app.py

import gradio as gr
import torch
import torch.nn as nn
from torchvision import transforms
from torchvision.models import vgg19
from PIL import Image
import io
from huggingface_hub import hf_hub_download

# Cihaz ayarı
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Hugging Face modelini yükleme bilgileri
REPO_ID = "kedimestan/retinoblastomaDetectionVGG19"
MODEL_FILE = "best_model.pth"

# Modeli indir ve yükle
def load_model():
    model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILE)
    
    # Model yapısını yeniden tanımla (VGG19 + özel sınıflandırıcı)
    model = vgg19(pretrained=False)
    model.classifier = nn.Sequential(
        nn.Linear(25088, 12544),
        nn.ReLU(),
        nn.Dropout(0.5),
        nn.Linear(12544, 6272),
        nn.ReLU(),
        nn.Dropout(0.5),
        nn.Linear(6272, 3136),
        nn.ReLU(),
        nn.Dropout(0.4),
        nn.Linear(3136, 1568),
        nn.ReLU(),
        nn.Dropout(0.4),
        nn.Linear(1568, 784),
        nn.ReLU(),
        nn.Dropout(0.3),
        nn.Linear(784, 392),
        nn.ReLU(),
        nn.Dropout(0.3),
        nn.Linear(392, 196),
        nn.ReLU(),
        nn.Dropout(0.2),
        nn.Linear(196, 98),
        nn.ReLU(),
        nn.Dropout(0.2),
        nn.Linear(98, 49),
        nn.ReLU(),
        nn.Dropout(0.1),
        nn.Linear(49, 1),
        nn.Sigmoid()
    )
    
    # Ağırlıkları yükle (state_dict), modeli yüklemeden önce
    model.load_state_dict(torch.load(model_path, map_location=device))
    
    # Cihazda modele yükle
    model = model.to(device)
    model.eval()
    return model

model = load_model()

# Görüntü dönüşüm pipeline'ı
transform = transforms.Compose([
    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 = "Normal" if output[0] > 0.5 else "Hasta"
        return prediction

# Gradio arayüzü
inputs = gr.Image(type="pil", label="Görsel Yükle")
outputs = gr.Textbox(label="Tahmin Sonucu")

gr.Interface(
    fn=predict,
    inputs=inputs,
    outputs=outputs,
    title="Retinoblastoma Tespiti",
    theme="default"
).launch(debug=True)