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import gradio as gr | |
from transformers import AutoProcessor, AutoModelForCausalLM | |
import spaces | |
import requests | |
import copy | |
from PIL import Image, ImageDraw, ImageFont | |
import io | |
import matplotlib.pyplot as plt | |
import matplotlib.patches as patches | |
import random | |
import numpy as np | |
import subprocess | |
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) | |
models = { | |
'microsoft/Florence-2-large-ft': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large-ft', trust_remote_code=True).to("cuda").eval(), | |
'microsoft/Florence-2-large': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True).to("cuda").eval(), | |
'microsoft/Florence-2-base-ft': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base-ft', trust_remote_code=True).to("cuda").eval(), | |
'microsoft/Florence-2-base': AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to("cuda").eval(), | |
} | |
processors = { | |
'microsoft/Florence-2-large-ft': AutoProcessor.from_pretrained('microsoft/Florence-2-large-ft', trust_remote_code=True), | |
'microsoft/Florence-2-large': AutoProcessor.from_pretrained('microsoft/Florence-2-large', trust_remote_code=True), | |
'microsoft/Florence-2-base-ft': AutoProcessor.from_pretrained('microsoft/Florence-2-base-ft', trust_remote_code=True), | |
'microsoft/Florence-2-base': AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True), | |
} | |
DESCRIPTION = "# [Florence-2 OCR Demo](https://huggingface.co/microsoft/Florence-2-large)" | |
colormap = ['blue','orange','green','purple','brown','pink','gray','olive','cyan','red', | |
'lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue'] | |
def fig_to_pil(fig): | |
buf = io.BytesIO() | |
fig.savefig(buf, format='png') | |
buf.seek(0) | |
return Image.open(buf) | |
def run_example(task_prompt, image, text_input=None, model_id='microsoft/Florence-2-large'): | |
model = models[model_id] | |
processor = processors[model_id] | |
if text_input is None: | |
prompt = task_prompt | |
else: | |
prompt = task_prompt + text_input | |
inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda") | |
generated_ids = model.generate( | |
input_ids=inputs["input_ids"], | |
pixel_values=inputs["pixel_values"], | |
max_new_tokens=1024, | |
early_stopping=False, | |
do_sample=False, | |
num_beams=3, | |
) | |
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] | |
parsed_answer = processor.post_process_generation( | |
generated_text, | |
task=task_prompt, | |
image_size=(image.width, image.height) | |
) | |
return parsed_answer | |
def process_image(image, task_prompt, text_input=None, model_id='microsoft/Florence-2-large'): | |
image = Image.fromarray(image) # Convert NumPy array to PIL Image | |
if task_prompt == 'OCR': | |
task_prompt = '<OCR>' | |
results = run_example(task_prompt, image, model_id=model_id) | |
return results, None | |
else: | |
return "", None # Return empty string and None for unknown task prompts | |
css = """ | |
#output { | |
height: 500px; | |
overflow: auto; | |
border: 1px solid #ccc; | |
} | |
""" | |
single_task_list =[ | |
'Caption', 'Detailed Caption', 'More Detailed Caption', 'Object Detection', | |
'Dense Region Caption', 'Region Proposal', 'Caption to Phrase Grounding', | |
'Referring Expression Segmentation', 'Region to Segmentation', | |
'Open Vocabulary Detection', 'Region to Category', 'Region to Description', | |
'OCR', 'OCR with Region' | |
] | |
cascased_task_list =[ | |
'Caption + Grounding', 'Detailed Caption + Grounding', 'More Detailed Caption + Grounding' | |
] | |
def update_task_dropdown(choice): | |
if choice == 'Cascased task': | |
return gr.Dropdown(choices=cascased_task_list, value='Caption + Grounding') | |
else: | |
return gr.Dropdown(choices=single_task_list, value='Caption') | |
with gr.Blocks(css=css) as demo: | |
gr.Markdown(DESCRIPTION) | |
with gr.Tab(label="Florence-2 Image Captioning"): | |
with gr.Row(): | |
with gr.Column(): | |
input_img = gr.Image(label="Input Picture") | |
model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value='microsoft/Florence-2-large') | |
task_type = gr.Radio(choices=['Single task', 'Cascased task'], label='Task type selector', value='Single task') | |
task_prompt = gr.Dropdown(choices=single_task_list, label="Task Prompt", value="Caption") | |
task_type.change(fn=update_task_dropdown, inputs=task_type, outputs=task_prompt) | |
text_input = gr.Textbox(label="Text Input (optional)") | |
submit_btn = gr.Button(value="Submit") | |
with gr.Column(): | |
output_text = gr.Textbox(label="Output Text") | |
gr.Examples( | |
examples=[ | |
["image1.jpg", 'Object Detection'], | |
["image2.jpg", 'OCR with Region'] | |
], | |
inputs=[input_img, task_prompt], | |
outputs=[output_text], | |
fn=process_image, | |
cache_examples=True, | |
label='Try examples' | |
) | |
submit_btn.click(process_image, [input_img, task_prompt, text_input, model_selector], [output_text]) | |
demo.launch(debug=True) | |