Update app.py

app.py

@@ -193,13 +193,65 @@
 # if __name__ == "__main__":
 #     demo.launch()
 
+# import os
+# import gradio as gr
+# from langchain_openai import ChatOpenAI
+# from langchain.prompts import PromptTemplate
+# from langchain.memory import ConversationBufferMemory
+# from langchain.schema import AIMessage, HumanMessage
+# from langchain import Runnable  # Using Runnable instead of RunnableSequence
+
+# # Set OpenAI API Key
+# OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
+
+# # Define the template for the chatbot's response
+# template = """You are a helpful assistant to answer all user queries.
+# {chat_history}
+# User: {user_message}
+# Chatbot:"""
+
+# # Define the prompt template
+# prompt = PromptTemplate(
+#     input_variables=["chat_history", "user_message"], 
+#     template=template
+# )
+
+# # Initialize conversation memory (following migration guide)
+# memory = ConversationBufferMemory(return_messages=True)  # Use return_messages=True for updated usage
+
+# # Define the LLM (language model)
+# llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
+
+# # Create the Runnable instead of RunnableSequence
+# llm_runnable = Runnable(lambda inputs: prompt.format(**inputs)) | llm
+
+# # Function to get chatbot response
+# def get_text_response(user_message, history):
+#     # Prepare the conversation history
+#     chat_history = [HumanMessage(content=user_message)]
+    
+#     # Pass the prompt and history to the language model sequence
+#     response = llm_runnable.invoke({"chat_history": history, "user_message": user_message})
+    
+#     return response
+
+# # Create a Gradio chat interface
+# demo = gr.Interface(fn=get_text_response, inputs="text", outputs="text")
+
+# if __name__ == "__main__":
+#     demo.launch()
+
 import os
+import subprocess
 import gradio as gr
+
+# Install necessary packages
+subprocess.check_call(["pip", "install", "-U", "langchain-openai", "gradio", "langchain-community"])
+
 from langchain_openai import ChatOpenAI
 from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
 from langchain.memory import ConversationBufferMemory
-from langchain.schema import AIMessage, HumanMessage
-from langchain import Runnable  # Using Runnable instead of RunnableSequence
 
 # Set OpenAI API Key
 OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
@@ -216,23 +268,22 @@ prompt = PromptTemplate(
     template=template
 )
 
-# Initialize conversation memory (following migration guide)
-memory = ConversationBufferMemory(return_messages=True)  # Use return_messages=True for updated usage
-
-# Define the LLM (language model)
-llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
+# Initialize conversation memory
+memory = ConversationBufferMemory(memory_key="chat_history")
 
-# Create the Runnable instead of RunnableSequence
-llm_runnable = Runnable(lambda inputs: prompt.format(**inputs)) | llm
+# Define the LLM chain with the ChatOpenAI model and conversation memory
+llm_chain = LLMChain(
+    llm=ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo"),  # Use 'model' instead of 'model_name'
+    prompt=prompt,
+    verbose=True,
+    memory=memory,
+)
 
 # Function to get chatbot response
 def get_text_response(user_message, history):
     # Prepare the conversation history
-    chat_history = [HumanMessage(content=user_message)]
-    
-    # Pass the prompt and history to the language model sequence
-    response = llm_runnable.invoke({"chat_history": history, "user_message": user_message})
-    
+    chat_history = history + [f"User: {user_message}"]
+    response = llm_chain.predict(user_message=user_message, chat_history=chat_history)
     return response
 
 # Create a Gradio chat interface
@@ -244,3 +295,4 @@ if __name__ == "__main__":
 
 
 
+