app.py

from fastapi import FastAPI, UploadFile, File
from fastapi.responses import JSONResponse
import tensorflow as tf
from PIL import Image
import numpy as np
import io
from fastapi.middleware.cors import CORSMiddleware

app = FastAPI()

# Allow CORS for all origins (adjust as needed)
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Load model
def load_model():
    return tf.keras.models.load_model("growlens_efficientnet_model.h5")

model = load_model()
class_names = [
    "ants", "bees", "beetle", "catterpillar", "earthworms", "earwig",
    "grasshopper", "moth", "slug", "snail", "wasp", "weevil"
]

@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    image_bytes = await file.read()
    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
    image = image.resize((224, 224))  # Adjust size as per your model
    img_array = np.array(image) / 255.0
    img_array = np.expand_dims(img_array, axis=0)
    preds = model.predict(img_array)
    pred_class = class_names[np.argmax(preds)]
    confidence = float(np.max(preds))
    return JSONResponse({"class": pred_class, "confidence": confidence})