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import gradio as gr
import torch
from huggingface_hub import from_pretrained_fastai
from pathlib import Path
examples = ["examples/example_0.png",
"examples/example_1.png",
"examples/example_2.png",
"examples/example_3.png",
"examples/example_4.png"]
repo_id = "hugginglearners/rice_image_classification"
path = Path("./")
def get_y(r):
return r["label"]
def get_x(r):
return path/r["fname"]
learner = from_pretrained_fastai(repo_id)
labels = learner.dls.vocab
def inference(image):
label_predict, _, probs = learner.predict(image)
labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
return labels_probs
gr.Interface(
fn=inference,
title="Rice Disease Classification",
description="Predict which type of rice disease is affecting the leaf: Tungro, Rice Blast, Bacterial Blight, or Healthy Rice Leaf.",
inputs=gr.Image(),
examples=examples,
outputs=gr.Label(num_top_classes=4, label='Prediction'),
cache_examples=False,
article="Authors: Your Team Name",
).launch(debug=True, enable_queue=True)
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