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Zero
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import os
import re
import json
import time
import shutil
import uuid
import gc
import tempfile
import unicodedata
from io import BytesIO
from typing import Tuple, Optional, List, Dict, Any
import gradio as gr
import numpy as np
import torch
import spaces
from PIL import Image, ImageDraw, ImageFont
# Transformers & Qwen Utils
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info
# -----------------------------------------------------------------------------
# 1. CONSTANTS & CONFIGURATION
# -----------------------------------------------------------------------------
# Map display names to Hugging Face Repo IDs
MODEL_MAP = {
"Fara-7B": "microsoft/Fara-7B",
"UI-TARS-1.5-7B": "ByteDance-Seed/UI-TARS-1.5-7B"
}
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# System Prompt
OS_SYSTEM_PROMPT = """You are a GUI agent. You are given a task and a screenshot of the current status.
You need to generate the next action to complete the task.
Output your action inside a <tool_call> block using JSON format.
Include "coordinate": [x, y] in pixels for interactions.
Examples:
<tool_call>
{"name": "User", "arguments": {"action": "click", "coordinate": [400, 300]}}
</tool_call>
<tool_call>
{"name": "User", "arguments": {"action": "type", "coordinate": [100, 200], "text": "hello"}}
</tool_call>
"""
# -----------------------------------------------------------------------------
# 2. GLOBAL MODEL STATE MANAGEMENT
# -----------------------------------------------------------------------------
# Global variables to track the currently loaded model
CURRENT_MODEL = None
CURRENT_PROCESSOR = None
CURRENT_MODEL_ID = None
def load_model(model_key: str):
"""
Dynamically loads the requested model.
Unloads the previous model to free up GPU memory if a switch occurs.
"""
global CURRENT_MODEL, CURRENT_PROCESSOR, CURRENT_MODEL_ID
target_repo_id = MODEL_MAP[model_key]
# If the requested model is already loaded, do nothing
if CURRENT_MODEL is not None and CURRENT_MODEL_ID == target_repo_id:
print(f"Model {model_key} is already loaded.")
return
print(f"--- Switching Model to {model_key} ({target_repo_id}) ---")
# 1. Unload existing model to free GPU memory
if CURRENT_MODEL is not None:
print("Unloading current model...")
del CURRENT_MODEL
del CURRENT_PROCESSOR
CURRENT_MODEL = None
CURRENT_PROCESSOR = None
gc.collect()
torch.cuda.empty_cache()
print("Memory cleared.")
# 2. Load new model
try:
print(f"Loading processor for {target_repo_id}...")
processor = AutoProcessor.from_pretrained(target_repo_id, trust_remote_code=True)
print(f"Loading model weights for {target_repo_id}...")
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
target_repo_id,
trust_remote_code=True,
torch_dtype=torch.bfloat16 if DEVICE == "cuda" else torch.float32,
device_map="auto" if DEVICE == "cuda" else None,
)
if DEVICE == "cpu":
model.to("cpu")
model.eval()
# Update global state
CURRENT_MODEL = model
CURRENT_PROCESSOR = processor
CURRENT_MODEL_ID = target_repo_id
print(f"Successfully loaded {model_key}.")
except Exception as e:
print(f"Error loading model {target_repo_id}: {e}")
raise e
def generate_response(messages: list[dict], max_new_tokens=512):
"""
Runs generation using the currently loaded global model.
"""
if CURRENT_MODEL is None or CURRENT_PROCESSOR is None:
raise ValueError("No model loaded.")
text = CURRENT_PROCESSOR.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = CURRENT_PROCESSOR(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to(CURRENT_MODEL.device)
with torch.no_grad():
generated_ids = CURRENT_MODEL.generate(**inputs, max_new_tokens=max_new_tokens)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
return CURRENT_PROCESSOR.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]
# -----------------------------------------------------------------------------
# 3. PARSING & VISUALIZATION LOGIC
# -----------------------------------------------------------------------------
def array_to_image(image_array: np.ndarray) -> Image.Image:
if image_array is None:
raise ValueError("No image provided. Please upload an image.")
return Image.fromarray(np.uint8(image_array))
def get_navigation_prompt(task, image):
return [
{"role": "system", "content": [{"type": "text", "text": OS_SYSTEM_PROMPT}]},
{"role": "user", "content": [
{"type": "image", "image": image},
{"type": "text", "text": f"Instruction: {task}"},
]},
]
def parse_tool_calls(response: str) -> list[dict]:
actions = []
matches = re.findall(r"<tool_call>(.*?)</tool_call>", response, re.DOTALL)
for match in matches:
try:
json_str = match.strip()
data = json.loads(json_str)
args = data.get("arguments", {})
coords = args.get("coordinate", [])
action_type = args.get("action", "unknown")
text_content = args.get("text", "")
if coords and isinstance(coords, list) and len(coords) == 2:
actions.append({
"type": action_type,
"x": float(coords[0]),
"y": float(coords[1]),
"text": text_content,
"raw_json": data
})
except Exception as e:
print(f"Error parsing tool call: {e}")
return actions
def create_localized_image(original_image: Image.Image, actions: list[dict]) -> Optional[Image.Image]:
if not actions:
return None
img_copy = original_image.copy()
draw = ImageDraw.Draw(img_copy)
width, height = img_copy.size
try:
font = ImageFont.load_default()
except:
font = None
colors = {
'type': 'blue',
'click': 'red',
'left_click': 'red',
'right_click': 'purple',
'double_click': 'orange',
'unknown': 'green'
}
for act in actions:
x = act['x']
y = act['y']
# Determine if coords are normalized or absolute
if x <= 1.0 and y <= 1.0 and x > 0:
pixel_x = int(x * width)
pixel_y = int(y * height)
else:
pixel_x = int(x)
pixel_y = int(y)
action_type = act['type']
color = colors.get(action_type, 'green')
# Draw Target
r = 12
draw.ellipse([pixel_x - r, pixel_y - r, pixel_x + r, pixel_y + r], outline=color, width=4)
draw.ellipse([pixel_x - 3, pixel_y - 3, pixel_x + 3, pixel_y + 3], fill=color)
# Label
label_text = f"{action_type}"
if act['text']:
label_text += f": '{act['text']}'"
text_pos = (pixel_x + 15, pixel_y - 10)
bbox = draw.textbbox(text_pos, label_text, font=font)
draw.rectangle(bbox, fill="black")
draw.text(text_pos, label_text, fill="white", font=font)
return img_copy
# -----------------------------------------------------------------------------
# 4. GRADIO PROCESSING LOGIC
# -----------------------------------------------------------------------------
@spaces.GPU(duration=120)
def process_screenshot(input_numpy_image: np.ndarray, task: str, selected_model_key: str) -> Tuple[str, Optional[Image.Image]]:
if input_numpy_image is None:
return "⚠️ Please upload an image first.", None
# 1. Ensure correct model is loaded
load_model(selected_model_key)
# 2. Prepare Data
input_pil_image = array_to_image(input_numpy_image)
prompt = get_navigation_prompt(task, input_pil_image)
# 3. Generate
print(f"Generating with {selected_model_key}...")
raw_response = generate_response(prompt, max_new_tokens=500)
print(f"Raw Output:\n{raw_response}")
# 4. Parse & Visualize
actions = parse_tool_calls(raw_response)
output_image = input_pil_image
if actions:
visualized = create_localized_image(input_pil_image, actions)
if visualized:
output_image = visualized
return raw_response, output_image
# -----------------------------------------------------------------------------
# 5. UI SETUP
# -----------------------------------------------------------------------------
title = "Computer Use Agent (CUA) Playground 🖥️"
description = """
Analyze GUI screenshots and generate action coordinates using State-of-the-Art Vision Language Models.
Supported Models: **Microsoft Fara-7B** and **ByteDance UI-TARS-1.5-7B**.
"""
custom_css = """
#out_img { height: 600px; object-fit: contain; }
"""
with gr.Blocks(theme=gr.themes.Soft(), css=custom_css) as demo:
gr.Markdown(f"<h1 style='text-align: center;'>{title}</h1>")
gr.Markdown(description)
with gr.Row():
with gr.Column():
input_image = gr.Image(label="Upload Screenshot", height=500)
# Model Selector
model_selector = gr.Dropdown(
label="Choose CUA Model",
choices=["Fara-7B", "UI-TARS-1.5-7B"],
value="Fara-7B",
interactive=True
)
task_input = gr.Textbox(
label="Task Instruction",
placeholder="e.g. Input the server address readyforquantum.com...",
lines=2
)
submit_btn = gr.Button("Analyze UI & Generate Action", variant="primary")
with gr.Column():
output_image = gr.Image(label="Visualized Action Points", elem_id="out_img", height=500)
output_text = gr.Textbox(label="Raw Model Output", lines=8, show_copy_button=True)
# Wire up the button
submit_btn.click(
fn=process_screenshot,
inputs=[input_image, task_input, model_selector],
outputs=[output_text, output_image]
)
gr.Examples(
examples=[
["./assets/google.png", "Search for 'Hugging Face'", "Fara-7B"],
["./assets/google.png", "Click the Sign In button", "UI-TARS-1.5-7B"],
],
inputs=[input_image, task_input, model_selector],
label="Quick Examples"
)
if __name__ == "__main__":
# Pre-load the default model on startup to speed up first inference (optional)
# load_model("Fara-7B")
demo.queue().launch() |