Update app.py

app.py

@@ -1,5 +1,4 @@
-from fastapi import FastAPI, UploadFile, File, HTTPException
-from fastapi.responses import JSONResponse
+import gradio as gr
 import torch
 import torch.nn as nn
 from torchvision import transforms
@@ -8,9 +7,6 @@ from PIL import Image
 import io
 from huggingface_hub import hf_hub_download
 
-# FastAPI uygulamasını başlat
-app = FastAPI()
-
 # Cihaz ayarı
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
@@ -81,21 +77,12 @@ def predict(image: Image.Image):
         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(...)):
-    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)
+# Gradio arayüzü
+def gradio_interface(image):
+    return predict(image)
+
+# Gradio arayüzü oluşturma
+inputs = gr.Image(type="pil", label="Upload Image")
+outputs = gr.JSON(label="Prediction Result")
 
-# Ana sayfa
-@app.get("/")
-def home():
-    return {"message": "Upload an image to /predict for classification."}
+gr.Interface(fn=gradio_interface, inputs=inputs, outputs=outputs, title="Retinoblastoma Detection", theme="default").launch(debug=True)