temp handle it turns request

handler.py

@@ -4,6 +4,7 @@ import torch
 import numpy as np
 import hashlib
 import json
+import base64
 import requests
 from PIL import Image
 from io import BytesIO
@@ -303,10 +304,19 @@ def generate_response(message_text, image_input, temperature=0.05, top_p=1.0, ma
         
         # Process image for model
         try:
-            image_tensor = process_images([image], our_chatbot.image_processor, our_chatbot.model.config)[0]
+            print(f"[DEBUG] Processing image for model...")
+            processed_images = process_images([image], our_chatbot.image_processor, our_chatbot.model.config)
+            print(f"[DEBUG] Processed images length: {len(processed_images)}")
+            
+            if len(processed_images) == 0:
+                return {"error": "Image processing returned empty list"}
+            
+            image_tensor = processed_images[0]
             image_tensor = image_tensor.half().to(our_chatbot.model.device)
             image_tensor = image_tensor.unsqueeze(0)
+            print(f"[DEBUG] Image tensor shape: {image_tensor.shape}")
         except Exception as e:
+            print(f"[DEBUG] Image processing error: {str(e)}")
             return {"error": f"Image processing failed: {str(e)}"}
         
         # Prepare conversation
@@ -346,8 +356,27 @@ def generate_response(message_text, image_input, temperature=0.05, top_p=1.0, ma
             )
         
         # Decode response
-        response = our_chatbot.tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)
-        our_chatbot.conversation.messages[-1][-1] = response
+        try:
+            print(f"[DEBUG] Outputs shape: {outputs.shape if hasattr(outputs, 'shape') else 'No shape attr'}")
+            print(f"[DEBUG] Outputs length: {len(outputs) if hasattr(outputs, '__len__') else 'No length'}")
+            print(f"[DEBUG] Input IDs shape: {input_ids.shape}")
+            
+            if len(outputs) == 0:
+                return {"error": "Model generated empty output"}
+            
+            response = our_chatbot.tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)
+            
+            print(f"[DEBUG] Conversation messages length: {len(our_chatbot.conversation.messages)}")
+            if len(our_chatbot.conversation.messages) > 0:
+                print(f"[DEBUG] Last message length: {len(our_chatbot.conversation.messages[-1])}")
+                our_chatbot.conversation.messages[-1][-1] = response
+            else:
+                print("[DEBUG] No conversation messages found")
+            
+            print(f"[DEBUG] Generated response length: {len(response)}")
+        except Exception as e:
+            print(f"[DEBUG] Response decoding error: {str(e)}")
+            return {"error": f"Response decoding failed: {str(e)}"}
         
         # Log conversation
         history = [(message_text, response)]