Update app.py

app.py

@@ -3,42 +3,56 @@ from fastapi.responses import JSONResponse
 from transformers import pipeline
 from PIL import Image
 import io
-
-# Inisialisasi FastAPI
-app = FastAPI(title="🌿 Plant Disease API (ResNet50)",
-              description="API untuk deteksi penyakit tanaman menggunakan ResNet50 dari Hugging Face.",
-              version="1.0")
-
-# Load model dari Hugging Face
-model = pipeline("image-classification", model="SanketJadhav/PlantDiseaseClassifier-Resnet50")
-
-@app.on_event("startup")
-async def load_model():
-    print("✅ Model ResNet50 siap digunakan (CPU mode).")
-
-@app.get("/")
-async def root():
-    return {"message": "🌿 Plant Disease API is running"}
+from contextlib import asynccontextmanager
+
+# Gunakan lifespan event handler (pengganti @app.on_event)
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global model
+    print("🚀 Memuat model ResNet50 dari Hugging Face...")
+    model = pipeline(
+        "image-classification",
+        model="SanketJadhav/PlantDiseaseClassifier-Resnet50",
+        feature_extractor_kwargs={"use_fast": True}  # hilangkan warning slow processor
+    )
+    print("✅ Model siap digunakan (CPU mode)")
+    yield
+    print("🧹 Server FastAPI dimatikan.")
+
+app = FastAPI(lifespan=lifespan)
 
 @app.post("/predict")
 async def predict(file: UploadFile = File(...)):
     try:
-        # Baca file gambar
-        image_data = await file.read()
-        image = Image.open(io.BytesIO(image_data)).convert("RGB")
+        # Baca file gambar dari request
+        image_bytes = await file.read()
+        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
 
-        # Prediksi
+        # Jalankan prediksi
         results = model(image)
 
-        # Ambil 3 prediksi teratas
-        top3 = sorted(results, key=lambda x: x['score'], reverse=True)[:3]
+        # Ambil 3 hasil teratas
+        top_results = sorted(results, key=lambda x: x['score'], reverse=True)[:3]
+        formatted = [
+            {"label": res['label'], "score": round(res['score'], 3)}
+            for res in top_results
+        ]
 
-        response = {
-            "filename": file.filename,
-            "predictions": top3
-        }
-
-        return JSONResponse(content=response)
+        return JSONResponse({
+            "status": "success",
+            "predictions": formatted
+        })
 
     except Exception as e:
-        return JSONResponse(content={"error": str(e)}, status_code=500)
+        return JSONResponse({
+            "status": "error",
+            "message": str(e)
+        }, status_code=500)
+
+@app.get("/")
+async def root():
+    return {"message": "🌱 Plant Disease API is running!"}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)