Update app.py

app.py

@@ -1,157 +1,182 @@
-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
+import gradio as gr
+import torch
+from transformers import ViTForImageClassification, ViTImageProcessor
+from PIL import Image
+import numpy as np
+
+# Senin model'inin sınıfları (config'den)
+class_names = {
+    0: "Enfeksiyonel",
+    1: "Ekzama",
+    2: "Akne", 
+    3: "Pigment",
+    4: "Benign",
+    5: "Malign",
+    6: "Acne",
+    7: "Actinic Keratosis",
+    8: "Basal Cell Carcinoma",
+    9: "Benign Keratosis",
+    10: "Dermatofibroma",
+    11: "Melanocytic Nevus",
+    12: "Melanoma",
+    13: "Vascular Lesion",
+    14: "Warts/Molluscum"
+}
+
+# Risk assessment mapping
+risk_categories = {
+    "high_risk": [5, 12, 8],  # Malign, Melanoma, Basal Cell Carcinoma
+    "medium_risk": [7, 0, 13],  # Actinic Keratosis, Enfeksiyonel, Vascular
+    "low_risk": [1, 2, 3, 4, 6, 9, 10, 11, 14]  # Diğerleri
+}
+
+@gr.cache
+def load_model():
+    """Senin trained ViT modelini yükle"""
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    
+    # Model ve processor yükle (local files'dan)
+    model = ViTForImageClassification.from_pretrained("./", local_files_only=True)
+    processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
+    
+    model.to(device)
+    model.eval()
+    
+    return model, processor, device
+
+def predict_skin_condition(image):
+    """Ana prediction fonksiyonu"""
+    
+    if image is None:
+        return {}, "Lütfen bir görüntü yükleyin"
+    
+    try:
+        # Model yükle
+        model, processor, device = load_model()
+        
+        # Image preprocessing
+        inputs = processor(images=image, return_tensors="pt")
+        inputs = {k: v.to(device) for k, v in inputs.items()}
+        
+        # Prediction
+        with torch.no_grad():
+            outputs = model(**inputs)
+            logits = outputs.logits
+            probabilities = torch.softmax(logits, dim=-1)[0]
+        
+        # Results dictionary oluştur
+        results = {}
+        for idx, prob in enumerate(probabilities):
+            if idx in class_names:
+                results[class_names[idx]] = float(prob)
+        
+        # En yüksek tahmin
+        top_pred_idx = torch.argmax(probabilities).item()
+        top_class = class_names[top_pred_idx]
+        confidence = float(probabilities[top_pred_idx])
+        
+        # Risk assessment
+        risk_html = get_risk_assessment(top_pred_idx, confidence, top_class)
+        
+        return results, risk_html
+        
+    except Exception as e:
+        return {}, f"Hata oluştu: {str(e)}"
+
+def get_risk_assessment(pred_idx, confidence, class_name):
+    """Risk değerlendirmesi yap"""
+    
+    if pred_idx in risk_categories["high_risk"] and confidence > 0.7:
+        risk_level = "🚨 YÜKSEK RİSK"
+        message = f"<strong>{class_name}</strong> tespit edildi. Dermatolog konsültasyonu ÖNERİLİR."
+        color = "#FF4444"
+    elif pred_idx in risk_categories["medium_risk"] and confidence > 0.5:
+        risk_level = "⚠️ ORTA RİSK"
+        message = f"<strong>{class_name}</strong> tespit edildi. Takip önerilir."
+        color = "#FF8800"
+    else:
+        risk_level = "✅ DÜŞÜK RİSK"
+        message = f"<strong>{class_name}</strong> tespit edildi. Rutin kontrol yeterli."
+        color = "#00AA44"
+    
+    return f"""
+    <div style='padding: 15px; background-color: {color}; color: white; border-radius: 10px; text-align: center; margin: 10px 0;'>
+        <h3 style='margin: 0 0 10px 0;'>{risk_level}</h3>
+        <p style='margin: 0; font-size: 16px;'>{message}</p>
+        <p style='margin: 5px 0 0 0; font-size: 14px;'>Güven Skoru: {confidence:.1%}</p>
+    </div>
+    """
+
+# CSS styling
+css = """
+.gradio-container {
+    max-width: 1000px;
+    margin: 0 auto;
+    font-family: 'Segoe UI', sans-serif;
+}
+.title {
+    text-align: center;
+    color: #2E86AB;
+    margin-bottom: 20px;
+}
+"""
+
+# Gradio interface
+with gr.Blocks(css=css, title="ViT Cilt Hastalığı Analizi") as demo:
+    
+    gr.Markdown("""
+    # 🔬 ViT Cilt Hastalığı AI Analizi
+    ### 15 farklı cilt hastalığını tespit eden yapay zeka sistemi
+    **Accuracy: %97 | Model: Vision Transformer | Classes: 15**
+    
+    ---
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_image = gr.Image(
+                label="📸 Cilt Görüntüsü Yükleyin",
+                type="pil",
+                height=400
+            )
+            
+            predict_btn = gr.Button(
+                "🔍 AI Analizi Başlat",
+                variant="primary", 
+                size="lg"
+            )
+            
+        with gr.Column(scale=1):
+            prediction_output = gr.Label(
+                label="📊 Tahmin Sonuçları",
+                num_top_classes=5
+            )
+            
+            risk_output = gr.HTML(
+                label="⚠️ Risk Değerlendirmesi"
+            )
+    
+    # Sınıf açıklamaları
+    gr.Markdown("""
+    ### 📋 Tespit Edilen Hastalık Kategorileri:
+    
+    **Yüksek Risk:** Malign, Melanoma, Basal Cell Carcinoma  
+    **Orta Risk:** Actinic Keratosis, Enfeksiyonel, Vascular Lesion  
+    **Düşük Risk:** Ekzama, Akne, Pigment, Benign, Dermatofibroma, vb.
+    
+    ⚠️ **Önemli:** Bu sistem sadece ön değerlendirme içindir. Kesin tanı için doktora başvurun.
+    """)
+    
+    # Event handling
+    predict_btn.click(
+        fn=predict_skin_condition,
+        inputs=input_image,
+        outputs=[prediction_output, risk_output]
+    )
+
+if __name__ == "__main__":
+    demo.launch(
+        share=True,
+        server_name="0.0.0.0", 
+        server_port=7860
     )