Create app.py

app.py

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+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import JSONResponse
+import torch
+from torchvision import transforms
+from PIL import Image
+import io
+
+# FastAPI uygulamasını başlat
+app = FastAPI()
+
+# Cihaz ayarı
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Eğitilmiş modeli yükleme
+model = torch.load("best_model.pth", map_location=device)
+model.eval()
+
+# Görüntü dönüşüm pipeline'ı
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),  # Modelle uyumlu olacak şekilde yeniden boyutlandır
+    transforms.ToTensor(),          # Tensor'a çevir
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # Normalize et
+])
+
+# Tahmin fonksiyonu
+def predict(image: Image.Image):
+    input_tensor = transform(image).unsqueeze(0).to(device)
+    with torch.no_grad():
+        output = model(input_tensor).item()  # Tahmini al
+        prediction = "Positive" if output > 0.5 else "Negative"
+        return {"Prediction": prediction, "Probability": round(output, 2)}
+
+# Ana API rotası
+@app.post("/predict")
+async def predict_image(file: UploadFile = File(...)):
+    try:
+        # Görüntüyü oku
+        image_data = await file.read()
+        image = Image.open(io.BytesIO(image_data)).convert("RGB")
+        
+        # Tahmin yap
+        result = predict(image)
+        return JSONResponse(content=result)
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)}, status_code=400)
+
+# Ana sayfa
+@app.get("/")
+def home():
+    return {"message": "Upload an image to /predict for classification."}