Update app.py

app.py

@@ -1,56 +1,84 @@
-from fastapi import FastAPI, UploadFile, File
+from fastapi import FastAPI, UploadFile, HTTPException
 from fastapi.responses import JSONResponse
+import uvicorn
 import torch
+import torch.nn as nn
+from torchvision.models import vgg19
 from torchvision import transforms
 from PIL import Image
 import io
-from huggingface_hub import hf_hub_download
 
-# FastAPI uygulamasını başlat
+# FastAPI uygulaması
 app = FastAPI()
 
-# Cihaz ayarı
+# Modeli yükle ve ayarla
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# Hugging Face modelini yükleme
-REPO_ID = "kedimestan/retinoblastomaDetectionVGG19"
-MODEL_FILE = "best_model.pth"
+def load_model():
+    model = vgg19(pretrained=False)  # Pretrained ağırlıklar olmadan model
+    model.classifier = nn.Sequential(
+        nn.Linear(25088, 12544),  # 25088 -> 12544
+        nn.ReLU(),
+        nn.Dropout(0.5),
+        nn.Linear(12544, 6272),   # 12544 -> 6272
+        nn.ReLU(),
+        nn.Dropout(0.5),
+        nn.Linear(6272, 3136),    # 6272 -> 3136
+        nn.ReLU(),
+        nn.Dropout(0.4),
+        nn.Linear(3136, 1568),    # 3136 -> 1568
+        nn.ReLU(),
+        nn.Dropout(0.4),
+        nn.Linear(1568, 784),     # 1568 -> 784
+        nn.ReLU(),
+        nn.Dropout(0.3),
+        nn.Linear(784, 392),      # 784 -> 392
+        nn.ReLU(),
+        nn.Dropout(0.3),
+        nn.Linear(392, 196),      # 392 -> 196
+        nn.ReLU(),
+        nn.Dropout(0.2),
+        nn.Linear(196, 98),       # 196 -> 98
+        nn.ReLU(),
+        nn.Dropout(0.2),
+        nn.Linear(98, 49),        # 98 -> 49
+        nn.ReLU(),
+        nn.Dropout(0.1),
+        nn.Linear(49, 1),         # 49 -> 1 (Binary classification)
+        nn.Sigmoid()
+    )
+    model = model.to(device)
+    model.load_state_dict(torch.load("best_model.pth", map_location=device))
+    model.eval()
+    return model
 
-# Model dosyasını indirin
-model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILE)
-model = torch.load(model_path, map_location=device)
-model.eval()
+model = load_model()
 
-# Görüntü dönüşüm pipeline'ı
+# Görsel işleme transformasyonu
 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
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
 ])
 
-# Tahmin fonksiyonu
-def predict(image: Image.Image):
-    input_tensor = transform(image).unsqueeze(0).to(device)
-    with torch.no_grad():
-        output = model(input_tensor).squeeze(0).cpu().numpy()
-        prediction = "Positive" if output[0] > 0.5 else "Negative"
-        return {"Prediction": prediction, "Probability": round(float(output[0]), 2)}
-
-# Ana API rotası
-@app.post("/predict")
-async def predict_image(file: UploadFile = File(...)):
+def process_image(file: UploadFile):
     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)
+        image = Image.open(io.BytesIO(file.file.read())).convert("RGB")
+        return transform(image).unsqueeze(0).to(device)
     except Exception as e:
-        return JSONResponse(content={"error": str(e)}, status_code=400)
+        raise HTTPException(status_code=400, detail=f"Invalid image file: {str(e)}")
+
+# Ana endpoint
+@app.post("/predict")
+async def predict(file: UploadFile):
+    if not file.content_type.startswith("image/"):
+        raise HTTPException(status_code=400, detail="File must be an image.")
+    image_tensor = process_image(file)
+    with torch.no_grad():
+        output = model(image_tensor)
+        prediction = float(output.item())
+    return JSONResponse({"prediction": prediction})
 
-# Ana sayfa
-@app.get("/")
-def home():
-    return {"message": "Upload an image to /predict for classification."}
+# Uygulama başlatıcı
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)