Update app.py

app.py

@@ -2,13 +2,13 @@ import gradio as gr
 import torch
 from PIL import Image
 from transformers import AutoTokenizer, AutoModelForCausalLM
-#import spaces
+import spaces
 
 # Model configuration
 MID = "apple/FastVLM-0.5B"
 IMAGE_TOKEN_INDEX = -200
 
-# Load model and tokenizer
+# Load model and tokenizer (will be loaded on first GPU allocation)
 tok = None
 model = None
 
@@ -37,41 +37,146 @@ def load_model():
 
 #@spaces.GPU(duration=60)
 def caption_image(image, custom_prompt=None):
+    """
+    Generate a caption for the input image.
+    
+    Args:
+        image: PIL Image from Gradio
+        custom_prompt: Optional custom prompt to use instead of default
+    
+    Returns:
+        Generated caption text
+    """
     if image is None:
         return "Please upload an image first."
     
     try:
+        # Load model if not already loaded
         tok, model = load_model()
+        # Convert image to RGB if needed
         if image.mode != "RGB":
             image = image.convert("RGB")
-
+        
+        # Use custom prompt or default
         prompt = custom_prompt if custom_prompt else "Describe this image in detail."
-        messages = [{"role": "user", "content": f"<image>\n{prompt}"}]
-        rendered = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
-
+        
+        # Build chat message
+        messages = [
+            {"role": "user", "content": f"<image>\n{prompt}"}
+        ]
+        
+        # Render to string to place <image> token correctly
+        rendered = tok.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=False
+        )
+        
+        # Split at image token
         pre, post = rendered.split("<image>", 1)
+        
+        # Tokenize text around the image token
         pre_ids = tok(pre, return_tensors="pt", add_special_tokens=False).input_ids
         post_ids = tok(post, return_tensors="pt", add_special_tokens=False).input_ids
-
+        
+        # Insert IMAGE token id at placeholder position
         img_tok = torch.tensor([[IMAGE_TOKEN_INDEX]], dtype=pre_ids.dtype)
         input_ids = torch.cat([pre_ids, img_tok, post_ids], dim=1).to(model.device)
         attention_mask = torch.ones_like(input_ids, device=model.device)
-
-        px = model.get_vision_tower().image_processor(images=image, return_tensors="pt")["pixel_values"]
+        
+        # Preprocess image using model's vision tower
+        px = model.get_vision_tower().image_processor(
+            images=image, return_tensors="pt"
+        )["pixel_values"]
         px = px.to(model.device, dtype=model.dtype)
-
+        
+        # Generate caption
         with torch.no_grad():
             out = model.generate(
                 inputs=input_ids,
                 attention_mask=attention_mask,
                 images=px,
                 max_new_tokens=128,
-                do_sample=False,
+                do_sample=False,  # Deterministic generation
                 temperature=1.0,
             )
-
+        
+        # Decode and return the generated text
         generated_text = tok.decode(out[0], skip_special_tokens=True)
-        return generated_text.split("assistant")[-1].strip() if "assistant" in generated_text else generated_text
-
+        
+        # Extract only the assistant's response
+        if "assistant" in generated_text:
+            response = generated_text.split("assistant")[-1].strip()
+        else:
+            response = generated_text
+            
+        return response
+        
     except Exception as e:
         return f"Error generating caption: {str(e)}"
+
+# Create Gradio interface
+with gr.Blocks(title="FastVLM Image Captioning") as demo:
+    gr.Markdown(
+        """
+        # 🖼️ FastVLM Image Captioning
+        
+        Upload an image to generate a detailed caption using Apple's FastVLM-0.5B model.
+        You can use the default prompt or provide your own custom prompt.
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(
+                type="pil",
+                label="Upload Image",
+                elem_id="image-upload"
+            )
+            
+            custom_prompt = gr.Textbox(
+                label="Custom Prompt (Optional)",
+                placeholder="Leave empty for default: 'Describe this image in detail.'",
+                lines=2
+            )
+            
+            with gr.Row():
+                clear_btn = gr.ClearButton([image_input, custom_prompt])
+                generate_btn = gr.Button("Generate Caption", variant="primary")
+        
+        with gr.Column():
+            output = gr.Textbox(
+                label="Generated Caption",
+                lines=8,
+                max_lines=15,
+                show_copy_button=True
+            )
+    
+    # Event handlers
+    generate_btn.click(
+        fn=caption_image,
+        inputs=[image_input, custom_prompt],
+        outputs=output
+    )
+    
+    # Also generate on image upload if no custom prompt
+    image_input.change(
+        fn=lambda img, prompt: caption_image(img, prompt) if img is not None and not prompt else None,
+        inputs=[image_input, custom_prompt],
+        outputs=output
+    )
+    
+    gr.Markdown(
+        """
+        ---
+        **Model:** [apple/FastVLM-0.5B](https://huggingface.co/apple/FastVLM-0.5B)
+        
+        **Note:** This Space uses ZeroGPU for dynamic GPU allocation.
+        """
+    )
+
+if __name__ == "__main__":
+    demo.launch(
+        share=False,
+        show_error=True,
+        server_name="0.0.0.0",
+        server_port=7860
+    )