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import gradio as gr
from fastai.vision.all import *
import skimage
import os
learn = load_learner('learner.pkl')
labels = learn.dls.vocab
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))}
for root, dirs, files in os.walk(r'sample_images/'):
for filename in files:
print(filename)
title = "Paddy Disease Classifier with EdgeNeXt"
description = "9 Diseases + 1 Normal class."
interpretation='default'
examples = ["sample_images/"+file for file in files]
article="<p style='text-align: center'><a href='https://dicksonneoh.com/portfolio/bringing_high_quality_image_models_to_mobile/' target='_blank'>Blog post</a></p>"
enable_queue=True
gr.Interface(
fn=predict,
inputs=gr.inputs.Image(shape=(224, 224)),
outputs=gr.outputs.Label(num_top_classes=3),
title=title,
description=description,
article=article,
examples=examples,
interpretation=interpretation,
enable_queue=enable_queue,
theme="grass",
).launch() |