Prompt Title: Code analysis for bot.

Human: You are a legendary butlazy and opportunistic data scientict/programmer. I have this bot and i need your help in analysing itand building on it. analyse it and put the aa flow structure in a tabuar format. This program is 100% fuctional so consider alll the code correct. After you analsye teh code ill ask you a few questions. Ill send you 3 files. main.py: # This wrapper automatically loads the API import sys, select, time, os, subprocess, sys if "API_SECRET" not in os.environ: print("You must set an API_SECRET using the Secrets tool", file=sys.stderr) elif "OPENAI_API_KEY" not in os.environ: print("You must set an OPENAI_API_KEY using the Secrets tool", file=sys.stderr) else: print("== OPENAI + REPLIT CUSTOM BOT==") print ("You have five seconds to select an option") print() print("1: Train Model\n2: Talk to your Bot\n3: Run API server (Default)\n> ", end="") i, o, e = select.select( [sys.stdin], [], [], 10 ) print() if (i): choice = sys.stdin.readline().strip() time.sleep(0.5) os.system('clear') if choice == "1": print("BOT TRAINING MODE") import process process.train() elif choice == "2": print("BOT CONVERSATION MODE") import process process.runPrompt() else: print("Booting into API Server…") time.sleep(1) os.system('clear') print("BOT API SERVER RUNNING") p = subprocess.Popen([sys.executable, 'server.py'], stdout=subprocess.PIPE, stderr=subprocess.STDOUT) while True: line = p.stdout.readline() if not line: break else: time.sleep(0.5) os.system('clear') print("Booting into API Server…") time.sleep(1) os.system('clear') print("BOT API SERVER RUNNING") p = subprocess.Popen([sys.executable, 'server.py'], stdout=subprocess.PIPE, stderr=subprocess.STDOUT) while True: line = p.stdout.readline() if not line: break server.py from pathlib import Path from langchain.text_splitter import CharacterTextSplitter import faiss from langchain.vectorstores import FAISS from langchain.embeddings import OpenAIEmbeddings import pickle from langchain import OpenAI, LLMChain from langchain.prompts import Prompt from flask import Flask, request, jsonify import os index = faiss.read_index("training.index") with open("faiss.pkl", "rb") as f: store = pickle.load(f) store.index = index with open("training/master.txt", "r") as f: promptTemplate = f.read() prompt = Prompt(template=promptTemplate, input_variables=["history", "context", "question"]) app = Flask(__name__) @app.route("/", methods=["GET"]) def index(): return "API Online" @app.route("/", methods=["POST"]) def ask(): reqData = request.get_json() if reqData['secret'] == os.environ["API_SECRET"]: try: llmChain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0, model_name="text-davinci-003", openai_api_key=os.environ["OPENAI_API_KEY"])) def onMessage(question, history): docs = store.similarity_search(question) contexts = [] for i, doc in enumerate(docs): contexts.append(f"Context {i}:\n{doc.page_content}") answer = llmChain.predict(question=question, context="\n\n".join(contexts), history=history) return answer return jsonify({ "answer": onMessage(reqData['question'], reqData['history']), "success": True }) except: return jsonify({ "answer": None, "success": False, "message": "Error" }), 400 else: return jsonify({ "answer": None, "success": False, "message": "Unauthorised" }) app.run(host="0.0.0.0", port=3000) process.py: from pathlib import Path from langchain.text_splitter import CharacterTextSplitter import faiss from langchain.vectorstores import FAISS from langchain.embeddings import OpenAIEmbeddings import pickle from langchain import OpenAI, LLMChain from langchain.prompts import Prompt def train(): trainingData = list(Path("training/facts/").glob("**/*.*")) #check there is data in the trainingData folder if len(trainingData) < 1: print("The folder training/facts should be populated with at least one .txt or .md file.", file=sys.stderr) return data = [] for training in trainingData: with open(training) as f: print(f"Add {f.name} to dataset") data.append(f.read()) textSplitter = CharacterTextSplitter(chunk_size=2000, separator="\n") docs = [] for sets in data: docs.extend(textSplitter.split_text(sets)) store = FAISS.from_texts(docs, OpenAIEmbeddings()) faiss.write_index(store.index, "training.index") store.index = None with open("faiss.pkl", "wb") as f: pickle.dump(store, f) def runPrompt(): index = faiss.read_index("training.index") with open("faiss.pkl", "rb") as f: store = pickle.load(f) store.index = index with open("training/master.txt", "r") as f: promptTemplate = f.read() prompt = Prompt(template=promptTemplate, input_variables=["history", "context", "question"]) llmChain = LLMChain(prompt=prompt, llm=OpenAI(temperature=0.25)) def onMessage(question, history): docs = store.similarity_search(question) contexts = [] for i, doc in enumerate(docs): contexts.append(f"Context {i}:\n{doc.page_content}") answer = llmChain.predict(question=question, context="\n\n".join(contexts), history=history) return answer history = [] while True: question = input("Ask a question > ") answer = onMessage(question, history) print(f"Bot: {answer}") history.append(f"Human: {question}") history.append(f"Bot: {answer}")

ChatGPT:

Here is the flow structure in tabular format:

File	Function/Flow	Description
main.py	Main menu	Presents options to the user: 1. Train Model, 2. Talk to Bot, 3. Run API server
main.py	Train Model	Imports and runs process.train()
main.py	Talk to Bot	Imports and runs process.runPrompt()
main.py	Run API server	Runs the server.py file
server.py	Index route	Returns "API Online"
server.py	Ask route	Validates secret, predicts answer based on question and history, and returns the response as JSON
process.py	train() function	Trains the model using training data from the training/facts folder and saves the index and store as files
process.py	runPrompt() function	Reads the index and store files, sets up the LLMChain object, and runs an interactive loop where the user can ask questions and the bot will provide answers using the model

Human: analyse with different diagrams about entity relationshiip, sequence and state transition

ChatGPT:

Entity Relationship Diagram:

Entities:

1. User
2. Main Menu
3. Training Model
4. Bot Conversation
5. API Server
6. Training Data
7. LLMChain

Relationships:

1. User interacts with Main Menu
2. Main Menu initiates Training Model
3. Main Menu initiates Bot Conversation
4. Main Menu initiates API Server
5. Training Model processes Training Data
6. Bot Conversation and API Server use LLMChain for predictions

Sequence Diagram:

1. User selects an option from Main Menu 1.1. Train Model 1.1.1. Read and process Training Data 1.1.2. Train LLMChain and save index and store files 1.2. Talk to Bot 1.2.1. Load LLMChain 1.2.2. Start interactive loop 1.2.2.1. User asks a question 1.2.2.2. Bot provides an answer 1.3. Run API Server 1.3.1. Start Flask server 1.3.2. Process incoming requests 1.3.2.1. Validate secret 1.3.2.2. Generate a response using LLMChain

State Transition Diagram:

States:

1. Main Menu
2. Train Model
3. Bot Conversation
4. API Server

Transitions:

1. Main Menu -> Train Model
2. Main Menu -> Bot Conversation
3. Main Menu -> API Server