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()