Create Ahmed.py

Ahmed.py

@@ -0,0 +1,55 @@
+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)