app.py

# -*- coding: utf-8 -*-
"""app
Automatically generated by Colaboratory.
Original file is located at
    https://colab.research.google.com/drive/1l1CvBkbH9l4Acj95i78ccFHTGV9JVEGF
"""

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

# Load your model
learn = load_learner('export2.pkl')

labels = learn.dls.vocab

# Define your 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))}

# Gradio interface setup
title = "Food Type Classifier"
description = "A food classifier trained on the food images dataset with fastai. Created as a demo for Gradio and HuggingFace Spaces."
article = "<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
examples = ['Food.jpg']
interpretation = 'default'
enable_queue = True

# Update the Interface setup to use the new syntax
gr.Interface(
    fn=predict,
    inputs=gr.Image(),  # Updated line - remove the shape parameter
    outputs=gr.Label(num_top_classes=2),
    title=title,
    description=description,
    article=article,
    examples=examples,
    interpretation=interpretation,
    enable_queue=enable_queue
).launch()