Update app.py

app.py

@@ -444,13 +444,67 @@
 # if __name__ == "__main__":
 #     demo.launch()
 
-import os
-import subprocess
-import gradio as gr
+# import os
+# import subprocess
+# import gradio as gr
+
+# # Install necessary packages
+# subprocess.check_call(["pip", "install", "-U", "langchain-openai", "gradio", "langchain-community"])
 
-# 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.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")
+
+# # Function to get chatbot response
+# 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}"]
+    
+#     # Create the full prompt string
+#     full_prompt = prompt.format(chat_history="\n".join(chat_history), user_message=user_message)
+
+#     llm = ChatOpenAI(temperature=0.5, model="gpt-3.5-turbo")
+    
+#     # Use the invoke method instead of __call__
+#     response = llm.invoke(full_prompt)
+
+#     # Return the response and updated history
+#     return response['choices'][0]['message']['content'], chat_history
+
+# # Create a Gradio chat interface
+# demo = gr.Interface(
+#     fn=get_text_response, 
+#     inputs=["text", "state"], 
+#     outputs=["text", "state"],
+# )
+
+# 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
@@ -493,13 +547,13 @@ def get_text_response(user_message, history=None):
     # Return the response and updated history
     return response['choices'][0]['message']['content'], chat_history
 
-# Create a Gradio chat interface
-demo = gr.Interface(
-    fn=get_text_response, 
-    inputs=["text", "state"], 
-    outputs=["text", "state"],
-)
-
+# Create a Gradio chat interface using ChatInterface
+with gr.Blocks() as demo:
+    chatbot = gr.ChatInterface(
+        get_text_response,  # Function to get responses
+        memory=ConversationBufferMemory()  # Gradio stateful chat memory
+    )
+    
 if __name__ == "__main__":
     demo.launch()
 
@@ -511,3 +565,4 @@ if __name__ == "__main__":
 
 
 
+