import gradio as gr from transformers import AutoProcessor, AutoModelForCausalLM import spaces from PIL import Image import subprocess subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True) model = AutoModelForCausalLM.from_pretrained('HuggingFaceM4/Florence-2-DocVQA', trust_remote_code=True).to("cuda").eval() processor = AutoProcessor.from_pretrained('HuggingFaceM4/Florence-2-DocVQA', trust_remote_code=True) TITLE = "# [Florence-2-DocVQA Demo](https://huggingface.co/HuggingFaceM4/Florence-2-DocVQA)" DESCRIPTION = "The demo for Florence-2 fine-tuned on DocVQA dataset. You can find the notebook [here](https://colab.research.google.com/drive/1hKDrJ5AH_o7I95PtZ9__VlCTNAo1Gjpf?usp=sharing). Read more about Florence-2 fine-tuning [here](finetune-florence2)." colormap = ['blue','orange','green','purple','brown','pink','gray','olive','cyan','red', 'lime','indigo','violet','aqua','magenta','coral','gold','tan','skyblue'] @spaces.GPU def run_example(task_prompt, image, text_input=None): 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, text_input=None): image = Image.fromarray(image) # Convert NumPy array to PIL Image task_prompt = '' results = run_example(task_prompt, image, text_input)[task_prompt].replace("", "") return results css = """ #output { height: 500px; overflow: auto; border: 1px solid #ccc; } """ with gr.Blocks(css=css) as demo: gr.Markdown(TITLE) gr.Markdown(DESCRIPTION) with gr.Tab(label="Florence-2 Image Captioning"): with gr.Row(): with gr.Column(): input_img = gr.Image(label="Input Picture") 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=[ ["hunt.jpg", 'What is this image?'], ["idefics2_architecture.png", 'How many tokens per image does it use?'], ["idefics2_architecture.png", "What type of encoder does the model use?"], ["image.jpg", "What's the share of Industry Switchers Gained?"] ], inputs=[input_img, text_input], outputs=[output_text], fn=process_image, cache_examples=True, label='Try the examples below' ) submit_btn.click(process_image, [input_img, text_input], [output_text]) demo.launch(debug=True)