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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"}