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from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
from transformers import pipeline
from PIL import Image
import io
from contextlib import asynccontextmanager
@asynccontextmanager
async def lifespan(app: FastAPI):
global model
print("🚀 Memuat model ResNet50 dari Hugging Face...")
# Muat model tanpa argumen tambahan
model = pipeline(
"image-classification",
model="SanketJadhav/PlantDiseaseClassifier-Resnet50"
)
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 dari request
image_bytes = await file.read()
image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
# Jalankan prediksi
results = model(image)
# 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
]
return JSONResponse({
"status": "success",
"predictions": formatted
})
except Exception as e:
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)
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