import gradio as gr
import torch
from PIL import Image
import open_clip

def greet(image):
    image = Image.fromarray(file_path)
    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]

    return str(embedding)

demo = gr.Interface(fn=greet, inputs=gr.inputs.Image(), outputs="text")
demo.launch()