removed examples

app.py

@@ -9,10 +9,8 @@ if NANOVLM_REPO_PATH not in sys.path:
 import gradio as gr
 from PIL import Image
 import torch
-# Import specific processor components
 from transformers import CLIPImageProcessor, GPT2TokenizerFast
 
-# Import the custom VisionLanguageModel class
 try:
     from models.vision_language_model import VisionLanguageModel
     print("Successfully imported VisionLanguageModel from nanoVLM clone.")
@@ -20,7 +18,6 @@ except ImportError as e:
     print(f"Error importing VisionLanguageModel from nanoVLM clone: {e}.")
     VisionLanguageModel = None
 
-# Determine the device to use
 device_choice = os.environ.get("DEVICE", "auto")
 if device_choice == "auto":
     device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -28,7 +25,6 @@ else:
     device = device_choice
 print(f"Using device: {device}")
 
-# --- Configuration for model components ---
 model_id_for_weights = "lusxvr/nanoVLM-222M"
 image_processor_id = "openai/clip-vit-base-patch32"
 tokenizer_id = "gpt2"
@@ -40,22 +36,18 @@ 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)
+        image_processor = CLIPImageProcessor.from_pretrained(image_processor_id) # Removed trust_remote_code if not strictly needed by processor
         print("CLIPImageProcessor loaded.")
         
         print(f"Attempting to load GPT2TokenizerFast from: {tokenizer_id}")
-        tokenizer = GPT2TokenizerFast.from_pretrained(tokenizer_id, trust_remote_code=True)
+        tokenizer = GPT2TokenizerFast.from_pretrained(tokenizer_id) # Removed trust_remote_code if not strictly needed by tokenizer
         if tokenizer.pad_token is None:
             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
-        ).to(device)
+        model = VisionLanguageModel.from_pretrained(model_id_for_weights).to(device)
         print("Model loaded successfully.")
         model.eval()
 
@@ -63,62 +55,43 @@ if VisionLanguageModel:
         print(f"Error loading model or processor components: {e}")
         import traceback
         traceback.print_exc()
-        image_processor = None
-        tokenizer = None
-        model = None
+        image_processor = None; tokenizer = None; model = None
 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.")
-    
     processed_image = image_processor_instance(images=image_input, return_tensors="pt").pixel_values.to(device_to_use)
-    
     processed_text = tokenizer_instance(
-        text=text_list, return_tensors="pt", padding=True, truncation=True, max_length=tokenizer_instance.model_max_length
+        text=text_list, return_tensors="pt", padding=True, truncation=True, max_length=getattr(tokenizer_instance, 'model_max_length', 512)
     )
     input_ids = processed_text.input_ids.to(device_to_use)
     attention_mask = processed_text.attention_mask.to(device_to_use)
-    
     return {"pixel_values": processed_image, "input_ids": input_ids, "attention_mask": attention_mask}
 
 def generate_text_for_image(image_input, prompt_input):
     if model is None or image_processor is None or tokenizer is None:
         return "Error: Model or processor components not loaded correctly. Check logs."
-
-    if image_input is None:
-        return "Please upload an image."
-    if not prompt_input:
-        return "Please provide a prompt."
+    if image_input is None: return "Please upload an image."
+    if not prompt_input: return "Please provide a prompt."
 
     try:
         if not isinstance(image_input, Image.Image):
             pil_image = Image.fromarray(image_input)
         else:
             pil_image = image_input
-        
-        if pil_image.mode != "RGB":
-            pil_image = pil_image.convert("RGB")
+        if pil_image.mode != "RGB": pil_image = pil_image.convert("RGB")
 
         inputs = prepare_inputs(
-            text_list=[prompt_input],
-            image_input=pil_image,
-            image_processor_instance=image_processor,
-            tokenizer_instance=tokenizer,
-            device_to_use=device
+            text_list=[prompt_input], image_input=pil_image,
+            image_processor_instance=image_processor, tokenizer_instance=tokenizer, device_to_use=device
         )
         
         generated_ids = model.generate(
-            pixel_values=inputs['pixel_values'],
-            input_ids=inputs['input_ids'],
-            attention_mask=inputs['attention_mask'],
-            max_new_tokens=150,
-            num_beams=3,
-            no_repeat_ngram_size=2,
-            early_stopping=True,
-            pad_token_id=tokenizer.pad_token_id
+            pixel_values=inputs['pixel_values'], input_ids=inputs['input_ids'],
+            attention_mask=inputs['attention_mask'], max_new_tokens=150, num_beams=3,
+            no_repeat_ngram_size=2, early_stopping=True, pad_token_id=tokenizer.pad_token_id
         )
         
         generated_text_list = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
@@ -128,38 +101,51 @@ def generate_text_for_image(image_input, prompt_input):
              cleaned_text = generated_text[len(prompt_input):].lstrip(" ,.:")
         else:
             cleaned_text = generated_text
-
         return cleaned_text.strip()
-
     except Exception as e:
         print(f"Error during generation: {e}")
-        import traceback
-        traceback.print_exc()
+        import traceback; traceback.print_exc()
         return f"An error occurred during text generation: {str(e)}"
 
 description = "Interactive demo for lusxvr/nanoVLM-222M."
-example_image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
-
-iface = gr.Interface(
-    fn=generate_text_for_image,
-    inputs=[
-        gr.Image(type="pil", label="Upload Image"),
-        gr.Textbox(label="Your Prompt/Question")
-    ],
-    outputs=gr.Textbox(label="Generated Text", show_copy_button=True),
-    title="Interactive nanoVLM-222M Demo",
-    description=description,
-    examples=[
-        [example_image_url, "a photo of a"],
-        [example_image_url, "Describe the image in detail."],
-    ],
-    # cache_examples=True, # Keep commented out for now
-    allow_flagging="never"
-)
+# example_image_url = "http://images.cocodataset.org/val2017/000000039769.jpg" # Not used for now
+
+print("Defining Gradio interface...")
+try:
+    iface = gr.Interface(
+        fn=generate_text_for_image,
+        inputs=[
+            gr.Image(type="pil", label="Upload Image"),
+            gr.Textbox(label="Your Prompt/Question")
+        ],
+        outputs=gr.Textbox(label="Generated Text", show_copy_button=True),
+        title="Interactive nanoVLM-222M Demo",
+        description=description,
+        # examples=[  # <<<< REMOVED EXAMPLES
+        #     [example_image_url, "a photo of a"],
+        #     [example_image_url, "Describe the image in detail."],
+        # ],
+        allow_flagging="never"
+    )
+    print("Gradio interface defined.")
+except Exception as e:
+    print(f"Error defining Gradio interface: {e}")
+    import traceback; traceback.print_exc()
+    iface = None
+
 
 if __name__ == "__main__":
     if model is None or image_processor is None or tokenizer is None:
-        print("CRITICAL: Model or processor components failed to load.")
-    else:
+        print("CRITICAL: Model or processor components failed to load. Gradio might not work.")
+    
+    if iface is not None:
         print("Launching Gradio interface...")
-    iface.launch(server_name="0.0.0.0", server_port=7860)
+        try:
+            iface.launch(server_name="0.0.0.0", server_port=7860)
+        except Exception as e:
+            print(f"Error launching Gradio interface: {e}")
+            import traceback; traceback.print_exc()
+            # This is where the ValueError: When localhost is not accessible... usually comes from
+            # if the underlying TypeError has already happened during iface setup.
+    else:
+        print("Gradio interface could not be defined due to earlier errors.")