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#!/usr/bin/env python
from __future__ import annotations
import os
import string
import gradio as gr
import PIL.Image
import spaces
import torch
from transformers import AutoProcessor, BitsAndBytesConfig, Blip2ForConditionalGeneration
from transformers import BitsAndBytesConfig
bnb_config = BitsAndBytesConfig(
load_in_8bit=True,
)
DESCRIPTION = "# [BLIP-2](https://github.com/salesforce/LAVIS/tree/main/projects/blip2)"
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
MODEL_ID_OPT_2_7B = "Salesforce/blip2-opt-2.7b"
MODEL_ID_OPT_6_7B = "Salesforce/blip2-opt-6.7b"
MODEL_ID_FLAN_T5_XL = "Salesforce/blip2-flan-t5-xl"
MODEL_ID_FLAN_T5_XXL = "Salesforce/blip2-flan-t5-xxl"
MODEL_ID_FLAN_T5_XL_COCO = "Salesforce/blip2-flan-t5-xl-coco"
MODEL_ID = MODEL_ID_FLAN_T5_XL_COCO
assert MODEL_ID in [MODEL_ID_OPT_2_7B, MODEL_ID_OPT_6_7B, MODEL_ID_FLAN_T5_XL, MODEL_ID_FLAN_T5_XXL, MODEL_ID_FLAN_T5_XL_COCO]
if torch.cuda.is_available():
processor = AutoProcessor.from_pretrained(MODEL_ID)
model = Blip2ForConditionalGeneration.from_pretrained(MODEL_ID, device_map="auto", quantization_config=bnb_config)
@spaces.GPU
def generate_caption(
image: PIL.Image.Image,
decoding_method: str = "Nucleus sampling",
temperature: float = 1.0,
length_penalty: float = 1.0,
repetition_penalty: float = 1.5,
max_length: int = 50,
min_length: int = 1,
num_beams: int = 5,
top_p: float = 0.9,
) -> str:
inputs = processor(images=[image], return_tensors="pt").to(device, dtype=torch.float16)
generated_ids = model.generate(
pixel_values=inputs.pixel_values,
do_sample=decoding_method == "Nucleus sampling",
temperature=temperature,
length_penalty=length_penalty,
repetition_penalty=repetition_penalty,
max_length=max_length,
min_length=min_length,
num_beams=num_beams,
top_p=top_p,
)
result = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
return result
@spaces.GPU
def generate_captions(
images: list[PIL.Image.Image],
decoding_method: str = "Nucleus sampling",
temperature: float = 1.0,
length_penalty: float = 1.0,
repetition_penalty: float = 1.5,
max_length: int = 50,
min_length: int = 1,
num_beams: int = 5,
top_p: float = 0.9,
) -> list[str]:
inputs = processor(images=images, return_tensors="pt").to(device, dtype=torch.float16)
generated_ids = model.generate(
pixel_values=inputs.pixel_values,
do_sample=decoding_method == "Nucleus sampling",
temperature=temperature,
length_penalty=length_penalty,
repetition_penalty=repetition_penalty,
max_length=max_length,
min_length=min_length,
num_beams=num_beams,
top_p=top_p,
)
results = processor.batch_decode(generated_ids, skip_special_tokens=True)
return [result.strip() for result in results]
@spaces.GPU
def answer_question(
image: PIL.Image.Image,
prompt: str,
decoding_method: str = "Nucleus sampling",
temperature: float = 1.0,
length_penalty: float = 1.0,
repetition_penalty: float = 1.5,
max_length: int = 50,
min_length: int = 1,
num_beams: int = 5,
top_p: float = 0.9,
) -> str:
inputs = processor(images=[image], text=prompt, return_tensors="pt").to(device, dtype=torch.float16)
generated_ids = model.generate(
**inputs,
do_sample=decoding_method == "Nucleus sampling",
temperature=temperature,
length_penalty=length_penalty,
repetition_penalty=repetition_penalty,
max_length=max_length,
min_length=min_length,
num_beams=num_beams,
top_p=top_p,
)
result = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
return result
def postprocess_output(output: str) -> str:
if output and output[-1] not in string.punctuation:
output += "."
return output
def chat(
image: PIL.Image.Image,
text: str,
decoding_method: str = "Nucleus sampling",
temperature: float = 1.0,
length_penalty: float = 1.0,
repetition_penalty: float = 1.5,
max_length: int = 50,
min_length: int = 1,
num_beams: int = 5,
top_p: float = 0.9,
history_orig: list[str] = [],
history_qa: list[str] = [],
) -> tuple[list[tuple[str, str]], list[str], list[str]]:
history_orig.append(text)
text_qa = f"Question: {text} Answer:"
history_qa.append(text_qa)
prompt = " ".join(history_qa)
output = answer_question(
image=image,
prompt=prompt,
decoding_method=decoding_method,
temperature=temperature,
length_penalty=length_penalty,
repetition_penalty=repetition_penalty,
max_length=max_length,
min_length=min_length,
num_beams=num_beams,
top_p=top_p,
)
output = postprocess_output(output)
history_orig.append(output)
history_qa.append(output)
chat_val = list(zip(history_orig[0::2], history_orig[1::2]))
return chat_val, history_orig, history_qa
examples = [
[
"images/house.png",
"How could someone get out of the house?",
],
[
"images/flower.jpg",
"What is this flower and where is it's origin?",
],
[
"images/pizza.jpg",
"What are steps to cook it?",
],
[
"images/sunset.jpg",
"Here is a romantic message going along the photo:",
],
[
"images/forbidden_city.webp",
"In what dynasties was this place built?",
],
]
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(
value="Duplicate Space for private use",
elem_id="duplicate-button",
visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
)
with gr.Tabs():
with gr.Tab(label="Single Image"):
with gr.Group():
image = gr.Image(type="pil")
with gr.Tabs():
with gr.Tab(label="Image Captioning"):
caption_button = gr.Button("Caption it!")
caption_output = gr.Textbox(label="Caption Output", show_label=False, container=False)
with gr.Tab(label="Visual Question Answering"):
chatbot = gr.Chatbot(label="VQA Chat", show_label=False)
history_orig = gr.State(value=[])
history_qa = gr.State(value=[])
vqa_input = gr.Text(label="Chat Input", show_label=False, max_lines=1, container=False)
with gr.Row():
clear_chat_button = gr.Button("Clear")
chat_button = gr.Button("Submit", variant="primary")
with gr.Accordion(label="Advanced settings", open=False):
text_decoding_method = gr.Radio(
label="Text Decoding Method",
choices=["Beam search", "Nucleus sampling"],
value="Nucleus sampling",
)
temperature = gr.Slider(
label="Temperature",
info="Used with nucleus sampling.",
minimum=0.5,
maximum=1.0,
step=0.1,
value=1.0,
)
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,
)
repetition_penalty = gr.Slider(
label="Repetition Penalty",
info="Larger value prevents repetition.",
minimum=1.0,
maximum=5.0,
step=0.5,
value=1.5,
)
max_length = gr.Slider(
label="Max Length",
minimum=20,
maximum=512,
step=1,
value=50,
)
min_length = gr.Slider(
label="Minimum Length",
minimum=1,
maximum=100,
step=1,
value=1,
)
num_beams = gr.Slider(
label="Number of Beams",
minimum=1,
maximum=10,
step=1,
value=5,
)
top_p = gr.Slider(
label="Top P",
info="Used with nucleus sampling.",
minimum=0.5,
maximum=1.0,
step=0.1,
value=0.9,
)
with gr.Tab(label="Batch Image"):
with gr.Group():
batch_images = gr.Files(label="Batch Process", interactive=True, elem_id="extras_image_batch")
with gr.Tabs():
with gr.Tab(label="Image Captioning"):
batch_caption_button = gr.Button("Caption it!")
batch_caption_output = gr.JSON(label="Caption Output")
with gr.Accordion(label="Advanced settings", open=False):
text_decoding_method = gr.Radio(
label="Text Decoding Method",
choices=["Beam search", "Nucleus sampling"],
value="Nucleus sampling",
)
temperature = gr.Slider(
label="Temperature",
info="Used with nucleus sampling.",
minimum=0.5,
maximum=1.0,
step=0.1,
value=1.0,
)
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,
)
repetition_penalty = gr.Slider(
label="Repetition Penalty",
info="Larger value prevents repetition.",
minimum=1.0,
maximum=5.0,
step=0.5,
value=1.5,
)
max_length = gr.Slider(
label="Max Length",
minimum=20,
maximum=512,
step=1,
value=50,
)
min_length = gr.Slider(
label="Minimum Length",
minimum=1,
maximum=100,
step=1,
value=1,
)
num_beams = gr.Slider(
label="Number of Beams",
minimum=1,
maximum=10,
step=1,
value=5,
)
top_p = gr.Slider(
label="Top P",
info="Used with nucleus sampling.",
minimum=0.5,
maximum=1.0,
step=0.1,
value=0.9,
)
gr.Examples(
examples=examples,
inputs=[image, vqa_input],
outputs=caption_output,
fn=generate_caption,
)
caption_button.click(
fn=generate_caption,
inputs=[
image,
text_decoding_method,
temperature,
length_penalty,
repetition_penalty,
max_length,
min_length,
num_beams,
top_p,
],
outputs=caption_output,
api_name="caption",
)
batch_caption_button.click(
fn=generate_captions,
inputs=[
batch_images,
text_decoding_method,
temperature,
length_penalty,
repetition_penalty,
max_length,
min_length,
num_beams,
top_p,
],
outputs=batch_caption_output,
api_name="caption",
)
chat_inputs = [
image,
vqa_input,
text_decoding_method,
temperature,
length_penalty,
repetition_penalty,
max_length,
min_length,
num_beams,
top_p,
history_orig,
history_qa,
]
chat_outputs = [
chatbot,
history_orig,
history_qa,
]
vqa_input.submit(
fn=chat,
inputs=chat_inputs,
outputs=chat_outputs,
).success(
fn=lambda: "",
outputs=vqa_input,
queue=False,
api_name=False,
)
chat_button.click(
fn=chat,
inputs=chat_inputs,
outputs=chat_outputs,
api_name="chat",
).success(
fn=lambda: "",
outputs=vqa_input,
queue=False,
api_name=False,
)
clear_chat_button.click(
fn=lambda: ("", [], [], []),
inputs=None,
outputs=[
vqa_input,
chatbot,
history_orig,
history_qa,
],
queue=False,
api_name="clear",
)
image.change(
fn=lambda: ("", [], [], []),
inputs=None,
outputs=[
caption_output,
chatbot,
history_orig,
history_qa,
],
queue=False,
)
if __name__ == "__main__":
demo.queue(max_size=10).launch()
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