import gradio as gr from transformers import AutoProcessor, AutoModelForCausalLM import spaces import io 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_id = 'J-LAB/Florence_2_B_FluxiAI_Product_Caption' model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda").eval() processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True) DESCRIPTION = "# [Florence-2 Product Describe by Fluxi IA](https://huggingface.co/microsoft/Florence-2-large)" @spaces.GPU def run_example(task_prompt, image): inputs = processor(text=task_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): image = Image.fromarray(image) # Convert NumPy array to PIL Image task_prompt = '' results = run_example(task_prompt, image) # Remove the key and get the text value if results and task_prompt in results: output_text = results[task_prompt] else: output_text = "" # Convert newline characters to HTML line breaks output_text = output_text.replace("\n\n", "

").replace("\n", "
") return output_text css = """ #output { height: 500px; overflow: auto; border: 1px solid #ccc; padding: 10px; background-color: #f9f9f9; } """ 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") submit_btn = gr.Button(value="Submit") with gr.Column(): with gr.Box(): output_text = gr.HTML(label="Output Text", elem_id="output") submit_btn.click(process_image, [input_img], [output_text]) demo.launch(debug=True)