Fix: Add @spaces.GPU decorator and huggingface_hub dependency for GPU detection

app.py

@@ -3,6 +3,7 @@ import torch
 from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
 from PIL import Image
 import logging
+from huggingface_hub import spaces
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -214,7 +215,9 @@ def create_interface():
     
     return demo
 
-if __name__ == "__main__":
+@spaces.GPU
+def main():
+    """Main function with GPU decorator for Hugging Face Spaces"""
     # Create and launch the interface
     demo = create_interface()
     demo.launch(
@@ -224,3 +227,6 @@ if __name__ == "__main__":
         show_error=True
     )
 
+if __name__ == "__main__":
+    main()
+

app.py.backup

@@ -0,0 +1,226 @@
+import gradio as gr
+import torch
+from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
+from PIL import Image
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Global variables for model and processor
+model = None
+processor = None
+
+def load_model():
+    """Load the fine-tuned dermatology model"""
+    global model, processor
+    
+    try:
+        # Load the merged model (replace with your actual model path)
+        model_name = "ColdSlim/Dermatology-Qwen2.5-VL-3B"  # Update with your actual model name
+        
+        logger.info(f"Loading model: {model_name}")
+        processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+        model = Qwen2VLForConditionalGeneration.from_pretrained(
+            model_name, 
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+            trust_remote_code=True
+        )
+        
+        logger.info("Model loaded successfully!")
+        return True
+        
+    except Exception as e:
+        logger.error(f"Error loading model: {e}")
+        return False
+
+def analyze_skin_condition(image, question="Describe this skin condition in detail."):
+    """Analyze skin condition from uploaded image"""
+    global model, processor
+    
+    if model is None or processor is None:
+        return "❌ Model not loaded. Please wait for the model to load or contact the administrator."
+    
+    if image is None:
+        return "❌ Please upload an image first."
+    
+    try:
+        # Prepare the conversation
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "image": image},
+                    {"type": "text", "text": question}
+                ]
+            }
+        ]
+        
+        # Process the input
+        text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        image_inputs, video_inputs = processor.process_vision_info(messages)
+        
+        inputs = processor(
+            text=[text],
+            images=image_inputs,
+            videos=video_inputs,
+            padding=True,
+            return_tensors="pt"
+        )
+        
+        # Move inputs to the same device as model
+        inputs = {k: v.to(model.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
+        
+        # Generate response
+        with torch.no_grad():
+            generated_ids = model.generate(
+                **inputs,
+                max_new_tokens=512,
+                do_sample=True,
+                temperature=0.7,
+                top_p=0.9,
+                pad_token_id=processor.tokenizer.eos_token_id
+            )
+        
+        # Decode the response
+        generated_ids_trimmed = [
+            out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+        ]
+        output_text = processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )[0]
+        
+        return output_text
+        
+    except Exception as e:
+        logger.error(f"Error during inference: {e}")
+        return f"❌ Error analyzing image: {str(e)}"
+
+def create_interface():
+    """Create the Gradio interface"""
+    
+    # Load model on startup
+    model_loaded = load_model()
+    
+    with gr.Blocks(
+        title="Dermatology AI Assistant",
+        theme=gr.themes.Soft(),
+        css="""
+        .gradio-container {
+            max-width: 1200px !important;
+            margin: auto !important;
+        }
+        .main-header {
+            text-align: center;
+            margin-bottom: 2rem;
+        }
+        .warning-box {
+            background-color: #fff3cd;
+            border: 1px solid #ffeaa7;
+            border-radius: 8px;
+            padding: 1rem;
+            margin: 1rem 0;
+        }
+        """
+    ) as demo:
+        
+        gr.HTML("""
+        <div class="main-header">
+            <h1>🩺 Dermatology AI Assistant</h1>
+            <p>Powered by Qwen2.5-VL-3B fine-tuned for dermatology analysis</p>
+        </div>
+        """)
+        
+        # Warning message
+        gr.HTML("""
+        <div class="warning-box">
+            <h3>⚠️ Medical Disclaimer</h3>
+            <p>This AI assistant is for educational and research purposes only. 
+            It should not be used as a substitute for professional medical advice, 
+            diagnosis, or treatment. Always consult with a qualified healthcare 
+            provider for medical concerns.</p>
+        </div>
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                # Image upload
+                image_input = gr.Image(
+                    label="Upload Skin Image",
+                    type="pil",
+                    height=400
+                )
+                
+                # Question input
+                question_input = gr.Textbox(
+                    label="Question (Optional)",
+                    placeholder="Describe this skin condition in detail.",
+                    value="Describe this skin condition in detail.",
+                    lines=3
+                )
+                
+                # Analyze button
+                analyze_btn = gr.Button(
+                    "🔍 Analyze Skin Condition",
+                    variant="primary",
+                    size="lg"
+                )
+                
+                # Example questions
+                gr.HTML("""
+                <h4>💡 Example Questions:</h4>
+                <ul>
+                    <li>What type of skin condition is this?</li>
+                    <li>Describe the characteristics of this lesion.</li>
+                    <li>What are the potential causes of this skin issue?</li>
+                    <li>What should I know about this skin condition?</li>
+                </ul>
+                """)
+            
+            with gr.Column(scale=1):
+                # Output
+                output_text = gr.Textbox(
+                    label="AI Analysis",
+                    lines=15,
+                    max_lines=20,
+                    show_copy_button=True
+                )
+        
+        # Examples
+        gr.Examples(
+            examples=[
+                ["What type of skin condition is this?", "Describe this skin condition in detail."],
+                ["What are the characteristics of this lesion?", "Describe this skin condition in detail."],
+                ["What should I know about this skin issue?", "Describe this skin condition in detail."],
+            ],
+            inputs=[question_input, question_input],
+            label="💡 Example Questions"
+        )
+        
+        # Event handlers
+        analyze_btn.click(
+            fn=analyze_skin_condition,
+            inputs=[image_input, question_input],
+            outputs=output_text
+        )
+        
+        # Model status
+        if model_loaded:
+            gr.HTML("<div style='text-align: center; color: green;'>✅ Model loaded successfully!</div>")
+        else:
+            gr.HTML("<div style='text-align: center; color: red;'>❌ Model loading failed. Please check the logs.</div>")
+    
+    return demo
+
+if __name__ == "__main__":
+    # Create and launch the interface
+    demo = create_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True
+    )
+

app.py.old

@@ -0,0 +1,226 @@
+import gradio as gr
+import torch
+from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
+from PIL import Image
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Global variables for model and processor
+model = None
+processor = None
+
+def load_model():
+    """Load the fine-tuned dermatology model"""
+    global model, processor
+    
+    try:
+        # Load the merged model (replace with your actual model path)
+        model_name = "ColdSlim/Dermatology-Qwen2.5-VL-3B"  # Update with your actual model name
+        
+        logger.info(f"Loading model: {model_name}")
+        processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+        model = Qwen2VLForConditionalGeneration.from_pretrained(
+            model_name, 
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+            trust_remote_code=True
+        )
+        
+        logger.info("Model loaded successfully!")
+        return True
+        
+    except Exception as e:
+        logger.error(f"Error loading model: {e}")
+        return False
+
+def analyze_skin_condition(image, question="Describe this skin condition in detail."):
+    """Analyze skin condition from uploaded image"""
+    global model, processor
+    
+    if model is None or processor is None:
+        return "❌ Model not loaded. Please wait for the model to load or contact the administrator."
+    
+    if image is None:
+        return "❌ Please upload an image first."
+    
+    try:
+        # Prepare the conversation
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "image": image},
+                    {"type": "text", "text": question}
+                ]
+            }
+        ]
+        
+        # Process the input
+        text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        image_inputs, video_inputs = processor.process_vision_info(messages)
+        
+        inputs = processor(
+            text=[text],
+            images=image_inputs,
+            videos=video_inputs,
+            padding=True,
+            return_tensors="pt"
+        )
+        
+        # Move inputs to the same device as model
+        inputs = {k: v.to(model.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
+        
+        # Generate response
+        with torch.no_grad():
+            generated_ids = model.generate(
+                **inputs,
+                max_new_tokens=512,
+                do_sample=True,
+                temperature=0.7,
+                top_p=0.9,
+                pad_token_id=processor.tokenizer.eos_token_id
+            )
+        
+        # Decode the response
+        generated_ids_trimmed = [
+            out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+        ]
+        output_text = processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )[0]
+        
+        return output_text
+        
+    except Exception as e:
+        logger.error(f"Error during inference: {e}")
+        return f"❌ Error analyzing image: {str(e)}"
+
+def create_interface():
+    """Create the Gradio interface"""
+    
+    # Load model on startup
+    model_loaded = load_model()
+    
+    with gr.Blocks(
+        title="Dermatology AI Assistant",
+        theme=gr.themes.Soft(),
+        css="""
+        .gradio-container {
+            max-width: 1200px !important;
+            margin: auto !important;
+        }
+        .main-header {
+            text-align: center;
+            margin-bottom: 2rem;
+        }
+        .warning-box {
+            background-color: #fff3cd;
+            border: 1px solid #ffeaa7;
+            border-radius: 8px;
+            padding: 1rem;
+            margin: 1rem 0;
+        }
+        """
+    ) as demo:
+        
+        gr.HTML("""
+        <div class="main-header">
+            <h1>🩺 Dermatology AI Assistant</h1>
+            <p>Powered by Qwen2.5-VL-3B fine-tuned for dermatology analysis</p>
+        </div>
+        """)
+        
+        # Warning message
+        gr.HTML("""
+        <div class="warning-box">
+            <h3>⚠️ Medical Disclaimer</h3>
+            <p>This AI assistant is for educational and research purposes only. 
+            It should not be used as a substitute for professional medical advice, 
+            diagnosis, or treatment. Always consult with a qualified healthcare 
+            provider for medical concerns.</p>
+        </div>
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                # Image upload
+                image_input = gr.Image(
+                    label="Upload Skin Image",
+                    type="pil",
+                    height=400
+                )
+                
+                # Question input
+                question_input = gr.Textbox(
+                    label="Question (Optional)",
+                    placeholder="Describe this skin condition in detail.",
+                    value="Describe this skin condition in detail.",
+                    lines=3
+                )
+                
+                # Analyze button
+                analyze_btn = gr.Button(
+                    "🔍 Analyze Skin Condition",
+                    variant="primary",
+                    size="lg"
+                )
+                
+                # Example questions
+                gr.HTML("""
+                <h4>💡 Example Questions:</h4>
+                <ul>
+                    <li>What type of skin condition is this?</li>
+                    <li>Describe the characteristics of this lesion.</li>
+                    <li>What are the potential causes of this skin issue?</li>
+                    <li>What should I know about this skin condition?</li>
+                </ul>
+                """)
+            
+            with gr.Column(scale=1):
+                # Output
+                output_text = gr.Textbox(
+                    label="AI Analysis",
+                    lines=15,
+                    max_lines=20,
+                    show_copy_button=True
+                )
+        
+        # Examples
+        gr.Examples(
+            examples=[
+                ["What type of skin condition is this?", "Describe this skin condition in detail."],
+                ["What are the characteristics of this lesion?", "Describe this skin condition in detail."],
+                ["What should I know about this skin issue?", "Describe this skin condition in detail."],
+            ],
+            inputs=[question_input, question_input],
+            label="💡 Example Questions"
+        )
+        
+        # Event handlers
+        analyze_btn.click(
+            fn=analyze_skin_condition,
+            inputs=[image_input, question_input],
+            outputs=output_text
+        )
+        
+        # Model status
+        if model_loaded:
+            gr.HTML("<div style='text-align: center; color: green;'>✅ Model loaded successfully!</div>")
+        else:
+            gr.HTML("<div style='text-align: center; color: red;'>❌ Model loading failed. Please check the logs.</div>")
+    
+    return demo
+
+if __name__ == "__main__":
+    # Create and launch the interface
+    demo = create_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        show_error=True
+    )
+

requirements.txt

@@ -4,6 +4,7 @@ torch>=2.0.0
 transformers>=4.37.0
 accelerate>=0.20.0
 gradio>=4.0.0
+huggingface_hub>=0.20.0
 
 # Vision and image processing
 Pillow>=9.0.0

requirements.txt.backup

@@ -0,0 +1,22 @@
+# Hugging Face Spaces Requirements for Dermatology AI Assistant
+# Core dependencies
+torch>=2.0.0
+transformers>=4.37.0
+accelerate>=0.20.0
+gradio>=4.0.0
+
+# Vision and image processing
+Pillow>=9.0.0
+opencv-python>=4.5.0
+
+# Qwen2-VL specific
+qwen-vl-utils>=0.0.1
+
+# Optional: For better performance
+flash-attn>=2.0.0
+deepspeed>=0.10.0
+
+# Utilities
+numpy>=1.21.0
+requests>=2.25.0
+

requirements.txt.old

@@ -0,0 +1,22 @@
+# Hugging Face Spaces Requirements for Dermatology AI Assistant
+# Core dependencies
+torch>=2.0.0
+transformers>=4.37.0
+accelerate>=0.20.0
+gradio>=4.0.0
+
+# Vision and image processing
+Pillow>=9.0.0
+opencv-python>=4.5.0
+
+# Qwen2-VL specific
+qwen-vl-utils>=0.0.1
+
+# Optional: For better performance
+flash-attn>=2.0.0
+deepspeed>=0.10.0
+
+# Utilities
+numpy>=1.21.0
+requests>=2.25.0
+