52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
from transformers import AutoProcessor, AutoModelForCausalLM
|
|
from huggingface_hub import hf_hub_download
|
|
from PIL import Image
|
|
import gradio as gr
|
|
import torch
|
|
from gradio.themes.utils import sizes
|
|
|
|
|
|
theme = gr.themes.Default(radius_size=sizes.radius_none).set(
|
|
block_label_text_color = '#4D63FF',
|
|
block_title_text_color = '#4D63FF',
|
|
button_primary_text_color = '#4D63FF',
|
|
button_primary_background_fill='#FFFFFF',
|
|
button_primary_border_color='#4D63FF',
|
|
button_primary_background_fill_hover='#EDEFFF',
|
|
)
|
|
|
|
|
|
processor = AutoProcessor.from_pretrained("microsoft/git-large-vqav2")
|
|
model = AutoModelForCausalLM.from_pretrained("microsoft/git-large-vqav2")
|
|
|
|
|
|
def vqa(image, question):
|
|
inp = Image.fromarray(image.astype('uint8'), 'RGB')
|
|
pixel_values = processor(images=inp, return_tensors="pt").pixel_values
|
|
|
|
input_ids = processor(text=question, add_special_tokens=False).input_ids
|
|
input_ids = [processor.tokenizer.cls_token_id] + input_ids
|
|
input_ids = torch.tensor(input_ids).unsqueeze(0)
|
|
|
|
generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50)
|
|
return processor.batch_decode(generated_ids, skip_special_tokens=True)
|
|
|
|
with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
|
|
gr.Markdown("""
|
|
<div align='center' ><font size='60'>图片问答</font></div>
|
|
""")
|
|
with gr.Row():
|
|
with gr.Column():
|
|
image = gr.Image(label="图片")
|
|
question = gr.Textbox(label="问题")
|
|
with gr.Row():
|
|
button = gr.Button("提交", variant="primary")
|
|
box2 = gr.Textbox(label="文本")
|
|
|
|
button.click(fn=vqa, inputs=[image, question], outputs=box2)
|
|
examples = gr.Examples(examples=[["cats.jpg", "How many cats are there?"], ["astronaut.jpg", "What's the astronaut riding on?"]], inputs=[image, question], label="例子")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
demo.queue(concurrency_count=3).launch(server_name = "0.0.0.0")
|