| import gradio as gr |
| import torch |
| import open_clip |
|
|
| def predict(image): |
| device = torch.device("cpu") |
| model, _, preprocess = open_clip.create_model_and_transforms('ViT-L-14', pretrained='openai', device=device) |
|
|
| image = preprocess().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="Input Image", type="pil"), outputs="text") |
| demo.launch() |
