app.py

import os
import tensorflow as tf
from tensorflow import keras
import gradio as gr
import numpy as np
import cv2
from huggingface_hub import hf_hub_download

# Load the model
model_path = hf_hub_download(repo_id="jamesywu5/cifar10_model", filename="image_classifier.keras")
model = tf.keras.models.load_model(model_path)

class_names = ['Airplane', 'Car', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck']

def predict_fn(img):
    img = cv2.resize(img, (32, 32)) / 255.0
    img = np.expand_dims(img, axis=0)
    preds = model.predict(img)[0]
    return {class_names[i]: float(preds[i]) for i in range(len(class_names))}

interface = gr.Interface(fn=predict_fn, inputs=gr.Image(), outputs=gr.Label())
interface.launch(inline=False,share=True)