|
|
|
|
|
from __future__ import annotations |
|
|
|
import gradio as gr |
|
import PIL.Image |
|
import spaces |
|
import torch |
|
from transformers import AutoProcessor, BlipForConditionalGeneration |
|
from typing import Union |
|
import os |
|
|
|
DESCRIPTION = "# Image Captioning with LongCap" |
|
|
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
print("Using device: ", device) |
|
|
|
model_id = "unography/blip-long-cap" |
|
processor = AutoProcessor.from_pretrained(model_id) |
|
model = BlipForConditionalGeneration.from_pretrained(model_id).to(device) |
|
|
|
torch.hub.download_url_to_file("https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg", "demo.jpg") |
|
torch.hub.download_url_to_file( |
|
"https://huggingface.co/datasets/nielsr/textcaps-sample/resolve/main/stop_sign.png", "stop_sign.png" |
|
) |
|
torch.hub.download_url_to_file( |
|
"https://cdn.openai.com/dall-e-2/demos/text2im/astronaut/horse/photo/0.jpg", "astronaut.jpg" |
|
) |
|
|
|
@spaces.GPU() |
|
def run(image: Union[str, PIL.Image.Image]) -> str: |
|
if isinstance(image, str): |
|
image = Image.open(image) |
|
inputs = processor(images=image, return_tensors="pt").to(device) |
|
out = model.generate(pixel_values=inputs.pixel_values, num_beams=3, repetition_penalty=2.5, max_length=300) |
|
generated_caption = processor.decode(out[0], skip_special_tokens=True) |
|
return generated_caption |
|
|
|
|
|
with gr.Blocks(css="style.css") as demo: |
|
gr.Markdown(DESCRIPTION) |
|
input_image = gr.Image(type="pil") |
|
run_button = gr.Button("Caption") |
|
output = gr.Textbox(label="Result") |
|
gr.Examples( |
|
examples=[ |
|
"demo.jpg", |
|
"stop_sign.png", |
|
"astronaut.jpg", |
|
], |
|
inputs=input_image, |
|
outputs=output, |
|
fn=run, |
|
cache_examples=os.getenv("CACHE_EXAMPLES") == "1", |
|
) |
|
|
|
run_button.click( |
|
fn=run, |
|
inputs=input_image, |
|
outputs=output, |
|
api_name="caption", |
|
) |
|
|
|
if __name__ == "__main__": |
|
demo.queue(max_size=20).launch() |
|
|
