import gradio as gr
import numpy as np
from tensorflow.keras.models import load_model
from PIL import Image

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

def predict_image(img):
    img = img.resize((150, 150))
    img_array = np.array(img) / 255.0
    img_array = np.expand_dims(img_array, axis=0)
    preds = model.predict(img_array)[0]
    confidences = {class_names[i]: float(preds[i]) for i in range(6)}
    return confidences

gr.Interface(
    fn=predict_image,
    inputs=gr.Image(type="pil"),
    outputs=gr.Label(num_top_classes=3),
    title="Intel Image Classifier",
    description="Upload a landscape image and get predictions (buildings, forest, glacier, etc.)"
).launch()