Update app.py

app.py

@@ -1,23 +1,24 @@
 from transformers import pipeline
+import gradio as gr
 
 # Load the pipeline for text generation
-generator = pipeline("text-generation", model="microsoft/DialoGPT-small")
+try:
+    generator = pipeline("text-generation", model="microsoft/DialoGPT-small")
+except Exception as e:
+    print(f"Error loading the model: {e}")
+    raise
 
 # Function to generate a response
 def dialoGPT_response(user_input, history):
-    # Since the pipeline handles everything, we just need to format our input
-    conversation = [{"role": "user", "content": user_input}] if history is None else history + [{"role": "user", "content": user_input}]
-    
-    # Generate response using the pipeline, which manages all pre/post-processing
-    response = generator(conversation, return_full_text=False, max_length=1000)
-
-    # Extract the last assistant response
-    assistant_response = response[0]['generated_text']
-    
-    # Append this response to history
-    new_history = conversation + [{"role": "assistant", "content": assistant_response}]
-    
-    return assistant_response, new_history
+    try:
+        conversation = [{"role": "user", "content": user_input}] if history is None else history + [{"role": "user", "content": user_input}]
+        response = generator(conversation, return_full_text=False, max_length=1000)
+        assistant_response = response[0]['generated_text']
+        new_history = conversation + [{"role": "assistant", "content": assistant_response}]
+        return assistant_response, new_history
+    except Exception as e:
+        print(f"Error generating response: {e}")
+        return "An error occurred while generating a response.", history
 
 # Gradio interface
 iface = gr.Interface(
@@ -27,12 +28,13 @@ iface = gr.Interface(
         "state"
     ],
     outputs=[
-        "text",  # The response
-        "state"  # Updated history
+        "text",  
+        "state"  
     ],
     title="DialoGPT Chat",
     description="Chat with DialoGPT-small model. Your conversation history is maintained.",
     allow_flagging="never"
 )
 
-iface.launch()
+if __name__ == "__main__":
+    iface.launch(debug=True)