Fixed some errors

app.py

@@ -11,46 +11,69 @@ model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
 # Define the prediction function
 def predict(message, history):
-    # 'history' is a list of lists, where each inner list has a user and a bot message.
-    # We need to format it for DialoGPT.
-    history_transformer_format = []
-    for user, bot in history:
-        history_transformer_format.append(user)
-        history_transformer_format.append(bot)
+    try:
+        # Validate inputs
+        if not message or not message.strip():
+            return "", history
+        
+        if history is None:
+            history = []
+        
+        # 'history' is a list of lists, where each inner list has a user and a bot message.
+        # We need to format it for DialoGPT.
+        history_transformer_format = []
+        for exchange in history:
+            if isinstance(exchange, list) and len(exchange) >= 2:
+                user_msg, bot_msg = exchange[0], exchange[1]
+                if user_msg:
+                    history_transformer_format.append(str(user_msg))
+                if bot_msg:
+                    history_transformer_format.append(str(bot_msg))
 
-    # Join the history and the new message, separated by the EOS token
-    history_string = "".join(history_transformer_format)
-    input_text = history_string + message + tokenizer.eos_token
+        # Join the history and the new message, separated by the EOS token
+        history_string = "".join(history_transformer_format)
+        input_text = history_string + str(message) + tokenizer.eos_token
 
-    # Tokenize the input
-    new_user_input_ids = tokenizer.encode(input_text, return_tensors='pt')
+        # Tokenize the input
+        new_user_input_ids = tokenizer.encode(input_text, return_tensors='pt')
 
-    # Generate a response
-    # The max_length is set to 1250 to allow for a decent conversation history.
-    bot_output_ids = model.generate(
-        new_user_input_ids,
-        max_length=1250,
-        pad_token_id=tokenizer.eos_token_id,
-        no_repeat_ngram_size=3,
-        do_sample=True,
-        top_k=100,
-        top_p=0.7,
-        temperature=0.8
-    )
+        # Generate a response
+        # The max_length is set to 1250 to allow for a decent conversation history.
+        with torch.no_grad():
+            bot_output_ids = model.generate(
+                new_user_input_ids,
+                max_length=1250,
+                pad_token_id=tokenizer.eos_token_id,
+                no_repeat_ngram_size=3,
+                do_sample=True,
+                top_k=100,
+                top_p=0.7,
+                temperature=0.8
+            )
 
-    # Decode the response, skipping the input part
-    response = tokenizer.decode(bot_output_ids[:, new_user_input_ids.shape[-1]:][0], skip_special_tokens=True)
-    
-    # Return an empty string to clear the textbox and the updated history
-    return "", history + [[message, response]]
+        # Decode the response, skipping the input part
+        response = tokenizer.decode(bot_output_ids[:, new_user_input_ids.shape[-1]:][0], skip_special_tokens=True)
+        
+        # Clean up response
+        response = response.strip()
+        if not response:
+            response = "I'm not sure how to respond to that. Could you try rephrasing?"
+        
+        # Return an empty string to clear the textbox and the updated history
+        return "", history + [[message, response]]
+        
+    except Exception as e:
+        print(f"Error in predict function: {e}")
+        error_response = "Sorry, I encountered an error. Please try again."
+        return "", history + [[message, error_response]]
 
 # Build the Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("## DialoGPT-medium Chatbot")
     gr.Markdown("This chatbot uses the microsoft/DialoGPT-medium model. Start typing to chat!")
     
-    chatbot = gr.Chatbot()
-    textbox = gr.Textbox(placeholder="Type your message here and press Enter")
+    chatbot = gr.Chatbot(value=[], label="DialoGPT Conversation")
+    textbox = gr.Textbox(placeholder="Type your message here and press Enter", label="Message")
 
     # When the user submits the textbox, call the 'predict' function
     textbox.submit(

requirements.txt

@@ -1,3 +1,3 @@
 torch
 transformers
-gradio
+gradio>=3.50.0,<4.0.0