#!/usr/bin/env python

from __future__ import annotations

import gradio as gr
import PIL.Image
import spaces
import torch
from transformers import AutoProcessor, BlipForConditionalGeneration

DESCRIPTION = "# Image Captioning with BLIP"

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

model_id = "Salesforce/blip-image-captioning-large"
processor = AutoProcessor.from_pretrained(model_id)
model = BlipForConditionalGeneration.from_pretrained(model_id).to(device)


@spaces.GPU
def run(image: PIL.Image.Image, text: str = "A picture of") -> str:
    inputs = processor(images=image, text=text, return_tensors="pt").to(device)
    generated_ids = model.generate(pixel_values=inputs.pixel_values, num_beams=3, max_length=20, min_length=5)
    generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return generated_caption


with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    input_image = gr.Image(type="pil")
    text = gr.Textbox(label="Text", value="A picture of")
    run_button = gr.Button("Caption")
    output = gr.Textbox(label="Result")

    gr.on(
        triggers=[text.submit, run_button.click],
        fn=run,
        inputs=[input_image, text],
        outputs=output,
        api_name="caption",
    )

if __name__ == "__main__":
    demo.queue(max_size=20).launch()