trying to fix typerror

app.py

@@ -31,8 +31,7 @@ print(f"Using device: {device}")
 # --- Configuration for model components ---
 model_id_for_weights = "lusxvr/nanoVLM-222M"
 image_processor_id = "openai/clip-vit-base-patch32"
-# Load the tokenizer from its original source to ensure all files are present
-tokenizer_id = "gpt2" # Changed from "lusxvr/nanoVLM-222M"
+tokenizer_id = "gpt2"
 
 image_processor = None
 tokenizer = None
@@ -41,20 +40,21 @@ model = None
 if VisionLanguageModel:
     try:
         print(f"Attempting to load CLIPImageProcessor from: {image_processor_id}")
+        # trust_remote_code for HF's classes is fine if they support it.
         image_processor = CLIPImageProcessor.from_pretrained(image_processor_id, trust_remote_code=True)
         print("CLIPImageProcessor loaded.")
         
         print(f"Attempting to load GPT2TokenizerFast from: {tokenizer_id}")
         tokenizer = GPT2TokenizerFast.from_pretrained(tokenizer_id, trust_remote_code=True)
         if tokenizer.pad_token is None:
-            tokenizer.pad_token = tokenizer.eos_token # Important for GPT-2
+            tokenizer.pad_token = tokenizer.eos_token
             print("Set tokenizer pad_token to eos_token.")
         print("GPT2TokenizerFast loaded.")
         
         print(f"Attempting to load model weights from {model_id_for_weights} using VisionLanguageModel.from_pretrained")
+        # Removed trust_remote_code=True as the custom VisionLanguageModel.from_pretrained doesn't expect it.
         model = VisionLanguageModel.from_pretrained(
-            model_id_for_weights,
-            trust_remote_code=True
+            model_id_for_weights
         ).to(device)
         print("Model loaded successfully.")
         model.eval()
@@ -69,6 +69,7 @@ if VisionLanguageModel:
 else:
     print("Custom VisionLanguageModel class not imported, cannot load model.")
 
+# ... (rest of the app.py remains the same) ...
 def prepare_inputs(text_list, image_input, image_processor_instance, tokenizer_instance, device_to_use):
     if image_processor_instance is None or tokenizer_instance is None:
         raise ValueError("Image processor or tokenizer not initialized.")
@@ -152,7 +153,7 @@ iface = gr.Interface(
         [example_image_url, "a photo of a"],
         [example_image_url, "Describe the image in detail."],
     ],
-    # cache_examples=True, # Temporarily commented out to ensure Gradio starts with minimal config
+    # cache_examples=True, # Keep commented out for now
     allow_flagging="never"
 )