solve json error

app.py

@@ -96,14 +96,36 @@ class LOperatorDemo:
                 }
             ]
             
-            # Process inputs
-            inputs = self.processor.apply_chat_template(
-                conversation,
-                add_generation_prompt=True,
-                return_tensors="pt"
-            ).to(self.model.device)
+            logger.info("Processing conversation with processor...")
+            
+            # Process inputs with better error handling
+            try:
+                inputs = self.processor.apply_chat_template(
+                    conversation,
+                    add_generation_prompt=True,
+                    return_tensors="pt"
+                )
+                logger.info(f"Processor output type: {type(inputs)}")
+                
+                # Ensure inputs is a tensor and move to correct device
+                if not isinstance(inputs, torch.Tensor):
+                    logger.warning("apply_chat_template did not return a tensor, attempting to convert...")
+                    if isinstance(inputs, (list, tuple)):
+                        inputs = torch.tensor(inputs)
+                    else:
+                        # If it's a string or other type, we need to handle it differently
+                        logger.error(f"Unexpected input type: {type(inputs)}, value: {inputs}")
+                        return "❌ Error: Processor returned unexpected format"
+                
+                inputs = inputs.to(self.model.device)
+                logger.info(f"Inputs shape: {inputs.shape}, device: {inputs.device}")
+                
+            except Exception as e:
+                logger.error(f"Error in processor: {str(e)}")
+                return f"❌ Error in processor: {str(e)}"
             
             # Generate response
+            logger.info("Generating response...")
             with torch.no_grad():
                 outputs = self.model.generate(
                     inputs,
@@ -113,6 +135,7 @@ class LOperatorDemo:
                     top_p=0.9
                 )
             
+            logger.info("Decoding response...")
             response = self.processor.tokenizer.decode(
                 outputs[0][inputs.shape[1]:], 
                 skip_special_tokens=True
@@ -200,6 +223,8 @@ def load_example_episodes():
                     episode_num = episode_dir.split('_')[1]
                     goal_text = metadata.get('goal', f'Episode {episode_num} example')
                     
+                    logger.info(f"Episode {episode_num} goal: {goal_text}")
+                    
                     examples.append([
                         pil_image,  # Use PIL Image object directly
                         goal_text  # Use the goal text from metadata

requirements.txt

@@ -5,4 +5,4 @@ Pillow>=10.0.0
 accelerate>=0.20.0
 huggingface-hub>=0.17.0
 safetensors>=0.4.0
-spaces>=0.1.0
+spaces