model.py

import torch
from fastapi import FastAPI
import torchvision.transforms as transforms
from pydantic import BaseModel
from PIL import Image
import io
import base64

# Load model
model = torch.load("rice-recognizer-vgg16-v1.pkl")
model.eval()

app = FastAPI()

class ImageData(BaseModel):
    image: str

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]),
])

@app.post("/predict/")
async def predict(data: ImageData):
    img_bytes = io.BytesIO(base64.b64decode(data.image))
    img = Image.open(img_bytes).convert("RGB")
    img_tensor = transform(img).unsqueeze(0)
    with torch.no_grad():
        outputs = model(img_tensor)
        _, predicted = torch.max(outputs, 1)
    return {"prediction": predicted.item()}