app.py

from fastapi import FastAPI, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
import torch
import timm
import json
from PIL import Image
import io
from torchvision import transforms
from huggingface_hub import hf_hub_download

app = FastAPI()

# CORS عشان الـ React يقدر يكلم الـ API
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

# تحميل الموديل
REPO_ID = "abdallah110/plant-disease-model"
device  = torch.device("cpu")

print("⏳ Loading model...")
model_path       = hf_hub_download(repo_id=REPO_ID, filename="final_model.pth")
class_names_path = hf_hub_download(repo_id=REPO_ID, filename="class_names.json")

with open(class_names_path) as f:
    class_names = json.load(f)

model = timm.create_model("convnext_tiny", pretrained=False, num_classes=len(class_names))
model.load_state_dict(torch.load(model_path, map_location=device))
model.eval()
print("✅ Model loaded!")

# Transforms
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406],
                         [0.229, 0.224, 0.225]),
])

@app.get("/")
def root():
    return {"status": "ok", "message": "Plant Disease API is running 🌿"}

@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    contents = await file.read()
    img = Image.open(io.BytesIO(contents)).convert("RGB")

    tensor = transform(img).unsqueeze(0).to(device)
    with torch.no_grad():
        outputs = model(tensor)
        probs   = torch.softmax(outputs, dim=1)[0]
        top5    = torch.topk(probs, 5)

    results = [
        {
            "class": class_names[idx.item()],
            "confidence": round(prob.item() * 100, 2)
        }
        for prob, idx in zip(top5.values, top5.indices)
    ]

    return {
        "prediction": results[0]["class"],
        "confidence": results[0]["confidence"],
        "top5": results
    }