Create app.py

app.py

@@ -0,0 +1,254 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+from huggingface_hub import hf_hub_download
+from tensorflow.keras.applications.efficientnet_v2 import preprocess_input
+import os
+
+# Class names for the 5 skin conditions
+CLASS_NAMES = [
+    'Atopic Dermatitis',
+    'Eczema', 
+    'Psoriasis',
+    'Seborrheic Keratoses',
+    'Tinea Ringworm Candidiasis'
+]
+
+# Class descriptions
+CLASS_DESCRIPTIONS = {
+    'Atopic Dermatitis': 'A chronic inflammatory skin condition causing dry, itchy patches',
+    'Eczema': 'Inflammatory skin condition causing red, itchy, and inflamed patches',
+    'Psoriasis': 'Autoimmune condition causing thick, scaly patches on the skin',
+    'Seborrheic Keratoses': 'Common benign (non-cancerous) skin growths',
+    'Tinea Ringworm Candidiasis': 'Fungal skin infections causing circular, scaly patches'
+}
+
+@gr.utils.require_minimum_gradio_version("3.0.0")
+class DermaAIModel:
+    def __init__(self):
+        self.model = None
+        self.load_model()
+    
+    def load_model(self):
+        """Load the DermaAI model from Hugging Face Hub"""
+        try:
+            print("🔄 Loading DermaAI model from Hugging Face...")
+            model_path = hf_hub_download(
+                repo_id="Siraja704/DermaAI", 
+                filename="DermaAI.keras"
+            )
+            self.model = tf.keras.models.load_model(model_path)
+            print("✅ Model loaded successfully!")
+        except Exception as e:
+            print(f"❌ Error loading model: {e}")
+            raise e
+    
+    def predict(self, image):
+        """Make prediction on the input image"""
+        if self.model is None:
+            return {"error": "Model not loaded"}
+        
+        try:
+            # Preprocess image
+            if image is None:
+                return {"error": "No image provided"}
+            
+            # Convert to RGB if necessary
+            if image.mode != 'RGB':
+                image = image.convert('RGB')
+            
+            # Resize to model input size
+            image_resized = image.resize((224, 224))
+            
+            # Convert to numpy array and preprocess
+            image_array = np.array(image_resized)
+            image_array = preprocess_input(image_array)
+            image_array = np.expand_dims(image_array, axis=0)
+            
+            # Make prediction
+            predictions = self.model.predict(image_array, verbose=0)
+            
+            # Get results
+            predicted_class_idx = np.argmax(predictions[0])
+            confidence = float(predictions[0][predicted_class_idx])
+            
+            # Prepare results dictionary for Gradio
+            results = {}
+            for i, class_name in enumerate(CLASS_NAMES):
+                results[class_name] = float(predictions[0][i])
+            
+            return results
+            
+        except Exception as e:
+            print(f"❌ Error during prediction: {e}")
+            return {"error": f"Prediction failed: {str(e)}"}
+
+# Initialize model
+print("🚀 Initializing DermaAI...")
+derma_model = DermaAIModel()
+
+def predict_skin_condition(image):
+    """Wrapper function for Gradio interface"""
+    if image is None:
+        return {"error": "Please upload an image"}
+    
+    return derma_model.predict(image)
+
+def get_medical_advice(image):
+    """Provide medical advice based on prediction"""
+    if image is None:
+        return "Please upload an image first."
+    
+    results = derma_model.predict(image)
+    
+    if "error" in results:
+        return results["error"]
+    
+    # Find the top prediction
+    top_prediction = max(results, key=results.get)
+    confidence = results[top_prediction] * 100
+    
+    # Generate advice based on confidence
+    advice = f"**Predicted Condition:** {top_prediction}\n\n"
+    advice += f"**Confidence:** {confidence:.1f}%\n\n"
+    advice += f"**Description:** {CLASS_DESCRIPTIONS.get(top_prediction, 'No description available')}\n\n"
+    
+    if confidence < 30:
+        advice += "⚠️ **Low Confidence Warning:** The AI model has low confidence in this prediction. Please retake the image with better lighting and focus, or consult a healthcare professional."
+    elif confidence < 60:
+        advice += "🔍 **Moderate Confidence:** This is a preliminary assessment. Consider consulting with a healthcare professional for accurate diagnosis."
+    else:
+        advice += "✅ **High Confidence:** The model shows high confidence, but this is still a preliminary assessment."
+    
+    advice += "\n\n🏥 **Important Medical Disclaimer:** This AI tool is for educational purposes only and should not replace professional medical diagnosis. Always consult qualified healthcare professionals for proper medical evaluation and treatment."
+    
+    return advice
+
+# Custom CSS for better styling
+custom_css = """
+.gradio-container {
+    font-family: 'Arial', sans-serif;
+    max-width: 1200px;
+    margin: 0 auto;
+}
+
+.medical-disclaimer {
+    background-color: #fff3cd;
+    border: 1px solid #ffeaa7;
+    border-radius: 8px;
+    padding: 15px;
+    margin: 10px 0;
+    color: #856404;
+}
+
+.prediction-box {
+    background-color: #f8f9fa;
+    border-radius: 8px;
+    padding: 15px;
+    margin: 10px 0;
+}
+"""
+
+# Create Gradio interface
+with gr.Blocks(
+    css=custom_css,
+    title="DermaAI - Skin Disease Classification",
+    theme=gr.themes.Soft()
+) as demo:
+    
+    gr.HTML("""
+    <div style="text-align: center; margin-bottom: 20px;">
+        <h1>🏥 DermaAI - Skin Disease Classification</h1>
+        <p style="font-size: 18px; color: #666;">
+            AI-powered skin condition analysis using deep learning
+        </p>
+    </div>
+    """)
+    
+    gr.HTML("""
+    <div class="medical-disclaimer">
+        <h3>⚕️ Important Medical Disclaimer</h3>
+        <p><strong>This AI tool is for educational and research purposes only.</strong> 
+        It should not be used as a substitute for professional medical diagnosis or treatment. 
+        Always consult with qualified healthcare professionals for proper medical evaluation.</p>
+    </div>
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.HTML("<h3>📸 Upload Skin Image</h3>")
+            input_image = gr.Image(
+                type="pil",
+                label="Upload a clear image of the skin condition",
+                height=400
+            )
+            
+            gr.HTML("""
+            <div style="margin-top: 15px; padding: 10px; background-color: #e3f2fd; border-radius: 5px;">
+                <h4>📋 Image Guidelines:</h4>
+                <ul>
+                    <li>Use good lighting and focus</li>
+                    <li>Ensure the affected area is clearly visible</li>
+                    <li>Avoid blurry or dark images</li>
+                    <li>JPG, PNG formats supported</li>
+                </ul>
+            </div>
+            """)
+        
+        with gr.Column(scale=1):
+            gr.HTML("<h3>🔍 Analysis Results</h3>")
+            
+            prediction_output = gr.Label(
+                label="Prediction Confidence Scores",
+                num_top_classes=5
+            )
+            
+            medical_advice = gr.Markdown(
+                label="Medical Assessment",
+                value="Upload an image to see the analysis..."
+            )
+    
+    gr.HTML("""
+    <div style="margin-top: 20px; padding: 15px; background-color: #f0f8ff; border-radius: 8px;">
+        <h3>🩺 Supported Skin Conditions</h3>
+        <div style="display: grid; grid-template-columns: repeat(auto-fit, minmax(300px, 1fr)); gap: 15px; margin-top: 10px;">
+            <div><strong>Atopic Dermatitis:</strong> Chronic inflammatory skin condition</div>
+            <div><strong>Eczema:</strong> Red, itchy, inflamed skin patches</div>
+            <div><strong>Psoriasis:</strong> Thick, scaly skin patches</div>
+            <div><strong>Seborrheic Keratoses:</strong> Benign skin growths</div>
+            <div><strong>Tinea Ringworm Candidiasis:</strong> Fungal skin infections</div>
+        </div>
+    </div>
+    """)
+    
+    # Set up the interface interactions
+    input_image.change(
+        fn=predict_skin_condition,
+        inputs=input_image,
+        outputs=prediction_output
+    )
+    
+    input_image.change(
+        fn=get_medical_advice,
+        inputs=input_image,
+        outputs=medical_advice
+    )
+    
+    gr.HTML("""
+    <div style="text-align: center; margin-top: 30px; padding: 20px; background-color: #f8f9fa; border-radius: 8px;">
+        <h3>📚 About DermaAI</h3>
+        <p>DermaAI is built using EfficientNetV2 architecture and trained on dermatological images. 
+        The model analyzes skin images and provides confidence scores for 5 different skin conditions.</p>
+        <p><strong>Model:</strong> <a href="https://huggingface.co/Siraja704/DermaAI" target="_blank">Siraja704/DermaAI</a></p>
+        <p><strong>Framework:</strong> TensorFlow/Keras | <strong>Architecture:</strong> EfficientNetV2</p>
+    </div>
+    """)
+
+# Launch the interface
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )