app.py

import gradio as gr
import spaces
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
import base64
from PIL import Image, ImageDraw
from io import BytesIO
import re

from deepseek_vl.models import DeepseekVLV2Processor, DeepseekVLV2ForCausalLM
from deepseek_vl.utils.io import load_pil_images
from transformers import AutoModelForCausalLM



models = {
    "OS-Copilot/OS-Atlas-Base-7B": Qwen2VLForConditionalGeneration.from_pretrained("OS-Copilot/OS-Atlas-Base-7B", torch_dtype="auto", device_map="auto"),
}

processors = {
    "OS-Copilot/OS-Atlas-Base-7B": AutoProcessor.from_pretrained("OS-Copilot/OS-Atlas-Base-7B")
}


def image_to_base64(image):
    buffered = BytesIO()
    image.save(buffered, format="PNG")
    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
    return img_str


def draw_bounding_boxes(image, bounding_boxes, outline_color="red", line_width=2):
    draw = ImageDraw.Draw(image)
    for box in bounding_boxes:
        xmin, ymin, xmax, ymax = box
        draw.rectangle([xmin, ymin, xmax, ymax], outline=outline_color, width=line_width)
    return image


def rescale_bounding_boxes(bounding_boxes, original_width, original_height, scaled_width=1000, scaled_height=1000):
    x_scale = original_width / scaled_width
    y_scale = original_height / scaled_height
    rescaled_boxes = []
    for box in bounding_boxes:
        xmin, ymin, xmax, ymax = box
        rescaled_box = [
            xmin * x_scale,
            ymin * y_scale,
            xmax * x_scale,
            ymax * y_scale
        ]
        rescaled_boxes.append(rescaled_box)
    return rescaled_boxes


def deepseek():
        
    # specify the path to the model
    model_path = "deepseek-ai/deepseek-vl2-small"
    vl_chat_processor: DeepseekVLV2Processor = DeepseekVLV2Processor.from_pretrained(model_path)
    tokenizer = vl_chat_processor.tokenizer

    vl_gpt: DeepseekVLV2ForCausalLM = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
    vl_gpt = vl_gpt.to(torch.bfloat16).cuda().eval()

    ## single image conversation example
    conversation = [
        {
            "role": "<|User|>",
            "content": "<image>\n<|ref|>The giraffe at the back.<|/ref|>.",
            "images": ["./images/visual_grounding.jpeg"],
        },
        {"role": "<|Assistant|>", "content": ""},
    ]

    ## multiple images (or in-context learning) conversation example
    # conversation = [
    #     {
    #         "role": "User",
    #         "content": "<image_placeholder>A dog wearing nothing in the foreground, "
    #                    "<image_placeholder>a dog wearing a santa hat, "
    #                    "<image_placeholder>a dog wearing a wizard outfit, and "
    #                    "<image_placeholder>what's the dog wearing?",
    #         "images": [
    #             "images/dog_a.png",
    #             "images/dog_b.png",
    #             "images/dog_c.png",
    #             "images/dog_d.png",
    #         ],
    #     },
    #     {"role": "Assistant", "content": ""}
    # ]

    # load images and prepare for inputs
    pil_images = load_pil_images(conversation)
    prepare_inputs = vl_chat_processor(
        conversations=conversation,
        images=pil_images,
        force_batchify=True,
        system_prompt=""
    ).to(vl_gpt.device)

    # run image encoder to get the image embeddings
    inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)

    # run the model to get the response
    outputs = vl_gpt.language_model.generate(
        inputs_embeds=inputs_embeds,
        attention_mask=prepare_inputs.attention_mask,
        pad_token_id=tokenizer.eos_token_id,
        bos_token_id=tokenizer.bos_token_id,
        eos_token_id=tokenizer.eos_token_id,
        max_new_tokens=512,
        do_sample=False,
        use_cache=True
    )

    answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
    print(f"{prepare_inputs['sft_format'][0]}", answer)

@spaces.GPU
def run_example(image, text_input, model_id="OS-Copilot/OS-Atlas-Base-7B"):

    deepseek()
    

def run_example_old(image, text_input, model_id="OS-Copilot/OS-Atlas-Base-7B"):
    model = models[model_id].eval()
    processor = processors[model_id]
    prompt = f"In this UI screenshot, what is the position of the element corresponding to the command \"{text_input}\" (with bbox)?"
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": f"data:image;base64,{image_to_base64(image)}"},
                {"type": "text", "text": prompt},
            ],
        }
    ]

    text = processor.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    image_inputs, video_inputs = process_vision_info(messages)
    inputs = processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    )
    inputs = inputs.to("cuda")

    generated_ids = model.generate(**inputs, max_new_tokens=128)
    generated_ids_trimmed = [
        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    output_text = processor.batch_decode(
        generated_ids_trimmed, skip_special_tokens=False, clean_up_tokenization_spaces=False
    )
    print(output_text)
    text = output_text[0]

    object_ref_pattern = r"<\|object_ref_start\|>(.*?)<\|object_ref_end\|>"
    box_pattern = r"<\|box_start\|>(.*?)<\|box_end\|>"

    object_ref = re.search(object_ref_pattern, text).group(1)
    box_content = re.search(box_pattern, text).group(1)

    boxes = [tuple(map(int, pair.strip("()").split(','))) for pair in box_content.split("),(")]
    boxes = [[boxes[0][0], boxes[0][1], boxes[1][0], boxes[1][1]]]

    scaled_boxes = rescale_bounding_boxes(boxes, image.width, image.height)
    return object_ref, scaled_boxes, draw_bounding_boxes(image, scaled_boxes)

css = """
  #output {
    height: 500px; 
    overflow: auto; 
    border: 1px solid #ccc; 
  }
"""
with gr.Blocks(css=css) as demo:
    gr.Markdown(
    """
    # Demo for OS-ATLAS: A Foundation Action Model For Generalist GUI Agents
    """)
    with gr.Row():
        with gr.Column():
            input_img = gr.Image(label="Input Image", type="pil")
            model_selector = gr.Dropdown(choices=list(models.keys()), label="Model", value="OS-Copilot/OS-Atlas-Base-7B")
            text_input = gr.Textbox(label="User Prompt")
            submit_btn = gr.Button(value="Submit")
        with gr.Column():
            model_output_text = gr.Textbox(label="Model Output Text")
            model_output_box = gr.Textbox(label="Model Output Box")
            annotated_image = gr.Image(label="Annotated Image")

    gr.Examples(
        examples=[
            ["assets/web_6f93090a-81f6-489e-bb35-1a2838b18c01.png", "select search textfield"],
            ["assets/web_6f93090a-81f6-489e-bb35-1a2838b18c01.png", "switch to discussions"],
        ],
        inputs=[input_img, text_input],
        outputs=[model_output_text, model_output_box, annotated_image],
        fn=run_example,
        cache_examples=True,
        label="Try examples"
    )

    submit_btn.click(run_example, [input_img, text_input, model_selector], [model_output_text, model_output_box, annotated_image])

demo.launch(debug=True)