app.py

import gradio as gr
from transformers import AutoProcessor, AutoModelForCausalLM
import spaces
import io
import base64  # Adicionando a biblioteca base64 para decodificação
from PIL import Image
import subprocess

# Instalando a dependência flash-attn se necessário
subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)

# Carregando o modelo e o processador
model_id = 'J-LAB/Florence-vl3'
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda").eval()
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

DESCRIPTION = "# Product Describe by Fluxi IA\n### Base Model [Florence-2] (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

# Função para processar imagens, agora suportando Base64
def process_image(image, task_prompt):
    # Verifica se a imagem é uma string base64
    if isinstance(image, str) and image.startswith("data:image"):
        # Extraindo a parte base64 da string
        base64_image = image.split(",")[1]
        # Decodificando a imagem base64
        image = Image.open(io.BytesIO(base64.b64decode(base64_image)))
    elif isinstance(image, bytes):
        image = Image.open(io.BytesIO(image))
    else:
        image = Image.fromarray(image)  # Convertendo um array NumPy para imagem PIL, se aplicável

    # Mapeando os prompts de tarefas
    if task_prompt == 'Product Caption':
        task_prompt = '<MORE_DETAILED_CAPTION>'
    elif task_prompt == 'OCR':
        task_prompt = '<OCR>'
    
    # Chamando o exemplo com a imagem processada e o prompt da tarefa
    results = run_example(task_prompt, image)
    
    # Extraindo o texto gerado a partir dos resultados
    if results and task_prompt in results:
        output_text = results[task_prompt]
    else:
        output_text = ""

    # Convertendo quebras de linha para quebras de linha HTML
    output_text = output_text.replace("\n\n", "<br><br>").replace("\n", "<br>")

    return output_text

css = """
  #output {
    overflow: auto; 
    border: 1px solid #ccc; 
    padding: 10px;
    background-color: rgb(31 41 55);
    color: #fff; 
  }
"""

js = """
function adjustHeight() {
    var outputElement = document.getElementById('output');
    outputElement.style.height = 'auto';  // Reset height to auto to get the actual content height
    var height = outputElement.scrollHeight + 'px';  // Get the scrollHeight
    outputElement.style.height = height;  // Set the height
}

// Attach the adjustHeight function to the click event of the submit button
document.querySelector('button').addEventListener('click', function() {
    setTimeout(adjustHeight, 500);  // Adjust the height after a small delay to ensure content is loaded
});
"""

single_task_list = ['Product Caption', 'OCR']

with gr.Blocks(css=css) as demo:
    gr.Markdown(DESCRIPTION)
    with gr.Tab(label="Product Image Select"):
        with gr.Row():
            with gr.Column():
                input_img = gr.Image(label="Input Picture", source="upload", type="pil")  # Suporte a PIL images
                task_prompt = gr.Dropdown(choices=single_task_list, label="Task Prompt", value="Product Caption")
                submit_btn = gr.Button(value="Submit")
            with gr.Column():
                output_text = gr.HTML(label="Output Text", elem_id="output")
        
        gr.Markdown(""" 
        ## How to use via API
        To use this model via API, you can follow the example code below:

        ```python
        import base64
        from PIL import Image
        import io
        import requests

        # Converting image to base64
        image_path = 'path_to_image.png'
        with open(image_path, 'rb') as image_file:
            image_base64 = base64.b64encode(image_file.read()).decode('utf-8')
        
        # Preparing the payload
        payload = {
            "image": f"data:image/png;base64,{image_base64}",
            "task_prompt": "Product Caption"
        }

        response = requests.post("http://your-space-url-here", json=payload)
        print(response.json())
        ``` 
        """)
        
        submit_btn.click(process_image, [input_img, task_prompt], [output_text])

    demo.load(lambda: None, inputs=None, outputs=None, js=js)

demo.launch(debug=True)