app.py

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

# Load model and classes
model = tf.keras.models.load_model("hf_keras_model.keras")
class_names = ['buildings', 'forest', 'glacier', 'mountain', 'sea', 'street']

# Initialize app
app = FastAPI()

# Allow all CORS (for frontend/test requests)
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

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

@app.post("/predict")
async def predict(file: UploadFile = File(...)):
    # Load image
    image = Image.open(file.file).convert("RGB").resize((150, 150))
    img_array = np.array(image) / 255.0
    img_array = np.expand_dims(img_array, axis=0)

    # Predict
    predictions = model.predict(img_array)[0]
    results = {class_names[i]: float(predictions[i]) for i in range(len(class_names))}
    top_class = class_names[np.argmax(predictions)]

    return {"top_prediction": top_class, "all_predictions": results}