app.py

import gradio as gr
from datasets import load_dataset
from transformers import CLIPTokenizerFast, CLIPProcessor, CLIPModel
import torch
from tqdm.auto import tqdm
import numpy as np
import time


def CLIP_model():
    global model, token, processor
    model_id = 'openai/clip-vit-base-patch32'
    model =  CLIPModel.from_pretrained(model_id)
    token = CLIPTokenizerFast.from_pretrained(model_id)
    processor = CLIPProcessor.from_pretrained(model_id)


def hello_name(name):
    return "Hello " + name

def main():
    CLIP_model()

    iface = gr.Interface(fn = hello_name, inputs = "text", outputs  = "text")
    iface.launch(inline = False)


if __name__ == "__main__":
    main()