Delete Ahmed.py

Ahmed.py

@@ -1,55 +0,0 @@
-from flask import Flask, render_template, request
-from flask_sqlalchemy import SQLAlchemy
-import os
-import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
-
-app = Flask(__name__)
-app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///chat_history.db'
-db = SQLAlchemy(app)
-
-class ChatHistory(db.Model):
-    id = db.Column(db.Integer, primary_key=True)
-    user_message = db.Column(db.String(255))
-    chatbot_response = db.Column(db.String(255))
-
-OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
-
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
-
-memory = ConversationBufferMemory(memory_key="chat_history")
-
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def save_to_database(user_message, chatbot_response):
-    entry = ChatHistory(user_message=user_message, chatbot_response=chatbot_response)
-    db.session.add(entry)
-    db.session.commit()
-
-def get_text_response(user_message, history):
-    response = llm_chain.predict(user_message=user_message)
-    save_to_database(user_message, response)
-    return response
-
-@app.route('/')
-def index():
-    chat_history_entries = ChatHistory.query.all()
-    return render_template('index.html', chat_history_entries=chat_history_entries)
-
-if __name__ == "__main__":
-    db.create_all()
-    gr.Interface(fn=get_text_response, live=True).launch(share=True)