Upload app.py

app.py

@@ -0,0 +1,157 @@
+import gradio as gr
+import numpy as np
+from PIL import Image
+import torch
+import torch.nn.functional as F
+
+# Temporary placeholder function - replace with your actual model later
+def predict_skin_lesion(image):
+    """
+    Placeholder prediction function
+    Replace this with your actual ResNet50 model inference
+    """
+    
+    # Skin lesion classes
+    classes = [
+        "Actinic Keratoses",
+        "Basal Cell Carcinoma", 
+        "Benign Keratosis-like Lesions",
+        "Dermatofibroma",
+        "Melanocytic Nevi (Normal)",
+        "Melanoma",
+        "Vascular Lesions"
+    ]
+    
+    # Placeholder prediction (random for now)
+    # TODO: Replace with actual model.predict(image)
+    confidence_scores = np.random.dirichlet(np.ones(len(classes)), size=1)[0]
+    
+    # Get top prediction
+    top_prediction_idx = np.argmax(confidence_scores)
+    top_class = classes[top_prediction_idx]
+    top_confidence = float(confidence_scores[top_prediction_idx])
+    
+    # Create confidence dictionary for gradio
+    predictions = {classes[i]: float(confidence_scores[i]) for i in range(len(classes))}
+    
+    # Risk assessment based on prediction
+    if top_class in ["Melanoma", "Basal Cell Carcinoma"] and top_confidence > 0.6:
+        risk_level = "⚠️ YÜKSEK RİSK - Dermatolog konsültasyonu önerilir"
+        risk_color = "#FF6B6B"
+    elif top_confidence > 0.4:
+        risk_level = "⚡ ORTA RİSK - Takip önerilir"  
+        risk_color = "#FFE66D"
+    else:
+        risk_level = "✅ DÜŞÜK RİSK - Rutin kontrol"
+        risk_color = "#4ECDC4"
+    
+    return predictions, f"<div style='padding: 10px; background-color: {risk_color}; border-radius: 5px; color: white; font-weight: bold;'>{risk_level}</div>"
+
+# Custom CSS for better UI
+css = """
+.gradio-container {
+    max-width: 900px;
+    margin: 0 auto;
+}
+
+.title {
+    text-align: center;
+    color: #2E86AB;
+    margin-bottom: 20px;
+}
+
+.description {
+    text-align: center;
+    color: #666;
+    margin-bottom: 30px;
+}
+
+.risk-box {
+    margin-top: 15px;
+    text-align: center;
+}
+"""
+
+# Create Gradio interface
+with gr.Blocks(css=css, title="Cilt Lezyonu AI Analizi") as demo:
+    
+    # Header
+    gr.Markdown(
+        """
+        # 🔬 Cilt Lezyonu AI Analizi
+        ### Yapay zeka destekli cilt kanseri erken teşhis sistemi
+        
+        **Nasıl kullanılır:**
+        1. Cilt lezyonu fotoğrafını yükleyin
+        2. AI analizi bekleyin (30 saniye)
+        3. Sonuçları değerlendirin
+        4. Gerekirse uzman hekime başvurun
+        
+        ⚠️ **Uyarı:** Bu sistem sadece ön değerlendirme amaçlıdır. Kesin tanı için mutlaka doktora başvurun.
+        """,
+        elem_classes=["description"]
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            # Input
+            input_image = gr.Image(
+                label="📸 Cilt Lezyonu Fotoğrafı",
+                type="pil",
+                elem_id="input-image"
+            )
+            
+            # Analyze button
+            analyze_btn = gr.Button(
+                "🔍 Analiz Et", 
+                variant="primary",
+                size="lg"
+            )
+            
+        with gr.Column(scale=1):
+            # Outputs
+            prediction_output = gr.Label(
+                label="📊 AI Tahmin Sonuçları",
+                num_top_classes=7
+            )
+            
+            risk_output = gr.HTML(
+                label="⚠️ Risk Değerlendirmesi"
+            )
+    
+    # Examples section
+    gr.Markdown("### 📋 Örnek Görüntüler (Test için)")
+    gr.Examples(
+        examples=[
+            # Add example image paths here later
+            # ["example_melanoma.jpg"],
+            # ["example_nevus.jpg"], 
+        ],
+        inputs=input_image,
+        label="Örnek görüntülere tıklayın"
+    )
+    
+    # Footer
+    gr.Markdown(
+        """
+        ---
+        **Geliştiren:** AI Healthcare Team | **Teknoloji:** ResNet50 + Transfer Learning | **Dataset:** HAM10000
+        
+        📞 **Acil Durum:** 112 | 🏥 **Dermatoloji Kliniği:** [Randevu Al](https://www.turkiye.gov.tr)
+        """
+    )
+    
+    # Set up the interface interaction
+    analyze_btn.click(
+        fn=predict_skin_lesion,
+        inputs=input_image,
+        outputs=[prediction_output, risk_output]
+    )
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch(
+        share=True,
+        server_name="0.0.0.0",
+        server_port=7860
+    )