app.py

import datasets
import gradio as gr
import torch
from transformers import AutoFeatureExtractor, AutoModelForImageClassification

dataset = datasets.load_dataset("beans")
labels = dataset["train"].features["labels"].names

extractor = AutoFeatureExtractor.from_pretrained("RKoops/BeanLeafClassifier")
model = AutoModelForImageClassification.from_pretrained("RKoops/BeanLeafClassifier")


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


interface = gr.Interface(
    classify,
    inputs="image",
    outputs="label",
    title="Bean plant disease classifier",
    description="Detect diseases in beans using images of leaves",
)

interface.launch()