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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
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
@spaces.GPU
def run_example(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\|>"
print(text)
print(re.search(object_ref_pattern, text))
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 output_text, 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(
"""
# OS-Atlas Demo
""")
with gr.Tab(label="OS-Atlas Input"):
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")
parsed_boxes = gr.Textbox(label="Parsed Boxes")
annotated_image = gr.Image(label="Annotated Image")
gr.Examples(
examples=[
["assets/web_6f93090a-81f6-489e-bb35-1a2838b18c01.png", "select search textfield"],
],
inputs=[input_img, text_input],
outputs=[model_output_text, parsed_boxes, 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, parsed_boxes, annotated_image])
demo.launch(debug=True) |