| import torch | |
| from transformers import ViTForImageClassification, ViTImageProcessor | |
| from PIL import Image | |
| import gradio as gr | |
| model = ViTForImageClassification.from_pretrained('Tirath5504/IPD-Image-ViT-Finetune') | |
| processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224') | |
| class_names = ['cut_throat_gesture', 'finger_gun_to_the_head', 'middle_finger', 'slanted_eyes_gesture', 'swastika'] | |
| def predict(image): | |
| inputs = processor(images=image, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model(**inputs).logits | |
| predicted_class_idx = outputs.argmax(-1).item() | |
| predicted_class = class_names[predicted_class_idx] | |
| return predicted_class | |
| iface = gr.Interface(fn=predict, | |
| inputs=gr.Image(type="pil"), | |
| outputs=gr.Label(num_top_classes=1), | |
| title="Hateful Content Detection", | |
| description="Upload an image to classify hateful gestures or symbols") | |
| if __name__ == "__main__": | |
| iface.launch() |