Update app.py

app.py

@@ -1,7 +1,7 @@
 import os
 import gradio as gr
 from langchain_openai import ChatOpenAI
-from langchain_core.messages import HumanMessage
+from langchain_core.messages import HumanMessage, AIMessage
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import START, MessagesState, StateGraph
 
@@ -11,7 +11,7 @@ def create_chat_app(api_key):
         model="gpt-4o-mini",
         api_key=api_key,
         temperature=0
-    )[1]
+    )
 
     # Define the graph
     workflow = StateGraph(state_schema=MessagesState)
@@ -19,38 +19,53 @@ def create_chat_app(api_key):
     # Define the function that calls the model
     def call_model(state: MessagesState):
         response = llm.invoke(state["messages"])
-        return {"messages": response}[1]
+        return {"messages": response}
 
     # Add node and edge to graph
     workflow.add_edge(START, "model")
-    workflow.add_node("model", call_model)[1]
+    workflow.add_node("model", call_model)
 
     # Add memory
     memory = MemorySaver()
-    return workflow.compile(checkpointer=memory)[1]
+    return workflow.compile(checkpointer=memory)
 
 def chat(message, history, api_key, thread_id):
     if not api_key:
-        return "Please enter your OpenAI API key first."
+        return "", [{"role": "assistant", "content": "Please enter your OpenAI API key first."}]
     
     try:
-        # Create chat application if not exists
+        # Create chat application
         app = create_chat_app(api_key)
         
         # Configure thread
-        config = {"configurable": {"thread_id": thread_id}}[1]
+        config = {"configurable": {"thread_id": thread_id}}
         
-        # Prepare input message
-        input_messages = [HumanMessage(message)]
+        # Convert history to messages format
+        messages = []
+        for msg in history:
+            if msg["role"] == "user":
+                messages.append(HumanMessage(content=msg["content"]))
+            elif msg["role"] == "assistant":
+                messages.append(AIMessage(content=msg["content"]))
+        
+        # Add current message
+        messages.append(HumanMessage(content=message))
         
         # Get response
-        output = app.invoke({"messages": input_messages}, config)[1]
+        output = app.invoke({"messages": messages}, config)
+        response = output["messages"][-1].content
+        
+        # Update history
+        history.append({"role": "user", "content": message})
+        history.append({"role": "assistant", "content": response})
         
-        return output["messages"][-1].content
+        return "", history
     except Exception as e:
-        return f"Error: {str(e)}"
+        error_message = f"Error: {str(e)}"
+        history.append({"role": "user", "content": message})
+        history.append({"role": "assistant", "content": error_message})
+        return "", history
 
-# Create Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# LangChain Chat with Message History")
     
@@ -66,10 +81,15 @@ with gr.Blocks() as demo:
             placeholder="Enter a unique thread ID"
         )
     
-    chatbot = gr.ChatInterface(
-        fn=lambda message, history: chat(message, history, api_key.value, thread_id.value),
-        title="Chat with GPT-4o-mini"
-    )[2]
+    chatbot = gr.Chatbot(type="messages")
+    msg = gr.Textbox(label="Message", placeholder="Type your message here...")
+    clear = gr.ClearButton([msg, chatbot])
+
+    msg.submit(
+        chat,
+        inputs=[msg, chatbot, api_key, thread_id],
+        outputs=[msg, chatbot]
+    )
 
-# Launch the application
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()