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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() | |