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import gradio as gr |
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from transformers import AutoProcessor, AutoModelForCausalLM |
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import spaces |
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import requests |
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import copy |
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from PIL import Image, ImageDraw, ImageFont |
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import io |
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import matplotlib.pyplot as plt |
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import matplotlib.patches as patches |
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import random |
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import numpy as np |
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import subprocess |
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subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) |
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model = AutoModelForCausalLM.from_pretrained('HuggingFaceM4/Florence-2-DocVQA', trust_remote_code=True).to("cuda").eval() |
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processor = AutoProcessor.from_pretrained('HuggingFaceM4/Florence-2-DocVQA', trust_remote_code=True) |
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DESCRIPTION = "# [Florence-2-DocVQA Demo](https://huggingface.co/HuggingFaceM4/Florence-2-DocVQA)" |
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colormap = ['blue','orange','green','purple','brown','pink','gray','olive','cyan','red', |
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'lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue'] |
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@spaces.GPU |
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def run_example(task_prompt, image, text_input=None): |
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if text_input is None: |
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prompt = task_prompt |
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else: |
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prompt = task_prompt + text_input |
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inputs = processor(text=prompt, images=image, return_tensors="pt").to("cuda") |
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generated_ids = model.generate( |
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input_ids=inputs["input_ids"], |
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pixel_values=inputs["pixel_values"], |
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max_new_tokens=1024, |
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early_stopping=False, |
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do_sample=False, |
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num_beams=3, |
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) |
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] |
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parsed_answer = processor.post_process_generation( |
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generated_text, |
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task=task_prompt, |
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image_size=(image.width, image.height) |
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) |
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return parsed_answer |
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def process_image(image, text_input=None): |
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image = Image.fromarray(image) |
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task_prompt = '<DocVQA>' |
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results = run_example(task_prompt, image, text_input)[task_prompt].replace("<pad>", "") |
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return results, None |
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css = """ |
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#output { |
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height: 500px; |
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overflow: auto; |
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border: 1px solid #ccc; |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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gr.Markdown(DESCRIPTION) |
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with gr.Tab(label="Florence-2 Image Captioning"): |
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with gr.Row(): |
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with gr.Column(): |
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input_img = gr.Image(label="Input Picture") |
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text_input = gr.Textbox(label="Text Input (optional)") |
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submit_btn = gr.Button(value="Submit") |
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with gr.Column(): |
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output_text = gr.Textbox(label="Output Text") |
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output_img = gr.Image(label="Output Image") |
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gr.Examples( |
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examples=[ |
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["idefics2_architecture.png", 'How many tokens per image does it use?'], |
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["idefics2_architecture.png", 'How large can the input images be?'], |
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["idefics2_architecture.png", 'Up to which size can the images be?'], |
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["image.jpg", "What's the share of Industry Switchers Gained?"] |
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], |
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inputs=[input_img, text_input], |
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outputs=[output_text, output_img], |
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fn=process_image, |
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cache_examples=True, |
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label='Try examples' |
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) |
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submit_btn.click(process_image, [input_img, text_input], [output_text, output_img]) |
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demo.launch(debug=True) |
