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import datasets | |
import gradio as gr | |
from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
import torch | |
dataset = datasets.load_dataset("beans") | |
feature_extractor = AutoFeatureExtractor.from_pretrained("saved_model_files") | |
model = AutoModelForImageClassification.from_pretrained("saved_model_files") | |
labels = dataset['train'].features['labels'].names | |
example_imgs = ["example_0.jpg", "example_1.jpg","example_2.jpg"] | |
def classify(im): | |
features = feature_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 = "Plant Leaf Disease Classifier", | |
description = """Below is a simple app to detect Angular Leaf Spot and Bean Rust diseases on leaves. | |
Data was annotated by experts from the National Crops Resources Research Institute (NaCRRI) | |
in Uganda and collected by the Makerere AI research lab. | |
The model being used is a fine-tuned Vision Transformer, specifically beginning with [google/vit-base-patch16-224] | |
(https://huggingface.co/google/vit-base-patch16-224) and trained using the [beans](https://huggingface.co/datasets/beans) dataset. | |
This app was created in Abubakar Abid's 'Building End-to-End Vision Applications' course through CoRise. | |
""", | |
examples = example_imgs) | |
interface.launch(debug=True) | |