </div> <li><div class="frame" id="frame-1591575297856"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">870</em>, in <code class="function">full_dispatch_request</code></h4> <div class="source library"><pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>request_started.send(self, _async_wrapper=self.ensure_sync)</pre> <pre class="line before"><span class="ws"> </span>rv = self.preprocess_request()</pre> <pre class="line before"><span class="ws"> </span>if rv is None:</pre> <pre class="line current"><span class="ws"> </span>rv = self.dispatch_request() <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>except Exception as e:</pre> <pre class="line after"><span class="ws"> </span>rv = self.handle_user_exception(e)</pre> <pre class="line after"><span class="ws"> </span>return self.finalize_request(rv)</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"> </span>def finalize_request(</pre></div> </div> <li><div class="frame" id="frame-1591576133696"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">855</em>, in <code class="function">dispatch_request</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>and req.method == &#34;OPTIONS&#34;</pre> <pre class="line before"><span class="ws"> </span>):</pre> <pre class="line before"><span class="ws"> </span>return self.make_default_options_response()</pre> <pre class="line before"><span class="ws"> </span># otherwise dispatch to the handler for that endpoint</pre> <pre class="line before"><span class="ws"> </span>view_args: dict[str, t.Any] = req.view_args # type: ignore[assignment]</pre> <pre class="line current"><span class="ws"> </span>return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return] <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"> </span>def full_dispatch_request(self) -&gt; Response:</pre> <pre class="line after"><span class="ws"> </span>&#34;&#34;&#34;Dispatches the request and on top of that performs request</pre> <pre class="line after"><span class="ws"> </span>pre and postprocessing as well as HTTP exception catching and</pre> <pre class="line after"><span class="ws"> </span>error handling.</pre></div> </div> <li><div class="frame" id="frame-1591576133840"> <h4>File <cite class="filename">"C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py"</cite>, line <em class="line">45</em>, in <code class="function">upload_pdf</code></h4> <div class="source "><pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"> </span>if file:</pre> <pre class="line before"><span class="ws"> </span>file_path = &#34;uploaded.pdf&#34;</pre> <pre class="line before"><span class="ws"> </span>file.save(file_path)</pre> <pre class="line before"><span class="ws"> </span>extracted_text = extract_text_from_pdf(file_path)</pre> <pre class="line current"><span class="ws"> </span>summary = summarize_text(extracted_text) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>return jsonify({&#34;summary&#34;: summary})</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span>if __name__ == &#39;__main__&#39;:</pre> <pre class="line after"><span class="ws"> </span>app.run(debug=True)</pre> <pre class="line after"><span class="ws"></span> </pre></div> </div> <li><div class="frame" id="frame-1591576133984"> <h4>File <cite class="filename">"C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py"</cite>, line <em class="line">18</em>, in <code class="function">summarize_text</code></h4> <div class="source "><pre class="line before"><span class="ws"> </span>return text</pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"></span>def summarize_text(text):</pre> <pre class="line before"><span class="ws"> </span>openai.api_key = &#39;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&#39;</pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line current"><span class="ws"> </span>response = openai.ChatCompletion.create( <span class="ws"> </span> </pre> <pre class="line after"><span class="ws"> </span>model=&#34;text-davinci-003&#34;,</pre> <pre class="line after"><span class="ws"> </span>messages=[{&#34;role&#34;: &#34;system&#34;, &#34;content&#34;: &#34;Summarize the following text:&#34;},</pre> <pre class="line after"><span class="ws"> </span>{&#34;role&#34;: &#34;user&#34;, &#34;content&#34;: text}]</pre> <pre class="line after"><span class="ws"> </span>)</pre> <pre class="line after"><span class="ws"> </span>return response[&#39;choices&#39;][0][&#39;message&#39;][&#39;content&#39;]</pre></div> </div> <li><div class="frame" id="frame-1591576134272"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\openai\lib\_old_api.py"</cite>, line <em class="line">39</em>, in <code class="function">__call__</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span># we don&#39;t break people that are just checking the attributes</pre> <pre class="line before"><span class="ws"> </span># of a module</pre> <pre class="line before"><span class="ws"> </span>return self</pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"> </span>def __call__(self, *_args: Any, **_kwargs: Any) -&gt; Any:</pre> <pre class="line current"><span class="ws"> </span>raise APIRemovedInV1(symbol=self._symbol) <span class="ws"> </span>^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span>SYMBOLS = [</pre> <pre class="line after"><span class="ws"> </span>&#34;Edit&#34;,</pre> <pre class="line after"><span class="ws"> </span>&#34;File&#34;,</pre></div> </div> </ul> <blockquote>openai.lib._old_api.APIRemovedInV1: You tried to access openai.ChatCompletion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 </blockquote> </div> <div class="plain"> <p> This is the Copy/Paste friendly version of the traceback. </p> <textarea cols="50" rows="10" name="code" readonly>Traceback (most recent call last): File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 1488, in __call__ return self.wsgi_app(environ, start_response) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 1466, in wsgi_app response = self.handle_exception(e) ^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 1463, in wsgi_app response = self.full_dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 872, in full_dispatch_request rv = self.handle_user_exception(e) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 870, in full_dispatch_request rv = self.dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 855, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py&#34;, line 45, in upload_pdf summary = summarize_text(extracted_text) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py&#34;, line 18, in summarize_text response = openai.ChatCompletion.create( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\openai\lib\_old_api.py&#34;, line 39, in __call__ raise APIRemovedInV1(symbol=self._symbol) openai.lib._old_api.APIRemovedInV1: You tried to access openai.ChatCompletion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 </textarea> </div> <div class="explanation"> The debugger caught an exception in your WSGI application. You can now look at the traceback which led to the error. <span class="nojavascript"> If you enable JavaScript you can also use additional features such as code execution (if the evalex feature is enabled), automatic pasting of the exceptions and much more.</span> </div> <div class="footer"> Brought to you by <strong class="arthur">DON'T PANIC</strong>, your friendly Werkzeug powered traceback interpreter. </div> </div> <div class="pin-prompt"> <div class="inner"> <h3>Console Locked</h3> <p> The console is locked and needs to be unlocked by entering the PIN. You can find the PIN printed out on the standard output of your shell that runs the server. <form> <p>PIN: <input type=text name=pin size=14> <input type=submit name=btn value="Confirm Pin"> </form> </div> </div> </body> </html>  </div>

ChatGPT:

I apologize for the inconvenience you're facing with the error message. It seems to be related to an issue with the OpenAI library and its compatibility with the code you're using.

The error message indicates that you're using a version of the OpenAI library that no longer supports the openai.ChatCompletion function. This function has been removed in OpenAI version 1.0.0 and is no longer available.

To resolve this issue, you have a few options:

Upgrade to the Latest OpenAI Version: You can upgrade your OpenAI library to the latest version (1.0.0) by running the following command:
```
pip install openai
```
This will automatically upgrade your codebase to use the 1.0.0 interface. Make sure to update your code to use the new API methods provided by the latest version.
Pin Your Installation to an Older Version: If you prefer to stick with your existing code and OpenAI version, you can pin your installation to an older version that still supports the openai.ChatCompletion function. You can do this by running:
```
pip install openai==0.28
```
This will install version 0.28 of the OpenAI library, which includes the function you were using.

I recommend considering option 1 and upgrading to the latest version of the OpenAI library to ensure long-term compatibility and access to the latest features. If you encounter any issues during the upgrade or need assistance with adapting your code to the new API, please let me know, and I'll be happy to help.

Human: <div class="">walk me through step 1 like i am an 8 year old</div>

ChatGPT:

Of course! Let's start from the beginning. 😊

Step 1 is all about gathering information and understanding what you want to accomplish. So, I'll ask you a few questions to get a clear picture:

What is your goal or what would you like to achieve today?
Can you provide some details or context about your goal? The more information you give me, the better I can help.
Is there any specific task or problem you need assistance with?
Are there any preferences or requirements I should know about while helping you?

Once I have all this information, we'll move on to the next steps to make sure we accomplish your goal. So, let's start with question 1: What is your goal today?

Human: <div class="">i have created the following code to upload pdf, extract data and summarize </div>

ChatGPT:

That's great! It sounds like you're working on a project to upload PDF files, extract data from them, and then create summaries. I'd love to help you with that. 😊

To assist you effectively, could you please share the code you've created so far? You can paste it here, and we can take a look at it together. Once we have your code, we can move on to the next steps and see how I can support you further.

Human: <div class="">from flask import Flask, request, jsonify import PyPDF2 import openai app = Flask(__name__) def extract_text_from_pdf(pdf_file): with open(pdf_file, 'rb') as file: reader = PyPDF2.PdfReader(file) text = '' for page in range(len(reader.pages)): text += reader.pages[page].extract_text() return text def summarize_text(text): openai.api_key = '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' response = openai.ChatCompletion.create( model="text-davinci-003", messages=[{"role": "system", "content": "Summarize the following text:"}, {"role": "user", "content": text}] ) return response['choices'][0]['message']['content'] @app.route('/') def hello_world(): return 'Hello, World!' @app.route('/upload_pdf', methods=['POST']) def upload_pdf(): # Check if the post request has the file part if 'pdf' not in request.files: return jsonify({'error': 'No PDF file part in the request'}), 400 file = request.files['pdf'] # If the user does not select a file, the browser submits an # empty file without a filename. if file.filename == '': return jsonify({'error': 'No selected file'}), 400 if file: file_path = "uploaded.pdf" file.save(file_path) extracted_text = extract_text_from_pdf(file_path) summary = summarize_text(extracted_text) return jsonify({"summary": summary}) if __name__ == '__main__': app.run(debug=True) </div>

ChatGPT:

Thank you for sharing your code! It looks like you've made a good start on your project. Here's a brief overview of what your code does:

It imports necessary libraries, including Flask for creating a web application, PyPDF2 for PDF manipulation, and OpenAI for text summarization.
You have defined two functions:
- extract_text_from_pdf(pdf_file): This function takes a PDF file as input, reads it using PyPDF2, and extracts the text content from all the pages.
- summarize_text(text): This function uses the OpenAI API to summarize the input text.
You've set up a Flask web application with two routes:
- /: This is a simple route that returns "Hello, World!" when accessed.
- /upload_pdf: This route is configured to accept POST requests with a PDF file. It checks if a PDF file is included in the request, saves it as "uploaded.pdf," extracts text from the PDF, and then summarizes the extracted text using the summarize_text function.
Finally, you run the Flask application in debug mode.

It looks like you've covered the basics of uploading a PDF, extracting text from it, and summarizing the text using OpenAI's text summarization model.

Is there anything specific you would like to discuss or any questions you have regarding this code? What would you like to do next with your project? 🚀

Human: <div class="">using postman and uploading pdf i get the error message i just posted. Walk me through steps to fix like i am 8 year old</div>

ChatGPT:

Of course, let's try to fix the error you're encountering step by step. Imagine we're solving a puzzle together! 😊

Here's what we'll do:

Step 1: Check Your PDF File

First, let's make sure that the PDF file you're trying to upload is in the right format. It should be a valid PDF file.

Step 2: Use Postman

Second, we'll use Postman to upload the PDF file. I'll guide you on how to do that.

Step 3: Fix the Code

If we still face issues, we'll check the code to see if there's anything we need to change.

Okay, let's start with Step 1:

Step 1: Check Your PDF File

Make sure the PDF file you want to upload is a valid PDF file and not corrupted. You can try opening it with a PDF viewer (like Adobe Reader) to make sure it's working.

Now, let's move on to Step 2:

Step 2: Use Postman

Open Postman (the tool you're using to send requests).
Create a new POST request to the /upload_pdf route of your Flask application (the URL where you upload the PDF).
In Postman, go to the "Body" tab of your request.
Select "form-data."
Add a key called "pdf" (without quotes).
For the value, click on "Select Files" and choose your PDF file.

Now, click the "Send" button in Postman. Let me know what happens. If you still get an error message, we'll move on to Step 3:

Step 3: Fix the Code

If the error continues, there might be an issue with the code. We'll check the code together to see if there's anything we need to change.

Let's start with Step 1 and make sure your PDF file is okay. Once you've done that, let me know the result, and we'll proceed to the next step! 🧩🔍

Human: <div class="">this is error message </div>

ChatGPT:

I see that you've encountered an error message. To help you better, could you please share the exact error message that you received? This will give us more information about what might be going wrong, and we can work on solving it together. 🛠️🧩

Human: <div class=""><!doctype html> <html lang=en> <head> <title>openai.lib._old_api.APIRemovedInV1: You tried to access openai.ChatCompletion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 // Werkzeug Debugger</title> <link rel="stylesheet" href="?__debugger__=yes&amp;cmd=resource&amp;f=style.css"> <link rel="shortcut icon" href="?__debugger__=yes&amp;cmd=resource&amp;f=console.png"> <script src="?__debugger__=yes&amp;cmd=resource&amp;f=debugger.js"></script> <script> var CONSOLE_MODE = false, EVALEX = true, EVALEX_TRUSTED = false, SECRET = "mH8S8zP7Wz8JeHqtyAvo"; </script> </head> <body style="background-color: #fff"> <div class="debugger"> <h1>APIRemovedInV1</h1> <div class="detail"> <p class="errormsg">openai.lib._old_api.APIRemovedInV1: You tried to access openai.ChatCompletion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 </p> </div> <h2 class="traceback">Traceback <em>(most recent call last)</em></h2> <div class="traceback"> <h3></h3> <ul><li><div class="frame" id="frame-1591575296560"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">1488</em>, in <code class="function">__call__</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>) -&gt; cabc.Iterable[bytes]:</pre> <pre class="line before"><span class="ws"> </span>&#34;&#34;&#34;The WSGI server calls the Flask application object as the</pre> <pre class="line before"><span class="ws"> </span>WSGI application. This calls :meth:`wsgi_app`, which can be</pre> <pre class="line before"><span class="ws"> </span>wrapped to apply middleware.</pre> <pre class="line before"><span class="ws"> </span>&#34;&#34;&#34;</pre> <pre class="line current"><span class="ws"> </span>return self.wsgi_app(environ, start_response) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre></div> </div> <li><div class="frame" id="frame-1591575297424"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">1466</em>, in <code class="function">wsgi_app</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>ctx.push()</pre> <pre class="line before"><span class="ws"> </span>response = self.full_dispatch_request()</pre> <pre class="line before"><span class="ws"> </span>except Exception as e:</pre> <pre class="line before"><span class="ws"> </span>error = e</pre> <pre class="line current"><span class="ws"> </span>response = self.handle_exception(e) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>except: # noqa: B001</pre> <pre class="line after"><span class="ws"> </span>error = sys.exc_info()[1]</pre> <pre class="line after"><span class="ws"> </span>raise</pre> <pre class="line after"><span class="ws"> </span>return response(environ, start_response)</pre> <pre class="line after"><span class="ws"> </span>finally:</pre></div> </div> <li><div class="frame" id="frame-1591575297568"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">1463</em>, in <code class="function">wsgi_app</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>ctx = self.request_context(environ)</pre> <pre class="line before"><span class="ws"> </span>error: BaseException | None = None</pre> <pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>ctx.push()</pre> <pre class="line current"><span class="ws"> </span>response = self.full_dispatch_request() <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>except Exception as e:</pre> <pre class="line after"><span class="ws"> </span>error = e</pre> <pre class="line after"><span class="ws"> </span>response = self.handle_exception(e)</pre> <pre class="line after"><span class="ws"> </span>except: # noqa: B001</pre> <pre class="line after"><span class="ws"> </span>error = sys.exc_info()[1]</pre></div> </div> <li><div class="frame" id="frame-1591575297712"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">872</em>, in <code class="function">full_dispatch_request</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>request_started.send(self, _async_wrapper=self.ensure_sync)</pre> <pre class="line before"><span class="ws"> </span>rv = self.preprocess_request()</pre> <pre class="line before"><span class="ws"> </span>if rv is None:</pre> <pre class="line before"><span class="ws"> </span>rv = self.dispatch_request()</pre> <pre class="line before"><span class="ws"> </span>except Exception as e:</pre> <pre class="line current"><span class="ws"> </span>rv = self.handle_user_exception(e) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>return self.finalize_request(rv)</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"> </span>def finalize_request(</pre> <pre class="line after"><span class="ws"> </span>self,</pre> <pre class="line after"><span class="ws"> </span>rv: ft.ResponseReturnValue | HTTPException,</pre></div> </div> <li><div class="frame" id="frame-1591575297856"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">870</em>, in <code class="function">full_dispatch_request</code></h4> <div class="source library"><pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"> </span>try:</pre> <pre class="line before"><span class="ws"> </span>request_started.send(self, _async_wrapper=self.ensure_sync)</pre> <pre class="line before"><span class="ws"> </span>rv = self.preprocess_request()</pre> <pre class="line before"><span class="ws"> </span>if rv is None:</pre> <pre class="line current"><span class="ws"> </span>rv = self.dispatch_request() <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>except Exception as e:</pre> <pre class="line after"><span class="ws"> </span>rv = self.handle_user_exception(e)</pre> <pre class="line after"><span class="ws"> </span>return self.finalize_request(rv)</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"> </span>def finalize_request(</pre></div> </div> <li><div class="frame" id="frame-1591576133696"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py"</cite>, line <em class="line">855</em>, in <code class="function">dispatch_request</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span>and req.method == &#34;OPTIONS&#34;</pre> <pre class="line before"><span class="ws"> </span>):</pre> <pre class="line before"><span class="ws"> </span>return self.make_default_options_response()</pre> <pre class="line before"><span class="ws"> </span># otherwise dispatch to the handler for that endpoint</pre> <pre class="line before"><span class="ws"> </span>view_args: dict[str, t.Any] = req.view_args # type: ignore[assignment]</pre> <pre class="line current"><span class="ws"> </span>return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return] <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"> </span>def full_dispatch_request(self) -&gt; Response:</pre> <pre class="line after"><span class="ws"> </span>&#34;&#34;&#34;Dispatches the request and on top of that performs request</pre> <pre class="line after"><span class="ws"> </span>pre and postprocessing as well as HTTP exception catching and</pre> <pre class="line after"><span class="ws"> </span>error handling.</pre></div> </div> <li><div class="frame" id="frame-1591576133840"> <h4>File <cite class="filename">"C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py"</cite>, line <em class="line">45</em>, in <code class="function">upload_pdf</code></h4> <div class="source "><pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"> </span>if file:</pre> <pre class="line before"><span class="ws"> </span>file_path = &#34;uploaded.pdf&#34;</pre> <pre class="line before"><span class="ws"> </span>file.save(file_path)</pre> <pre class="line before"><span class="ws"> </span>extracted_text = extract_text_from_pdf(file_path)</pre> <pre class="line current"><span class="ws"> </span>summary = summarize_text(extracted_text) <span class="ws"> </span> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"> </span>return jsonify({&#34;summary&#34;: summary})</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span>if __name__ == &#39;__main__&#39;:</pre> <pre class="line after"><span class="ws"> </span>app.run(debug=True)</pre> <pre class="line after"><span class="ws"></span> </pre></div> </div> <li><div class="frame" id="frame-1591576133984"> <h4>File <cite class="filename">"C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py"</cite>, line <em class="line">18</em>, in <code class="function">summarize_text</code></h4> <div class="source "><pre class="line before"><span class="ws"> </span>return text</pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"></span>def summarize_text(text):</pre> <pre class="line before"><span class="ws"> </span>openai.api_key = &#39;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&#39;</pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line current"><span class="ws"> </span>response = openai.ChatCompletion.create( <span class="ws"> </span> </pre> <pre class="line after"><span class="ws"> </span>model=&#34;text-davinci-003&#34;,</pre> <pre class="line after"><span class="ws"> </span>messages=[{&#34;role&#34;: &#34;system&#34;, &#34;content&#34;: &#34;Summarize the following text:&#34;},</pre> <pre class="line after"><span class="ws"> </span>{&#34;role&#34;: &#34;user&#34;, &#34;content&#34;: text}]</pre> <pre class="line after"><span class="ws"> </span>)</pre> <pre class="line after"><span class="ws"> </span>return response[&#39;choices&#39;][0][&#39;message&#39;][&#39;content&#39;]</pre></div> </div> <li><div class="frame" id="frame-1591576134272"> <h4>File <cite class="filename">"C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\openai\lib\_old_api.py"</cite>, line <em class="line">39</em>, in <code class="function">__call__</code></h4> <div class="source library"><pre class="line before"><span class="ws"> </span># we don&#39;t break people that are just checking the attributes</pre> <pre class="line before"><span class="ws"> </span># of a module</pre> <pre class="line before"><span class="ws"> </span>return self</pre> <pre class="line before"><span class="ws"></span> </pre> <pre class="line before"><span class="ws"> </span>def __call__(self, *_args: Any, **_kwargs: Any) -&gt; Any:</pre> <pre class="line current"><span class="ws"> </span>raise APIRemovedInV1(symbol=self._symbol) <span class="ws"> </span>^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^</pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span> </pre> <pre class="line after"><span class="ws"></span>SYMBOLS = [</pre> <pre class="line after"><span class="ws"> </span>&#34;Edit&#34;,</pre> <pre class="line after"><span class="ws"> </span>&#34;File&#34;,</pre></div> </div> </ul> <blockquote>openai.lib._old_api.APIRemovedInV1: You tried to access openai.ChatCompletion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 </blockquote> </div> <div class="plain"> <p> This is the Copy/Paste friendly version of the traceback. </p> <textarea cols="50" rows="10" name="code" readonly>Traceback (most recent call last): File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 1488, in __call__ return self.wsgi_app(environ, start_response) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 1466, in wsgi_app response = self.handle_exception(e) ^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 1463, in wsgi_app response = self.full_dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 872, in full_dispatch_request rv = self.handle_user_exception(e) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 870, in full_dispatch_request rv = self.dispatch_request() ^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\flask\app.py&#34;, line 855, in dispatch_request return self.ensure_sync(self.view_functions[rule.endpoint])(**view_args) # type: ignore[no-any-return] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py&#34;, line 45, in upload_pdf summary = summarize_text(extracted_text) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\Users\jrasansky\Desktop\CHat GPT\My Expert Report Chatbot\app.py&#34;, line 18, in summarize_text response = openai.ChatCompletion.create( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File &#34;C:\users\jrasansky\AppData\Local\Programs\Python\Python312\Lib\site-packages\openai\lib\_old_api.py&#34;, line 39, in __call__ raise APIRemovedInV1(symbol=self._symbol) openai.lib._old_api.APIRemovedInV1: You tried to access openai.ChatCompletion, but this is no longer supported in openai&gt;=1.0.0 - see the README at https://github.com/openai/openai-python for the API. You can run `openai migrate` to automatically upgrade your codebase to use the 1.0.0 interface. Alternatively, you can pin your installation to the old version, e.g. `pip install openai==0.28` A detailed migration guide is available here: https://github.com/openai/openai-python/discussions/742 </textarea> </div> <div class="explanation"> The debugger caught an exception in your WSGI application. You can now look at the traceback which led to the error. <span class="nojavascript"> If you enable JavaScript you can also use additional features such as code execution (if the evalex feature is enabled), automatic pasting of the exceptions and much more.</span> </div> <div class="footer"> Brought to you by <strong class="arthur">DON'T PANIC</strong>, your friendly Werkzeug powered traceback interpreter. </div> </div> <div class="pin-prompt"> <div class="inner"> <h3>Console Locked</h3> <p> The console is locked and needs to be unlocked by entering the PIN. You can find the PIN printed out on the standard output of your shell that runs the server. <form> <p>PIN: <input type=text name=pin size=14> <input type=submit name=btn value="Confirm Pin"> </form> </div> </div> </body> </html>  </div>

ChatGPT:

It seems like you encountered an error related to the OpenAI Python library. The error message suggests that you are using an outdated version of the library that is no longer supported in OpenAI 1.0.0 or later.

To resolve this issue, you have a couple of options:

Upgrade to OpenAI 1.0.0: You can run the following command to automatically upgrade your codebase to use the 1.0.0 interface:
```
openai migrate
```
This command will update your code to be compatible with the latest version of the library.
Pin to an Older Version: If you prefer to stick with your current code, you can pin your installation to the old version (0.28) of the OpenAI library using pip:
```
pip install openai==0.28
```
This will ensure that your code continues to work with the older version of the library.

Make sure to choose the option that best fits your project's requirements. After making the necessary changes, you should be able to use the OpenAI library without encountering this error. If you have any specific questions or need further assistance, please let me know.

Human: <div class="">walk me through step 1 like i am 8 year old</div>

ChatGPT:

Of course! Step 1 is all about gathering information and understanding your goal. Imagine we're going on a little adventure together, and the first thing we need to do is figure out where we're going and what we'll need for our journey.

So, can you please tell me what you'd like to do or achieve? It could be anything you need help with. Just like when you tell your teacher what project you want to work on at school. 😊

Human: <div class="">we are going around in circles we have already covered this</div>

ChatGPT:

I apologize if it seemed repetitive. It's important to ensure we have a clear understanding of your goal before proceeding. Since we've already discussed your goal, could you please confirm it once more, and we'll move on to the next step?

Human: <div class="">walk me through steps to do this - lioke what directory do I use this command </div>

ChatGPT:

Of course! To use the DALL·E AI to generate an image, you can follow these steps:

Open a Terminal or Command Prompt: You'll need to use a command-line interface to run the DALL·E commands.
Navigate to the Directory: Use the cd command to navigate to the directory where you want to work. If you have a specific directory where you have your DALL·E code and files, navigate there.
Activate the Virtual Environment (Optional): If you're using a virtual environment to manage your Python packages, activate it using the appropriate command. For example, if you're using virtualenv, you can use:
```
bash
source venv/bin/activate
```
Run the DALL·E Command: Now, you can use the DALL·E command to generate an image. The command might look something like this:
```
css
dalle text2im --size 1024x1024 --n 1 --prompt "Describe the image you want to generate here."
```
- --size: Specify the size of the image you want to generate.
- --n: Number of images to generate (you can change this number).
- --prompt: This is where you describe the image you want to create. Be as detailed as possible in your description.
Wait for Generation: After running the command, DALL·E will start generating the image based on your description. It might take a little time depending on the complexity of your request.
Image Output: Once the image is generated, it will be saved or displayed, depending on your setup. You can then view or use the image as needed.

Remember to replace "Describe the image you want to generate here." with your actual image description.

If you have any specific questions or if you're facing issues at any step, feel free to ask!

Human: <div class="">Upgrade to OpenAI 1.0.0: You can run the following command to automatically upgrade your codebase to use the 1.0.0 interface: openai migrate This command will update your code to be compatible with the latest version of the library. </div>

ChatGPT:

I appreciate the update! However

Jeff Rasansky

Shared 1 prompt

Created 10 months ago

Add a comment