| import gradio as gr | |
| from fastai.vision.all import * | |
| import skimage | |
| learn = load_learner('model_moblenet.h5') | |
| 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))} | |
| import os | |
| for root, dirs, files in os.walk(r'sample_images/'): | |
| for filename in files: | |
| print(filename) | |
| title = "Rice Disease Classifier" | |
| description = "A rice disease classifier that can detect 3 diseases - blast, blight, tungro" | |
| interpretation='default' | |
| examples = ["sample_images/"+file for file in files] | |
| article="<p style='text-align: center'><a href='https://dicksonneoh.com/' target='_blank'>Blog post</a></p>" | |
| enable_queue=True | |
| gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(384, 384)),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() | |