app.py

import data
import torch
import gradio as gr
from models import imagebind_model
from models.imagebind_model import ModalityType


device = "cuda:0" if torch.cuda.is_available() else "cpu"
model = imagebind_model.imagebind_huge(pretrained=True)
model.eval()
model.to(device)


def image_text_zeroshot(image, text_list):
    image_paths = [image]
    labels = [label.strip(" ") for label in text_list.strip(" ").split("|")]
    inputs = {
        ModalityType.TEXT: data.load_and_transform_text(labels, device),
        ModalityType.VISION: data.load_and_transform_vision_data(image_paths, device),
    }

    with torch.no_grad():
        embeddings = model(inputs)

    scores = torch.softmax(
        embeddings[ModalityType.VISION] @ embeddings[ModalityType.TEXT].T,
        dim=-1
    ).squeeze(0).tolist()

    score_dict = {label:score for label, score in zip(labels, scores)}

    return score_dict


inputs = [
    gr.inputs.Image(type='file',
                    label="Input image"),
    gr.inputs.Textbox(lines=1,
                      label="Candidate texts"),
]

iface = gr.Interface(image_text_zeroshot,
            inputs,
            "label",
            examples=[["assets/dog_image.jpg", "A dog|A car|A bird"],
                      ["assets/car_image.jpg", "A dog|A car|A bird"],
                      ["assets/bird_image.jpg", "A dog|A car|A bird"]],
            description="""Zeroshot test""",
            title="Zero-shot Classification")

iface.launch()