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import datasets
import transformers
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
import torch
dataset = datasets.load_dataset('beans')
extractor = AutoFeatureExtractor.from_pretrained("lucasdmpp/BeanLeaf")
model = AutoModelForImageClassification.from_pretrained("lucasdmpp/BeanLeaf")
labels = dataset['train'].features['labels'].names
example_imgs = ["example_0.jpg", "example_1.jpg"]
def classify(im):
features = extractor(im, return_tensors='pt')
logits = model(features["pixel_values"])[-1]
probability = torch.nn.functional.softmax(logits, dim=-1)
probs = probability[0].detach().numpy()
confidences = {label: float(probs[i]) for i, label in enumerate(labels)}
return confidences
import gradio as gr
interface = interface = gr.Interface(classify, inputs='image',
outputs='label',
title='Bean Leaf Classification',
description='Check the health of your bean leaves',
examples = example_imgs)
interface.launch(debug=True) |