app.py

import gradio as gr
from transformers import pipeline

# Load the model for food classification
food_classifier = pipeline(task="image-classification", model="mhdiqbalpradipta/minang_food_classification")

def predict_food(image):
    # Perform prediction using the model
    result = food_classifier(images=image)[0]

    # Save label and score
    food_label = result['label']
    score = result['score']
    return f"Food: {food_label}, Score: {score:.2f}"

# Gradio Interface
image_in = gr.Image(type='pil')
label_out = "text"
example_images = ['ayam_goreng.jpg', 'ayam_pop.jpg', 'daging_rendang.jpg', 'dendeng_batokok.jpg', 'gulai_ikan.jpg', 'gulai_tambusu.jpg', 'gulai_tunjang.jpg', 'telur_balado.jpg', 'telur_dadar.jpg']

intf = gr.Interface(fn=predict_food, inputs=image_in, outputs=label_out, examples=example_images, title="Minang Food Classifier", description="Upload an image of food to classify it into Minang dishes.")
intf.launch(share=False);