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#!/usr/bin/env python
from __future__ import annotations
import os
import gradio as gr
import PIL.Image
import spaces
import torch
from transformers import InstructBlipForConditionalGeneration, InstructBlipProcessor
DESCRIPTION = "# InstructBLIP"
MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_id = "Salesforce/instructblip-vicuna-7b"
processor = InstructBlipProcessor.from_pretrained(model_id)
model = InstructBlipForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
@spaces.GPU
def run(
image: PIL.Image.Image,
prompt: str,
text_decoding_method: str = "Nucleus sampling",
num_beams: int = 5,
max_length: int = 256,
min_length: int = 1,
top_p: float = 0.9,
repetition_penalty: float = 1.5,
length_penalty: float = 1.0,
temperature: float = 1.0,
) -> str:
h, w = image.size
scale = MAX_IMAGE_SIZE / max(h, w)
if scale < 1:
new_w = int(w * scale)
new_h = int(h * scale)
image = image.resize((new_w, new_h), resample=PIL.Image.Resampling.LANCZOS)
inputs = processor(images=image, text=prompt, return_tensors="pt").to(device, torch.float16)
generated_ids = model.generate(
**inputs,
do_sample=text_decoding_method == "Nucleus sampling",
num_beams=num_beams,
max_length=max_length,
min_length=min_length,
top_p=top_p,
repetition_penalty=repetition_penalty,
length_penalty=length_penalty,
temperature=temperature,
)
generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
return generated_caption
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
with gr.Row():
with gr.Column():
input_image = gr.Image(type="pil")
prompt = gr.Textbox(label="Prompt")
run_button = gr.Button()
with gr.Accordion(label="Advanced options", open=False):
text_decoding_method = gr.Radio(
label="Text Decoding Method",
choices=["Beam search", "Nucleus sampling"],
value="Nucleus sampling",
)
num_beams = gr.Slider(
label="Number of Beams",
minimum=1,
maximum=10,
step=1,
value=5,
)
max_length = gr.Slider(
label="Max Length",
minimum=1,
maximum=512,
step=1,
value=256,
)
min_length = gr.Slider(
label="Minimum Length",
minimum=1,
maximum=64,
step=1,
value=1,
)
top_p = gr.Slider(
label="Top P",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.9,
)
repetition_penalty = gr.Slider(
label="Repetition Penalty",
info="Larger value prevents repetition.",
minimum=1.0,
maximum=5.0,
step=0.5,
value=1.5,
)
length_penalty = gr.Slider(
label="Length Penalty",
info="Set to larger for longer sequence, used with beam search.",
minimum=-1.0,
maximum=2.0,
step=0.2,
value=1.0,
)
temperature = gr.Slider(
label="Temperature",
info="Used with nucleus sampling.",
minimum=0.5,
maximum=1.0,
step=0.1,
value=1.0,
)
with gr.Column():
output = gr.Textbox(label="Result")
gr.on(
triggers=[prompt.submit, run_button.click],
fn=run,
inputs=[
input_image,
prompt,
text_decoding_method,
num_beams,
max_length,
min_length,
top_p,
repetition_penalty,
length_penalty,
temperature,
],
outputs=output,
api_name="run",
)
if __name__ == "__main__":
demo.queue(max_size=20).launch()
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