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import gradio as gr
from lavis.models import load_model_and_preprocess
import torch
device = torch.device("cuda") if torch.cuda.is_available() else "cpu"
model_name = "blip2_t5_instruct"
model_type = "flant5xl"
model, vis_processors, _ = load_model_and_preprocess(
name=model_name,
model_type=model_type,
is_eval=True,
device=device
)
def infer(image, prompt, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, decoding_method):
use_nucleus_sampling = decoding_method == "Nucleus sampling"
image = vis_processors["eval"](image).unsqueeze(0).to(device)
samples = {
"image": image,
"prompt": prompt,
}
output = model.generate(
samples,
length_penalty=float(len_penalty),
repetition_penalty=float(repetition_penalty),
num_beams=beam_size,
max_length=max_len,
min_length=min_len,
top_p=top_p,
use_nucleus_sampling=use_nucleus_sampling
)
return output[0]
theme = gr.themes.Monochrome(
primary_hue="indigo",
secondary_hue="blue",
neutral_hue="slate",
radius_size=gr.themes.sizes.radius_sm,
font=[gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif"],
)
css = ".generating {visibility: hidden}"
examples = [
["banff.jpg", "Can you tell me about this image in detail", 1, 200, 5, 1, 3, 0.9, "Beam search"]
]
with gr.Blocks(theme=theme, analytics_enabled=False,css=css) as demo:
gr.Markdown("## InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning")
gr.Markdown(
"""
Unofficial demo for InstructBLIP. InstructBLIP is a new vision-language instruction-tuning framework by Salesforce that uses BLIP-2 models, achieving state-of-the-art zero-shot generalization performance on a wide range of vision-language tasks.
The demo is based on the official <a href="https://github.com/salesforce/LAVIS/tree/main/projects/instructblip" style="text-decoration: underline;" target="_blank"> Github </a> implementation
"""
)
gr.HTML("<p>You can duplicate this Space to run it privately without a queue for shorter queue times : <a style='display:inline-block' href='https://huggingface.co/spaces/RamAnanth1/InstructBLIP?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14' alt='Duplicate Space'></a> </p>")
with gr.Row():
with gr.Column(scale=3):
image_input = gr.Image(type="pil")
prompt_textbox = gr.Textbox(label="Prompt:", placeholder="prompt", value="Please describe what you see.", lines=2)
output = gr.Textbox(label="Output")
submit = gr.Button("Run", variant="primary")
with gr.Column(scale=1):
min_len = gr.Slider(
minimum=1,
maximum=100,
value=25,
step=1,
interactive=False,
label="Min Length",
)
max_len = gr.Slider(
minimum=10,
maximum=500,
value=500,
step=5,
interactive=False,
label="Max Length",
)
sampling = gr.Radio(
choices=["Beam search", "Nucleus sampling"],
value="Beam search",
label="Text Decoding Method",
interactive=False,
)
top_p = gr.Slider(
minimum=0.5,
maximum=1.0,
value=0.9,
step=0.1,
interactive=False,
label="Top p",
)
beam_size = gr.Slider(
minimum=1,
maximum=10,
value=5,
step=1,
interactive=False,
label="Beam Size",
)
len_penalty = gr.Slider(
minimum=-1,
maximum=2,
value=0.4,
step=0.2,
interactive=False,
label="Length Penalty",
)
repetition_penalty = gr.Slider(
minimum=-1,
maximum=3,
value=5,
step=0.2,
interactive=False,
label="Repetition Penalty",
)
gr.Examples(
examples=examples,
inputs=[image_input, prompt_textbox, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, sampling],
cache_examples=False,
fn=infer,
outputs=[output],
)
submit.click(infer, inputs=[image_input, prompt_textbox, min_len, max_len, beam_size, len_penalty, repetition_penalty, top_p, sampling], outputs=[output])
demo.queue(concurrency_count=16).launch(debug=True, share=True,)
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