Delete app.py.old

app.py.old

@@ -1,226 +0,0 @@
-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
-    )
-