app.py

import gradio as gr
import open_clip
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model, _, transform = open_clip.create_model_and_transforms(
    "coca_ViT-L-14",
    pretrained="mscoco_finetuned_laion2B-s13B-b90k"
)

model.to(device)

def output_generate(image):
    im = transform(image).unsqueeze(0).to(device)
    with torch.no_grad(), torch.cuda.amp.autocast():
        generated = model.generate(im, seq_len=20)
    return open_clip.decode(generated[0].detach()).split("<end_of_text>")[0].replace("<start_of_text>", "")

iface = gr.Interface(fn=output_generate, inputs=gr.Image(type="pil"), outputs="text")
iface.launch()