Update app.py

app.py

@@ -96,16 +96,16 @@ def create_visualizations(df):
         
     return figures[0] if figures else None
 
-def respond(
+def process_message(
     message,
-    history: list[tuple[str, str]],
+    chat_history,
     system_message,
     max_tokens,
     temperature,
     top_p,
-    chat_history_text=""
+    chat_history_text
 ):
-    """Enhanced response function with data analysis capabilities."""
+    """Process message and return response with analysis."""
     try:
         # Process chat history if provided
         if chat_history_text:
@@ -123,53 +123,53 @@ def respond(
                 for field, count in top_fields.items():
                     summary += f"  • {field}: {count} researchers\n"
             
+            # Create visualization
+            fig = create_visualizations(df)
+            
             # Add analysis to message
             message += f"\n\nCommunity Analysis:\n{summary}"
-        
+        else:
+            fig = None
+
         # Generate response using the LLM
         messages = [{"role": "system", "content": system_message}]
-        for val in history:
-            if val[0]:
-                messages.append({"role": "user", "content": val[0]})
-            if val[1]:
-                messages.append({"role": "assistant", "content": val[1]})
-        
+        for user_msg, bot_msg in chat_history:
+            messages.append({"role": "user", "content": user_msg})
+            messages.append({"role": "assistant", "content": bot_msg})
         messages.append({"role": "user", "content": message})
-        
-        response = ""
-        for token in client.chat_completion(
+
+        response = client.chat_completion(
             messages,
             max_tokens=max_tokens,
-            stream=True,
             temperature=temperature,
             top_p=top_p,
-        ):
-            token_content = token.choices[0].delta.content
-            response += token_content
-            yield response
-            
+        )
+        
+        bot_message = response.choices[0].message.content
+        chat_history.append((message, bot_message))
+        
+        return chat_history, fig, chat_history
+
     except Exception as e:
-        yield f"Error: {str(e)}"
+        error_message = f"Error: {str(e)}"
+        chat_history.append((message, error_message))
+        return chat_history, None, chat_history
 
-# Create Gradio interface
-demo = gr.Interface(
-    fn=respond,
-    inputs=[
-        gr.Textbox(label="Message"),
-        gr.State([]),  # history
-        gr.Textbox(value="You are a friendly Research Community Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
-        gr.Textbox(label="Chat History", lines=10)
-    ],
-    outputs=[
-        gr.Textbox(label="Response"),
-        gr.Plot(label="Community Analysis")
-    ],
-    title="CohortBot",
-    description="A chatbot that analyzes research community data and provides visualizations."
-)
+with gr.Blocks(title="CohortBot") as demo:
+    chatbot = gr.Chatbot(label="Chat History")
+    msg = gr.Textbox(label="Message", placeholder="Type your message here...")
+    system_msg = gr.Textbox(value="You are a friendly Research Community Chatbot.", label="System message")
+    max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
+    temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+    top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p")
+    chat_history_text = gr.Textbox(label="Chat History for Analysis", lines=10)
+    plot = gr.Plot(label="Community Analysis")
+    
+    msg.submit(
+        process_message,
+        [msg, chatbot, system_msg, max_tokens, temperature, top_p, chat_history_text],
+        [chatbot, plot, chatbot]
+    )
 
 if __name__ == "__main__":
     demo.launch()