app.py

from fastapi import FastAPI, File, UploadFile
import tensorflow as tf
import numpy as np
from PIL import Image
import io

app = FastAPI()

# Load model
model = tf.keras.models.load_model("saved_model")

def preprocess(image):
    image = image.resize((224, 224))
    image = np.array(image) / 255.0
    return np.expand_dims(image, axis=0)

@app.get("/")
def root():
    return {"message": "Model is running!"}

@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    image = Image.open(io.BytesIO(await file.read())).convert("RGB")
    input_data = preprocess(image)
    
    prediction = model.predict(input_data)
    
    return {
        "prediction": prediction.tolist()
    }