Update app.py

app.py

@@ -241,6 +241,57 @@
 # 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
+
+# # 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
+# memory = ConversationBufferMemory(memory_key="chat_history")
+
+# # 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 = history + [f"User: {user_message}"]
+#     response = llm_chain.predict(user_message=user_message, chat_history=chat_history)
+#     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
@@ -250,8 +301,8 @@ subprocess.check_call(["pip", "install", "-U", "langchain-openai", "gradio", "la
 
 from langchain_openai import ChatOpenAI
 from langchain.prompts import PromptTemplate
-from langchain.chains import LLMChain
 from langchain.memory import ConversationBufferMemory
+from langchain.chains import Runnable, RunnableSequence
 
 # Set OpenAI API Key
 OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
@@ -271,23 +322,23 @@ prompt = PromptTemplate(
 # Initialize conversation memory
 memory = ConversationBufferMemory(memory_key="chat_history")
 
-# 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,
-)
+# Define the runnable sequence
+chatbot_runnable = RunnableSequence(prompt | ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo"))
 
 # Function to get chatbot response
-def get_text_response(user_message, history):
+def get_text_response(user_message, history=None):
+    # Ensure history is a list
+    if history is None:
+        history = []
+
     # Prepare the conversation history
     chat_history = history + [f"User: {user_message}"]
-    response = llm_chain.predict(user_message=user_message, chat_history=chat_history)
+    response = chatbot_runnable.invoke({"chat_history": "\n".join(chat_history), "user_message": user_message})
+    
     return response
 
 # Create a Gradio chat interface
-demo = gr.Interface(fn=get_text_response, inputs="text", outputs="text")
+demo = gr.Interface(fn=get_text_response, inputs=["text", "state"], outputs="text")
 
 if __name__ == "__main__":
     demo.launch()
@@ -296,3 +347,4 @@ if __name__ == "__main__":
 
 
 
+