| import gradio as gr | |
| import torch | |
| import open_clip | |
| import joblib | |
| def predict(input_image): | |
| device = torch.device("cpu") | |
| model, _, preprocess = open_clip.create_model_and_transforms('ViT-L-14', pretrained='openai', device=device) | |
| image = preprocess(input_image).unsqueeze(0).to(device) | |
| with torch.amp.autocast(device_type=device.type): | |
| with torch.no_grad(): | |
| image_features = model.encode_image(image) | |
| image_features /= image_features.norm(dim=-1, keepdim=True) | |
| embedding = image_features[0].cpu().float().numpy() | |
| model = joblib.load('model.pkl') | |
| result = model.predict([embedding]) | |
| return "Map" if result == 1 else "No map" | |
| demo = gr.Interface(fn=predict, inputs=gr.Image(label="Check whether the image is a map or not", type="pil"), outputs="text") | |
| demo.launch() |