Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| from transformers import AutoProcessor, AutoModelForCausalLM | |
| import spaces | |
| from PIL import Image | |
| import io | |
| import subprocess | |
| subprocess.run("pip install flash-attn --no-build-isolation", env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"}, shell=True) | |
| models = { | |
| "maxiw/Florence-2-ScreenQA-base": AutoModelForCausalLM.from_pretrained("maxiw/Florence-2-ScreenQA-base", trust_remote_code=True).to("cuda").eval(), | |
| } | |
| processors = { | |
| "maxiw/Florence-2-ScreenQA-base": AutoProcessor.from_pretrained("maxiw/Florence-2-ScreenQA-base", trust_remote_code=True), | |
| } | |
| DESCRIPTION = "# [Florence-2-ScreenQA Demo](https://huggingface.co/maxiw/Florence-2-ScreenQA-base)" | |
| def run_example(task_prompt, image, text_input=None, model_id="maxiw/Florence-2-ScreenQA-base"): | |
| 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) | |
| ) | |
| if "<SQA>" in parsed_answer: | |
| parsed_answer = parsed_answer["<SQA>"] | |
| return parsed_answer | |
| def process_image(image, task_prompt, text_input=None, model_id="maxiw/Florence-2-ScreenQA-base"): | |
| image = Image.fromarray(image) # Convert NumPy array to PIL Image | |
| if task_prompt == "ScreenQA": | |
| task_prompt = "<SQA>" | |
| results = run_example(task_prompt, image, text_input, model_id=model_id) | |
| return results | |
| else: | |
| print("Unknown task prompt") | |
| return "", None # Return empty string and None for unknown task prompts | |
| css = """ | |
| #output { | |
| height: 500px; | |
| overflow: auto; | |
| border: 1px solid #ccc; | |
| } | |
| """ | |
| single_task_list =[ | |
| "ScreenQA" | |
| ] | |
| with gr.Blocks(css=css) as demo: | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Tab(label="Florence-2 Input"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_img = gr.Image(label="Input Picture") | |
| model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="maxiw/Florence-2-ScreenQA-base") | |
| task_prompt = gr.Dropdown(choices=single_task_list, label="Task Prompt", value="ScreenQA") | |
| text_input = gr.Textbox(label="Question") | |
| submit_btn = gr.Button(value="Submit") | |
| with gr.Column(): | |
| output_text = gr.Textbox(label="Output Text") | |
| gr.Examples( | |
| examples=[ | |
| ["image1.jpg", "ScreenQA", "What is the version of the settings?"], | |
| ["image1.jpg", "ScreenQA", "What is the state of use lower resolution images?"], | |
| ["image2.jpg", "ScreenQA", "How much is the discount for the product?"] | |
| ], | |
| inputs=[input_img, task_prompt, text_input], | |
| 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) | |