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| import gradio as gr | |
| import requests | |
| from datasets import load_dataset | |
| from PIL import Image | |
| from transformers import AutoFeatureExtractor, AutoModelForImageClassification | |
| import requests | |
| dataset = load_dataset("hamdan07/UltraSound-lung") | |
| API_URL = "https://api-inference.huggingface.co/models/hamdan07/UltraSound-Lung" | |
| headers = {"Authorization": "Bearer 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"} | |
| extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224") | |
| model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224") | |
| def query(filename): | |
| with open(filename, "rb") as f: | |
| data = f.read() | |
| response = requests.post(API_URL, headers=headers, data=data) | |
| return response.json() | |
| example_list = [['examples/cov1.png'], | |
| ['examples/cov2.jpg'], | |
| ['examples/nor1.jpg'], | |
| ['examples/nor2.jpg'], | |
| ['examples/penu1.jpg'], | |
| ['examples/penu2.jpg']] | |
| title ="<p align='center'>COVID-19 Detection in Ultrasound with Transformer</p>" | |
| description ="<p style='text-align: center'> Trained on 300 data using Hugging Face dataset<br>It been traied using google/vit-base-patch16-224 </p><p style='text-align: center'>Link for the resource! <br> <a href='https://huggingface.co/datasets/hamdan07/UltraSound-lung' target='_blank'>Hugging Face Dataset</a> |<a href='https://huggingface.co/google/vit-base-patch16-224' target='_blank'>Model</a> | <a href='https://github.com/hamdanhh07/UltraSound-Lung' target='_blank'>github</a></p>" | |
| gr.Interface.load("models/hamdan07/UltraSound-Lung",examples=example_list,title=title,description=description).launch(debug=False,share=False) | |