import gradio as gr import requests from datasets import load_dataset from transformers import AutoFeatureExtractor, AutoModelForImageClassification from transformers import ViTFeatureExtractor, ViTForImageClassification from PIL import Image import requests extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224") dataset = load_dataset("hamdan07/UltraSound-lung") image = Image.open(requests.get(dataset, stream=True).raw) feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224') model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) API_URL = "https://api-inference.huggingface.co/models/hamdan07/UltraSound-Lung" headers = {"Authorization": "Bearer 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"} 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 ="
COVID-19 Detection in Ultrasound with Timesformer
" description =" Trained on 500 data using Hugging Face dataset
It been traied using google/vit-base-patch16-224
Link for the resource!
Hugging Face Dataset |Model | github