Daniel Meyer
launch app
11328f2
import datasets
import torch
import gradio as gr
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: gr.inputs.Image) -> dict:
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(fn=classify,
inputs="image",
outputs="label",
title="Bean classifier",
description="Web-application that can take in an image of a bean leaf and predict whether it is healthy or diseased",
examples = ['./images/img_0.png'])
interface.launch(debug=True)