Update app.py

app.py

@@ -145,13 +145,61 @@
 # 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.chains import 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 RunnableSequence instead of LLMChain
+# llm_sequence = prompt | llm  # This pipelines the prompt into the language model
+
+# # 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_sequence.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 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.chains import RunnableSequence
+from langchain import Runnable  # Using Runnable instead of RunnableSequence
 
 # Set OpenAI API Key
 OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
@@ -174,8 +222,8 @@ memory = ConversationBufferMemory(return_messages=True)  # Use return_messages=T
 # Define the LLM (language model)
 llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
 
-# Create the RunnableSequence instead of LLMChain
-llm_sequence = prompt | llm  # This pipelines the prompt into the language model
+# 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):
@@ -183,7 +231,7 @@ def get_text_response(user_message, history):
     chat_history = [HumanMessage(content=user_message)]
     
     # Pass the prompt and history to the language model sequence
-    response = llm_sequence.invoke({"chat_history": history, "user_message": user_message})
+    response = llm_runnable.invoke({"chat_history": history, "user_message": user_message})
     
     return response
 
@@ -195,3 +243,4 @@ if __name__ == "__main__":
 
 
 
+