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import numpy as np
import torch
import datasets
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
dataset = datasets.load_dataset('beans')
extractor = AutoFeatureExtractor.from_pretrained("saved_model_files")
model = AutoModelForImageClassification.from_pretrained("saved_model_files")
labels = dataset['train'].features['labels'].names
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 = gr.Interface(fn=classify, inputs="image", outputs="label",
examples=[
["https://images.unsplash.com/photo-1550147760-44c9966d6bc7?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8Nnx8bGVhZnxlbnwwfHwwfHw%3D&auto=format&fit=crop&w=800&q=60"],
["https://images.unsplash.com/photo-1525498128493-380d1990a112?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MTd8fGxlYWZ8ZW58MHx8MHx8&auto=format&fit=crop&w=800&q=60"],
["https://apps.lucidcentral.org/pppw_v10/images/entities/bean_angular_leaf_spot_216/angularspot1.jpg"],
["https://extension.umn.edu/sites/extension.umn.edu/files/beans-viral-diseases-2.jpg"],
["http://1.bp.blogspot.com/-CcMICF_A1CI/UHKSvTV2k2I/AAAAAAAAHI0/TlFMGU8RpYQ/s1600/DSCF9698.JPG"],
["https://www.garden.eco/wp-content/uploads/2017/12/bean-leaves.jpg"],
["https://apps.lucidcentral.org/pppw_v10/images/entities/bean_angular_leaf_spot_216/angularspot1.jpg"]
],
title="🍃 Bean Leaf Image Classification",
description="Based on a leaf image, the goal is to predict the disease type (Angular Leaf Spot and Bean Rust), if any.",)
interface.launch(debug=True)
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