app.py

import gradio as gr
from fastai.vision.all import *
import skimage

# Load the trained model
learn = load_learner('ripeorrotten_apple.pkl')

# Define the labels for your model
labels = ['ripe', 'rotten']

# Define the prediction function
def predict(img):
    img = PILImage.create(img)
    pred, pred_idx, probs = learn.predict(img)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}

# Customize the Gradio interface
title = "Apple Ripeness Classifier"
description = "Is your apple ripe or rotten? Use this AQCC (apple quality control classifier) trained on web images with fastai."
article = "<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"

# Example images (ensure these are valid paths or URLs)
examples = ['apple-fruit-ripe.jpg']
interpretation = 'default'
enable_queue = True

# Create and launch the Gradio interface
gr.Interface(
    fn=predict,
    inputs=gr.inputs.Image(shape=(512, 512)),
    outputs=gr.outputs.Label(num_top_classes=2),
    title=title,
    description=description,
    article=article,
    examples=examples,
    interpretation=interpretation,
    enable_queue=enable_queue
).launch()