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| import gradio as gr | |
| from transformers import BeitFeatureExtractor, BeitForImageClassification | |
| from PIL import Image | |
| import requests | |
| import numpy as np | |
| # Load the pre-trained BEiT model and feature extractor | |
| feature_extractor = BeitFeatureExtractor.from_pretrained('microsoft/beit-large-patch16-512') | |
| model = BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-512') | |
| def classify_image(input_image): | |
| image = Image.fromarray(input_image.astype('uint8')) | |
| inputs = feature_extractor(images=image, return_tensors="pt") | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| predicted_class_idx = logits.argmax(-1).item() | |
| predicted_class = model.config.id2label[predicted_class_idx] | |
| return {"Predicted Class": predicted_class} | |
| iface = gr.Interface( | |
| fn=classify_image, | |
| inputs=gr.inputs.Image(type="numpy"), # Specify input type as numpy array | |
| outputs="json", | |
| live=True, | |
| title="BEiT Classification", | |
| description="Upload an image and you will get a description" | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |