commented few lines

app.py

@@ -32,29 +32,29 @@ model = MllamaForConditionalGeneration.from_pretrained(
 model.to(device)
 processor = AutoProcessor.from_pretrained(model_name, use_auth_token=HF_TOKEN)
 
-@spaces.GPU  # Use the free GPU provided by Hugging Face Spaces
-def predict(image, text):
-    # Prepare the input messages
-    messages = [
-        {"role": "user", "content": [
-            {"type": "image"},  # Specify that an image is provided
-            {"type": "text", "text": text}  # Add the user-provided text input
-        ]}
-    ]
+# @spaces.GPU  # Use the free GPU provided by Hugging Face Spaces
+# def predict(image, text):
+#     # Prepare the input messages
+#     messages = [
+#         {"role": "user", "content": [
+#             {"type": "image"},  # Specify that an image is provided
+#             {"type": "text", "text": text}  # Add the user-provided text input
+#         ]}
+#     ]
     
-    # Create the input text using the processor's chat template
-    input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
+#     # Create the input text using the processor's chat template
+#     input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
     
-    # Process the inputs and move to the appropriate device
-    inputs = processor(image, input_text, return_tensors="pt").to(device)
+#     # Process the inputs and move to the appropriate device
+#     inputs = processor(image, input_text, return_tensors="pt").to(device)
     
-    # Generate a response from the model
-    outputs = model.generate(**inputs, max_new_tokens=100)
+#     # Generate a response from the model
+#     outputs = model.generate(**inputs, max_new_tokens=100)
     
-    # Decode the output to return the final response
-    response = processor.decode(outputs[0], skip_special_tokens=True)
-    return response
-
+#     # Decode the output to return the final response
+#     response = processor.decode(outputs[0], skip_special_tokens=True)
+#     return response
+# @spaces.GPU
 def predict_text(text):
     # Prepare the input messages
     messages = [{"role": "user", "content": [{"type": "text", "text": text}]}]