app.py

import gradio as gr
import numpy as np
from keras.models import load_model
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image

# load model
model = load_model("intel_model.keras")

# class labels (same order as training)
class_labels = ['buildings', 'forest', 'glacier', 'mountain', 'sea', 'street']

def predict(img):
    img = img.resize((150,150))
    img_array = np.array(img) / 255.0
    img_array = np.expand_dims(img_array, axis=0)

    pred = model.predict(img_array)
    return class_labels[np.argmax(pred)]

# UI
gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),
    outputs="label",
    title="Intel Image Classifier"
).launch()