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fix conflict

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This commit is contained in:
tsuky_chen
2024-03-21 16:01:31 +08:00
parent 3d2ff5d64e d8c8627e78
commit ca03baacf5
53 changed files with 3072 additions and 298 deletions
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3
.gitignore vendored
View File

@@ -2,6 +2,9 @@
*.pth
*.pt
# Credential files
evaluation_examples/settings/googledrive/credentials.json
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]

9
README.md
View File

@@ -23,8 +23,11 @@ Please refer to [guidance](https://docs.google.com/document/d/1KBdeZwmZs2Vi_Wsnn
2. Install the environment package, download the examples and the virtual machine image.
For x86_64 Linux or Windows, you can install the environment package and download the examples and the virtual machine image by running the following commands:
```bash
pip install desktop-env
gdown xxxx
git clone https://github.com/xlang-ai/DesktopEnv
cd DesktopEnv
pip install -r requirements.txt
gdown https://drive.google.com/drive/folders/1HX5gcf7UeyR-2UmiA15Q9U-
Wr6E6Gio8 -O Ubuntu --folder
vmrun -T ws start "Ubuntu/Ubuntu.vmx" nogui
vmrun -T ws snapshot "Ubuntu/Ubuntu.vmx" "init_state"
```
@@ -89,4 +92,4 @@ If you find this environment useful, please consider citing our work:
journal={arXiv preprint arXiv:xxxx.xxxx},
year={2024}
}
```
```

29
desktop_env/controllers/python.py
View File

@@ -64,14 +64,14 @@ class PythonController:
It can be used to execute the pyautogui commands, or... any other python command. who knows?
"""
# command_list = ["python", "-c", self.pkgs_prefix.format(command=command)]
command_list = ["python3", "-c", self.pkgs_prefix.format(command=command)]
command_list = ["python", "-c", self.pkgs_prefix.format(command=command)]
payload = json.dumps({"command": command_list, "shell": False})
headers = {
'Content-Type': 'application/json'
}
try:
response = requests.post(self.http_server + "/execute", headers=headers, data=payload)
response = requests.post(self.http_server + "/execute", headers=headers, data=payload, timeout=90)
if response.status_code == 200:
logger.info("Command executed successfully: %s", response.text)
else:
@@ -263,16 +263,19 @@ class PythonController:
"""
Ends recording the screen.
"""
response = requests.post(self.http_server + "/end_recording")
if response.status_code == 200:
logger.info("Recording stopped successfully")
with open(dest, 'wb') as f:
for chunk in response.iter_content(chunk_size=8192):
if chunk:
f.write(chunk)
else:
logger.error("Failed to stop recording. Status code: %d", response.status_code)
return None
try:
response = requests.post(self.http_server + "/end_recording")
if response.status_code == 200:
logger.info("Recording stopped successfully")
with open(dest, 'wb') as f:
for chunk in response.iter_content(chunk_size=8192):
if chunk:
f.write(chunk)
else:
logger.error("Failed to stop recording. Status code: %d", response.status_code)
return None
except Exception as e:
logger.error("An error occurred while trying to download the recording: %s", e)
# Additional info
def get_vm_platform(self):
@@ -341,4 +344,4 @@ class PythonController:
return response.json()["directory_tree"]
else:
logger.error("Failed to get directory tree. Status code: %d", response.status_code)
return None
return None

10
desktop_env/envs/desktop_env.py
View File

@@ -58,7 +58,8 @@ class DesktopEnv(gym.Env):
tmp_dir: str = "tmp",
cache_dir: str = "cache",
screen_size: Tuple[int] = (1920, 1080),
headless: bool = False
headless: bool = False,
require_a11y_tree: bool = True,
):
"""
Args:
@@ -77,6 +78,7 @@ class DesktopEnv(gym.Env):
self.cache_dir_base: str = cache_dir
self.vm_screen_size = screen_size # todo: add the logic to get the screen size from the VM
self.headless = headless
self.require_a11y_tree = require_a11y_tree
os.makedirs(self.tmp_dir_base, exist_ok=True)
@@ -248,7 +250,7 @@ class DesktopEnv(gym.Env):
observation = {
"screenshot": self._get_obs(),
"accessibility_tree": self.controller.get_accessibility_tree(),
"accessibility_tree": self.controller.get_accessibility_tree() if self.require_a11y_tree else None,
}
return observation
@@ -284,8 +286,8 @@ class DesktopEnv(gym.Env):
observation = {
"screenshot": self._get_obs(),
"accessibility_tree": self.controller.get_accessibility_tree(),
"terminal": self.controller.get_terminal_output(),
"accessibility_tree": self.controller.get_accessibility_tree() if self.require_a11y_tree else None,
# "terminal": self.controller.get_terminal_output(),
"instruction": self.instruction
}

1
desktop_env/evaluators/metrics/__init__.py
View File

@@ -77,6 +77,7 @@ from .general import (
literal_match
)
from .gimp import (
check_structure_sim_resized,
check_brightness_decrease_and_structure_sim,
check_contrast_increase_and_structure_sim,
check_saturation_increase_and_structure_sim,

52
desktop_env/evaluators/metrics/gimp.py
View File

@@ -430,11 +430,11 @@ def check_image_size(src_path, rule):
img = Image.open(src_path)
# Check the size
if rule["height"] is not None:
if rule.get("height", None) is not None:
height_same = img.size[1] == rule["height"]
else:
height_same = True
if rule["width"] is not None:
if rule.get("width", None) is not None:
width_same = img.size[0] == rule["width"]
else:
width_same = True
@@ -568,3 +568,51 @@ def check_image_file_size(src_path, rule):
return 1.0
else:
return 0.0
if __name__ == "__main__":
actual_config_path = "../../../cache/sessionrc_test"
rule = {
"key": "hide-docks",
"value": "no"
}
print(check_config_status(actual_config_path, rule))
actual_config_path = "../../../cache/action-history_test"
rule = {
"key": ["history-item", "\"filters-vignette\""],
"value": "1"
}
print(check_config_status(actual_config_path, rule))
actual_config_path = "../../../cache/gimprc_test"
rule = {
"key": "undo-levels",
"value": "100"
}
print(check_config_status(actual_config_path, rule))
src_path = "../../../cache/734d6579-c07d-47a8-9ae2-13339795476b/green_background_with_object.png"
tgt_path = "../../../cache/734d6579-c07d-47a8-9ae2-13339795476b/white_background_with_object.png"
print(check_green_background(src_path, tgt_path))
tgt_path = "../../../cache/f4aec372-4fb0-4df5-a52b-79e0e2a5d6ce/Triangle_In_The_Middle.png"
print(check_triangle_position(tgt_path))
src_path = "../../../cache/bb7db4c2-30b5-4be7-8dd7-b8c4ec7d3108/anmi_sharper.png"
tgt_path = "../../../cache/bb7db4c2-30b5-4be7-8dd7-b8c4ec7d3108/anmi.png"
print(check_sharper(src_path, tgt_path))
src_path = "../../../cache/3c8f201a-009d-4bbe-8b65-a6f8b35bb57f/compressed.jpeg"
rule = {
"max_size": 500000
}
print(check_image_file_size(src_path, rule))
src_path = "../../../cache/d16c99dc-2a1e-46f2-b350-d97c86c85c15/resized.png"
tgt_path = "../../../cache/d16c99dc-2a1e-46f2-b350-d97c86c85c15/dog_with_background.png"
rule = {
"height": 512
}
print(check_image_size(src_path, rule))
print(check_structure_sim_resized(src_path, tgt_path))

6
desktop_env/evaluators/metrics/vscode.py
View File

@@ -236,6 +236,9 @@ def check_html_background_image(src_path: str, rule: Dict = None) -> float:
Check if the background image is correctly set.
multi-app:bb7db4c2-30b5-4be7-8dd7-b8c4ec7d3108
"""
if not src_path:
return 0.0
from bs4 import BeautifulSoup
with open(src_path, 'r') as f:
html_content = f.read()
@@ -252,6 +255,9 @@ def compare_result_files(src_path, tgt_path):
Compare whether the content of two files are the same.
multi-app:7f35355e-02a6-45b5-b140-f0be698bcf85
"""
if not src_path or not tgt_path:
return 0.0
with open(src_path, 'r') as f:
src_content = f.read().strip()
with open(tgt_path, 'r') as f:

32
desktop_env/server/main.py
View File

@@ -63,7 +63,7 @@ def execute_command():
# Execute the command without any safety checks.
try:
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=shell, text=True)
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=shell, text=True, timeout=120)
return jsonify({
'status': 'success',
'output': result.stdout,
@@ -414,9 +414,18 @@ def _create_pywinauto_node(node: BaseWrapper, depth: int = 0, flag: Optional[str
attribute_dict: Dict[str, Any] = {"name": node.element_info.name}
# States {{{ #
attribute_dict["{{{:}}}enabled".format(_accessibility_ns_map["st"])] = str(node.is_enabled()).lower()
attribute_dict["{{{:}}}visible".format(_accessibility_ns_map["st"])] = str(node.is_visible()).lower()
attribute_dict["{{{:}}}active".format(_accessibility_ns_map["st"])] = str(node.is_active()).lower()
try:
attribute_dict["{{{:}}}enabled".format(_accessibility_ns_map["st"])] = str(node.is_enabled()).lower()
except:
pass
try:
attribute_dict["{{{:}}}visible".format(_accessibility_ns_map["st"])] = str(node.is_visible()).lower()
except:
pass
try:
attribute_dict["{{{:}}}active".format(_accessibility_ns_map["st"])] = str(node.is_active()).lower()
except:
pass
if hasattr(node, "is_minimized"):
try:
@@ -545,12 +554,14 @@ def _create_pywinauto_node(node: BaseWrapper, depth: int = 0, flag: Optional[str
node_role_name = "".join( map( lambda ch: ch if ch.isidentifier()\
or ch in {"-"}\
or ch.isalnum()
else "-"
else "-"
, node_role_name
)
)
if node_role_name.strip() == "":
node_role_name = "unknown"
if not node_role_name[0].isalpha():
node_role_name = "tag" + node_role_name
xml_node = lxml.etree.Element(
node_role_name,
@@ -601,9 +612,14 @@ def get_accessibility_tree():
@app.route('/screen_size', methods=['POST'])
def get_screen_size():
d = display.Display()
screen_width = d.screen().width_in_pixels
screen_height = d.screen().height_in_pixels
if platform_name=="Linux":
d = display.Display()
screen_width = d.screen().width_in_pixels
screen_height = d.screen().height_in_pixels
elif platform_name=="Windows":
user32 = ctypes.windll.user32
screen_width: int = user32.GetSystemMetrics(0)
screen_height: int = user32.GetSystemMetrics(1)
return jsonify(
{
"width": screen_width,

96
evaluation_examples/examples/Windows/multi_app/185f29bd-5da0-40a6-b69c-ba7f4e0324ef.json Normal file
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@@ -0,0 +1,96 @@
{
"id": "185f29bd-5da0-40a6-b69c-ba7f4e0324ef",
"snapshot": "libreoffice_calc",
"instruction": "Transfer the data from our 'Employee Performance Evaluation Summary' Excel sheet into our standardized PDF evaluation forms. Each employee's evaluation data should be accurately filled into the designated fields of the PDF form. It's crucial that the final PDF documents retain a uniform and professional look, ready for distribution to our staff or for filing purposes. Furthermore, please ensure that each PDF file is named according to the employee's name as it appears in the Excel document. This will greatly streamline our evaluation process and enhance our efficiency in managing employee performance records. Oh, use \"√\" as mark on characters.",
"source": "authors",
"config": [
{
"type": "download",
"parameters": {
"files": [
{
"path": "C:\\Users\\chenj\\Desktop\\Employee Performance Evaluation Summary.xlsx",
"url": "https://drive.google.com/uc?id=1uOzi66bzO_WUnoS4Oqsodrd7_YPLatEk&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\review_template.pdf",
"url": "https://drive.google.com/uc?id=1YJ4RPGFUuS48tBh31gBerA16JSMw498w&export=download"
}
]
}
},
{
"type": "open",
"parameters": {
"path": "C:\\Users\\chenj\\Desktop\\Employee Performance Evaluation Summary.xlsx"
}
},
{
"type": "sleep",
"parameters": {
"seconds": 2
}
},
{
"type": "open",
"parameters": {
"path": "C:\\Users\\chenj\\Desktop\\review_template.pdf"
}
}
],
"trajectory": "trajectories/185f29bd-5da0-40a6-b69c-ba7f4e0324ef",
"related_apps": [
"libreoffice_calc",
"os",
"pdf"
],
"evaluator": {
"func": "compare_pdfs",
"result": {
"type": "cloud_file",
"path": [
"https://drive.google.com/uc?id=1kZM90nA1krRmV9ug5_BBe8VlrZRVLiLK&export=download",
"https://drive.google.com/uc?id=1zyLzYYThwyit9ciXpfNfPFlBomolOauY&export=download",
"https://drive.google.com/uc?id=1gMT7JBftuymajMAO5rwksORpeVq3uGmH&export=download",
"https://drive.google.com/uc?id=1x0DdtUSZyBifl1tGIWlWKn255WusJeR4&export=download",
"https://drive.google.com/uc?id=1UAcG32WO8XCXElcanjGwbSpJwFuyOkts&export=download",
"https://drive.google.com/uc?id=1PRgryg7Y5evKnDG2LPtAttVp9qAf5VyZ&export=download",
"https://drive.google.com/uc?id=1JxEDriCS2W7BQLdkIgxu_WFCRa9ib4D7&export=download"
],
"dest": [
"Alex Lee_Gold.pdf",
"David Wilson_Gold.pdf",
"Emily Johnson_Gold.pdf",
"John Doe_Gold.pdf",
"Linda Green_Gold.pdf",
"Michael Brown_Gold.pdf",
"Sophia Carter_Gold.pdf"
],
"multi": true,
"gives": [0,1,2,3,4,5,6]
},
"expected": {
"type": "vm_file",
"path": [
"C:\\Users\\chenj\\Desktop\\Alex Lee.pdf",
"C:\\Users\\chenj\\Desktop\\David Wilson.pdf",
"C:\\Users\\chenj\\Desktop\\Emily Johnson.pdf",
"C:\\Users\\chenj\\Desktop\\John Doe.pdf",
"C:\\Users\\chenj\\Desktop\\Linda Green.pdf",
"C:\\Users\\chenj\\Desktop\\Michael Brown.pdf",
"C:\\Users\\chenj\\Desktop\\Sophia Carter.pdf"
],
"dest": [
"Alex Lee.pdf",
"David Wilson.pdf",
"Emily Johnson.pdf",
"John Doe.pdf",
"Linda Green.pdf",
"Michael Brown.pdf",
"Sophia Carter.pdf"
],
"multi": true,
"gives": [0,1,2,3,4,5,6]
}
}
}

109
evaluation_examples/examples/Windows/multi_app/1f18aa87-af6f-41ef-9853-cdb8f32ebdea.json Normal file
View File

@@ -0,0 +1,109 @@
{
"id": "1f18aa87-af6f-41ef-9853-cdb8f32ebdea",
"snapshot": "libreoffice_calc",
"instruction": "I've prepared some grammar tests and placed them in the 'Grammar test' folder. I've already provided the multiple-choice answers for Test 1 in the 'answer doc' file. Could you please follow the same format to write out the answers for the remaining two tests in the doc file? This way, I can distribute them to the students as a reference. Thank you.",
"source": "authors",
"config": [
{
"type": "command",
"parameters": {
"command": "mkdir \"C:\\Users\\chenj\\Desktop\\students work\" \"C:\\Users\\chenj\\Desktop\\Lec powerpoint\" \"C:\\Users\\chenj\\Desktop\\Grammar test\" \"C:\\Users\\chenj\\Desktop\\Grammar rules PDF\" C:\\Users\\chenj\\Desktop\\FDI",
"shell": true
}
},
{
"type": "download",
"parameters": {
"files": [
{
"path": "C:\\Users\\chenj\\Desktop\\Grammer test 1.docx",
"url": "https://drive.google.com/uc?id=1VaXQ9XdzMv079xKFs0Y2XrwdmwFHIvBK&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Grammer test 2.docx",
"url": "https://drive.google.com/uc?id=1k2T88WreTwi-Yyp9mEJnreEQC3DdkJ2x&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Grammer test 3.docx",
"url": "https://drive.google.com/uc?id=1QgyQWVOcAJuPaSlrywb9nuFiQDySsTb2&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Answer.docx",
"url": "https://drive.google.com/uc?id=1BC2DuWJuZggmf6fXl6Ys9xQMZzU6a1br&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Grammar rules PDF\\irregularrules02.pdf",
"url": "https://drive.google.com/uc?id=1Eln9ehX6y6Df2-S_Hp7Ao1teKRu6I1Tg&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Grammar rules PDF\\irregularrules01.pdf",
"url": "https://drive.google.com/uc?id=1krdEEdNWvTwMKZU14QtI_xc2lCFVeVcl&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Grammar rules PDF\\fragrules.pdf",
"url": "https://drive.google.com/uc?id=1IXyI2KeiXsuh6XV2LelcmhZ2PDh_dBQf&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Grammar rules PDF\\csfsrules.pdf",
"url": "https://drive.google.com/uc?id=1ernwGGrjhYNoHVNAevdb2qNKQ0I5n3RP&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Public Lecture Teaching Plan.docx",
"url": "https://drive.google.com/uc?id=1ywfVFTEbiSkypZpzLjLmq_ppSbQIC8s8&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Course Timetable.xlsx",
"url": "https://drive.google.com/uc?id=1NGtahknRq_kXsXlw0tRQ1_CZp9SljoVg&export=download"
}
]
}
}
],
"trajectory": "trajectories/1f18aa87-af6f-41ef-9853-cdb8f32ebdea",
"related_apps": [
"os",
"libreoffice_writer"
],
"evaluator": {
"postconfig": [
{
"type": "activate_window",
"parameters": {
"window_name": "Answer - Word",
"strict": true
}
},
{
"type": "sleep",
"parameters": {
"seconds": 0.5
}
},
{
"type": "execute",
"parameters": {
"command": [
"python",
"-c",
"import pyautogui; import time; pyautogui.hotkey(\"ctrl\", \"s\"); time.sleep(0.5); pyautogui.press(\"enter\");"
]
}
}
],
"func": "compare_docx_files",
"expected": {
"type": "cloud_file",
"path": "https://drive.google.com/uc?id=1TOMGWC3OFuP6yEGQuRJMEFWdg2NcBPSs&export=download",
"dest": "Answer gold.docx"
},
"result": {
"type": "vm_file",
"path": "C:\\Users\\chenj\\Desktop\\Answer.docx",
"dest": "Answer.docx"
},
"options": {
"ignore_case": true,
"ignore_blanks": true
}
}
}

68
evaluation_examples/examples/Windows/multi_app/26660ad1-6ebb-4f59-8cba-a8432dfe8d38.json Normal file
View File

@@ -0,0 +1,68 @@
{
"id": "26660ad1-6ebb-4f59-8cba-a8432dfe8d38",
"snapshot": "multiapps",
"instruction": "I want to test the quality of the network environment my laptop is currently in. Please measure my network situation through speedtest.net, export the measurement results, and save them to Documents\\Test\\Speed (if the dir does not exist, create it).",
"source": "https://www.speedtest.net/",
"config": [
{
"type": "launch",
"parameters": {
"command": [
"C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe",
"--remote-debugging-port=1337"
]
}
},
{
"type": "launch",
"parameters": {
"command": [
"ncat.exe", "-k", "-l", "0.0.0.0", "9222",
"--sh-exec", "ncat.exe 127.0.0.1 1337"
]
}
},
{
"type": "chrome_open_tabs",
"parameters": {
"urls_to_open": [
"https://www.speedtest.net/"
]
}
},
{
"type": "activate_window",
"parameters": {
"window_name": "Google Chrome"
}
},
{
"type": "execute",
"parameters": {
"command": [
"python",
"-c",
"import pyautogui; import time; time.sleep(0.5);"
]
}
}
],
"trajectory": "trajectories/",
"related_apps":[
"os",
"browser"
],
"evaluator":{
"func": "compare_time_in_speedtest_results",
"result":{
"type": "vm_file",
"path": "C:\\Users\\chenj\\Documents\\Test\\Speed\\Speedtest Results Export-.csv",
"dest": "Speedtest Results Export-.csv",
"time_suffix": true
},
"expected":{
"type": "time_diff_range",
"diff_range_in_minutes": "60"
}
}
}

127
evaluation_examples/examples/Windows/multi_app/2c1ebcd7-9c6d-4c9a-afad-900e381ecd5e.json Normal file
View File

@@ -0,0 +1,127 @@
{
"id": "2c1ebcd7-9c6d-4c9a-afad-900e381ecd5e",
"snapshot": "libreoffice_calc",
"instruction": "Could you please take a moment to review the 'case study' file located within the 'student work' folder? I'm particularly interested in ensuring that the references section at the end of the document adheres to the APA 7th edition formatting guidelines. Making the necessary adjustments if it turns out that the current formatting does not align with APA 7 standards or exists some errors.",
"source": "authors",
"config": [
{
"type": "command",
"parameters": {
"command": "mkdir \"C:\\Users\\chenj\\Desktop\\students work\" \"C:\\Users\\chenj\\Desktop\\Lec powerpoint\" \"C:\\Users\\chenj\\Desktop\\Grammar test\" \"C:\\Users\\chenj\\Desktop\\Grammar rules PDF\" C:\\Users\\chenj\\Desktop\\FDI",
"shell": true
}
},
{
"type": "download",
"parameters": {
"files": [
{
"path": "C:\\Users\\chenj\\Desktop\\students work\\Zheng He .docx",
"url": "https://drive.google.com/uc?id=1wI4141LAthnY5m6qcCUaGgDooe4wiTgz&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\students work\\The literature reviews of weekly readings.docx",
"url": "https://drive.google.com/uc?id=18zoZCNtP-wTkxXp2FhH3O_NdLZKVMPIr&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\students work\\The British Justice System.docx",
"url": "https://drive.google.com/uc?id=1z3YHSN4CvC5kN1AwTWB_-plRS4p5GAch&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\students work\\quiz2.docx",
"url": "https://drive.google.com/uc?id=1R5Bii_kvnv_fZVXV-6DMt6Hgq-1gXMo1&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\students work\\quiz.docx",
"url": "https://drive.google.com/uc?id=1PvlGMVX7YkricrjoPRe0e5VQlHeozRPD&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\students work\\Q1&2&3.docx",
"url": "https://drive.google.com/uc?id=1kLQ3lnba6p9lqikHhKDdbqrYagHnZWU_&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\students work\\Photo Ethics in Journalism.docx",
"url": "https://drive.google.com/uc?id=1V6nG6HP_9Kb5KBCRTpaGsRTdPxnJSmRm&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\students work\\cassie.docx",
"url": "https://drive.google.com/uc?id=1cW9TGJy56vossXxDsdnutPyCbR70af7M&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\students work\\case study.docx",
"url": "https://drive.google.com/uc?id=11GzpoZvp4qnL2ukXdpbhH-a3zOIHhtDx&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Grammar rules PDF\\irregularrules02.pdf",
"url": "https://drive.google.com/uc?id=1Eln9ehX6y6Df2-S_Hp7Ao1teKRu6I1Tg&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Grammar rules PDF\\irregularrules01.pdf",
"url": "https://drive.google.com/uc?id=1krdEEdNWvTwMKZU14QtI_xc2lCFVeVcl&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Grammar rules PDF\\fragrules.pdf",
"url": "https://drive.google.com/uc?id=1IXyI2KeiXsuh6XV2LelcmhZ2PDh_dBQf&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Grammar rules PDF\\csfsrules.pdf",
"url": "https://drive.google.com/uc?id=1ernwGGrjhYNoHVNAevdb2qNKQ0I5n3RP&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Public Lecture Teaching Plan.docx",
"url": "https://drive.google.com/uc?id=1ywfVFTEbiSkypZpzLjLmq_ppSbQIC8s8&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Course Timetable.xlsx",
"url": "https://drive.google.com/uc?id=1NGtahknRq_kXsXlw0tRQ1_CZp9SljoVg&export=download"
}
]
}
}
],
"trajectory": "trajectories/2c1ebcd7-9c6d-4c9a-afad-900e381ecd5e",
"related_apps": [
],
"evaluator": {
"postconfig": [
{
"type": "activate_window",
"parameters": {
"window_name": "case study - Word",
"strict": true
}
},
{
"type": "sleep",
"parameters": {
"seconds": 0.5
}
},
{
"type": "execute",
"parameters": {
"command": [
"python",
"-c",
"import pyautogui; import time; pyautogui.hotkey(\"ctrl\", \"s\"); time.sleep(0.5); pyautogui.press(\"enter\");"
]
}
}
],
"func": "compare_references",
"expected": {
"type": "cloud_file",
"path": "https://drive.google.com/uc?id=1325Qfch0JaJ_wJ20ICxMoHeW8KLpK8v0&export=download",
"dest": "case study gold.docx"
},
"result": {
"type": "vm_file",
"path": "C:\\Users\\chenj\\Desktop\\students work\\case study.docx",
"dest": "case study.docx"
},
"options": {
"content_only": true,
"reference_base_result": 0.92
}
}
}

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{
"id": "3a93cae4-ad3e-403e-8c12-65303b271818",
"snapshot": "libreoffice_calc",
"instruction": "Could you please add a two-hour lecture slot to my weekly course timetable, scheduled for every Wednesday at 12 PM? It seems I accidentally omitted that when setting up my schedule. I'd appreciate you taking care of that for me. Thanks!",
"source": "authors",
"config": [
{
"type": "command",
"parameters": {
"command": "mkdir \"C:\\Users\\chenj\\Desktop\\students work\" \"C:\\Users\\chenj\\Desktop\\Lec powerpoint\" \"C:\\Users\\chenj\\Desktop\\Grammar test\" \"C:\\Users\\chenj\\Desktop\\Grammar rules PDF\" C:\\Users\\chenj\\Desktop\\FDI",
"shell": true
}
},
{
"type": "download",
"parameters": {
"files": [
{
"path": "C:\\Users\\chenj\\Desktop\\students work\\Zheng He .docx",
"url": "https://drive.google.com/uc?id=1wI4141LAthnY5m6qcCUaGgDooe4wiTgz&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\students work\\cassie.docx",
"url": "https://drive.google.com/uc?id=1cW9TGJy56vossXxDsdnutPyCbR70af7M&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\students work\\case study.docx",
"url": "https://drive.google.com/uc?id=11GzpoZvp4qnL2ukXdpbhH-a3zOIHhtDx&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Grammar rules PDF\\irregularrules02.pdf",
"url": "https://drive.google.com/uc?id=1Eln9ehX6y6Df2-S_Hp7Ao1teKRu6I1Tg&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Grammar rules PDF\\irregularrules01.pdf",
"url": "https://drive.google.com/uc?id=1krdEEdNWvTwMKZU14QtI_xc2lCFVeVcl&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Grammar rules PDF\\fragrules.pdf",
"url": "https://drive.google.com/uc?id=1IXyI2KeiXsuh6XV2LelcmhZ2PDh_dBQf&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Grammar rules PDF\\csfsrules.pdf",
"url": "https://drive.google.com/uc?id=1ernwGGrjhYNoHVNAevdb2qNKQ0I5n3RP&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Public Lecture Teaching Plan.docx",
"url": "https://drive.google.com/uc?id=1ywfVFTEbiSkypZpzLjLmq_ppSbQIC8s8&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\Course Timetable.xlsx",
"url": "https://drive.google.com/uc?id=1NGtahknRq_kXsXlw0tRQ1_CZp9SljoVg&export=download"
}
]
}
}
],
"trajectory": "trajectories/3a93cae4-ad3e-403e-8c12-65303b271818",
"related_apps": [
"os",
"libreoffice_calc"
],
"evaluator": {
"postconfig": [
{
"type": "activate_window",
"parameters": {
"window_name": "Course Timetable - Excel",
"strict": true
}
},
{
"type": "sleep",
"parameters": {
"seconds": 0.5
}
},
{
"type": "execute",
"parameters": {
"command": [
"python",
"-c",
"import pyautogui; import time; pyautogui.hotkey(\"ctrl\", \"s\"); time.sleep(0.5); pyautogui.press(\"enter\");"
]
}
}
],
"func": [
"compare_table",
"compare_table",
"compare_table"
],
"result": [
{
"type": "vm_file",
"path": "C:\\Users\\chenj\\Desktop\\Course Timetable.xlsx",
"dest": "Course Timetable.xlsx"
},
{
"type": "vm_file",
"path": "C:\\Users\\chenj\\Desktop\\Course Timetable.xlsx",
"dest": "Course Timetable.xlsx"
},
{
"type": "vm_file",
"path": "C:\\Users\\chenj\\Desktop\\Course Timetable.xlsx",
"dest": "Course Timetable.xlsx"
}
],
"expected": [
{
"type": "cloud_file",
"path": "https://drive.google.com/uc?id=1VMOon8byWuoCW2Uk5etGMJLMzAfwFVyB&export=download",
"dest": "Course Timetable gold.xlsx"
},
{
"type": "cloud_file",
"path": "https://drive.google.com/uc?id=1jAThiIqILZ5t-RFPHVniSvAL8ZJO1H3P&export=download",
"dest": "Course Timetable gold 2.xlsx"
},
{
"type": "cloud_file",
"path": "https://drive.google.com/uc?id=1U0THDtPCgsw-Rb0N9fjF8DeOepPeUajP&export=download",
"dest": "Course Timetable gold 3.xlsx"
}
],
"options": [
{
"rules": [
{
"type": "sheet_data",
"sheet_idx0": "RNSheet1",
"sheet_idx1": "ENSheet1",
"ignore_case": true
}
]
},
{
"rules": [
{
"type": "sheet_data",
"sheet_idx0": "RNSheet1",
"sheet_idx1": "ENSheet1",
"ignore_case": true
}
]
},
{
"rules": [
{
"type": "sheet_data",
"sheet_idx0": "RNSheet1",
"sheet_idx1": "ENSheet1",
"ignore_case": true
}
]
}
],
"conj": "or"
}
}

143
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{
"id": "46407397-a7d5-4c6b-92c6-dbe038b1457b",
"snapshot": "chrome",
"instruction": "Help me export charts, graph or other images from docx files received in email \"Lecture Document\" in Notes folder and upload these png files to the figures/ folder in Google Drive for later use (use numbers to name them).",
"source": "https://marketplace.uipath.com/listings/merge-pdfs-from-gmail-email-attachments-and-upload-to-gogle-drive",
"config": [
{
"type": "googledrive",
"parameters": {
"settings_file": "evaluation_examples/settings/googledrive/settings.yml",
"operation": ["delete"],
"args": [
{
"query": "title = 'figures' and 'root' in parents and mimeType = 'application/vnd.google-apps.folder'",
"trash": false
}
]
}
},
{
"type": "launch",
"parameters": {
"command": [
"C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe",
"--remote-debugging-port=1337"
]
}
},
{
"type": "launch",
"parameters": {
"command": [
"ncat.exe", "-k", "-l", "0.0.0.0", "9222",
"--sh-exec", "ncat.exe 127.0.0.1 1337"
]
}
},
{
"type": "chrome_open_tabs",
"parameters": {
"urls_to_open": [
"https://news.google.com",
"https://x.com"
]
}
},
{
"type": "login",
"parameters": {
"settings_file": "evaluation_examples/settings/google/settings.json",
"platform": "googledrive"
}
},
{
"type": "download",
"parameters": {
"files": [
{
"url": "https://drive.google.com/uc?id=1Yy-ZrkMq4pIQq1Y75bD2WVJXxHMTaMqE&export=download",
"path": "C:\\Users\\chenj\\thunderbird-profile.7z"
}
]
}
},
{
"type": "execute",
"parameters": {
"command": [
"C:\\Program Files\\7-Zip\\7z.exe",
"x", "C:\\Users\\chenj\\thunderbird-profile.7z"
]
}
},
{
"type": "execute",
"parameters": {
"command": "rd /s /q C:\\Users\\chenj\\AppData\\Roaming\\Thunderbird",
"shell": true
}
},
{
"type": "execute",
"parameters": {
"command": "move C:\\Users\\chenj\\Thunderbird C:\\Users\\chenj\\AppData\\Roaming\\Thunderbird",
"shell": true
}
},
{
"type": "launch",
"parameters": {
"command": [
"C:\\Program Files\\Mozilla Thunderbird\\thunderbird.exe"
]
}
}
],
"trajectory": "trajectories/",
"related_apps": [
"thunderbird",
"chrome"
],
"evaluator": {
"func": "compare_image_list",
"result": {
"type": "googledrive_file",
"settings_file": "evaluation_examples/settings/googledrive/settings.yml",
"query_list": [
[
"title = 'figures' and trashed = false and 'root' in parents and mimeType = 'application/vnd.google-apps.folder'",
"title = '1.png' and trashed = false"
],
[
"title = 'figures' and trashed = false and 'root' in parents and mimeType = 'application/vnd.google-apps.folder'",
"title = '2.png' and trashed = false"
],
[
"title = 'figures' and trashed = false and 'root' in parents and mimeType = 'application/vnd.google-apps.folder'",
"title = '3.png' and trashed = false"
]
],
"dest": [
"1.png",
"2.png",
"3.png"
]
},
"expected": {
"type": "cloud_file",
"path": [
"https://drive.usercontent.google.com/download?id=19J5tzWjx9hdo-n0MC3upzAntVMa8WUgk&export=download&authuser=0&confirm=t&uuid=be790579-8db9-4bd2-a757-beb27af386af&at=APZUnTVM2PjNDXhlwFZ6WAFdNVsD:1706497547717",
"https://drive.usercontent.google.com/download?id=1S04RpR5dk80LylIYGvA4e3sAUBd6wdlQ&export=download&authuser=0&confirm=t&uuid=b302de03-04f7-455c-ab0c-b3cbbeb6929a&at=APZUnTVD8zMZGO1_GWaFUm1cNXul:1706497555463",
"https://drive.usercontent.google.com/download?id=11NRLh93RTzEd0Cy-cYwMyNJSFG7-vP9c&export=download&authuser=0&confirm=t&uuid=02500115-dea3-481a-af4f-a723d9a62169&at=APZUnTW9-gENlsyfdIPA4PTA0emh:1706497560874"
],
"dest": [
"1_gold.png",
"2_gold.png",
"3_gold.png"
],
"multi": true,
"gives": [0, 1, 2]
}
}
}

18
evaluation_examples/examples/Windows/multi_app/6d72aad6-187a-4392-a4c4-ed87269c51cf.json Normal file
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{
"id": "6d72aad6-187a-4392-a4c4-ed87269c51cf",
"snapshot": "libreoffice_calc",
"instruction": "Could you please converting MS Office PowerPoint presentation to video and play it with VLC?",
"source": "https://superuser.com/questions/923171/converting-openoffice-impress-presentation-to-video-without-screen-recording",
"config": [
],
"trajectory": "trajectories/6d72aad6-187a-4392-a4c4-ed87269c51cf",
"related_apps": [
"excel",
"powerpoint",
"word",
"vlc"
],
"evaluator": {
"func": "infeasible"
}
}

72
evaluation_examples/examples/Windows/multi_app/6f4073b8-d8ea-4ade-8a18-c5d1d5d5aa9a.json Normal file
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{
"id": "6f4073b8-d8ea-4ade-8a18-c5d1d5d5aa9a",
"snapshot": "multiapps",
"instruction": "I now want to count the meeting cities of the three machine learning conferences in the past ten years from 2013 to 2019(including 2013 and 2019). I have listed the names and years of the conferences in excel. Please fill in the vacant locations.",
"source": "author",
"config": [
{
"type": "download",
"parameters": {
"files": [
{
"url": "https://drive.google.com/uc?export=download&id=19wUxTQeoKr6ihJWJ_9cu2tzKQH0cnxWH",
"path": "C:\\Users\\chenj\\Desktop\\ConferenceCity.xlsx"
}
]
}
},
{
"type": "open",
"parameters": {
"path": "C:\\Users\\chenj\\Desktop\\ConferenceCity.xlsx"
}
}
],
"trajectory": "trajectories/",
"related_apps": [
"calc", "chrome", "os"
],
"evaluator": {
"postconfig":[
{
"type": "download",
"parameters": {
"files": [
{
"url": "https://drive.google.com/uc?export=download&id=1ZcITkIOs2Z86S5L6MShSohFs3_xVfeCP",
"path": "C:\\Users\\chenj\\Desktop\\ConferenceCity_Gold.xlsx"
}
]
}
},
{
"type": "activate_window",
"parameters": {
"window_name": "ConferenceCity - Excel"
}
},
{
"type": "execute",
"parameters": {
"command": [
"python",
"-c",
"import pyautogui; import time; pyautogui.hotkey(\"ctrl\", \"s\"); time.sleep(0.5); pyautogui.press(\"enter\");"
]
}
}
],
"func": "compare_conference_city_in_order",
"expected": {
"type": "rule",
"rules":{
"expected": ["Scottsdale","Atlanta","Lake Tahoe","Banff","Beijing",["Montreal", "Montréal"],"San Diego","Lille",["Montreal", "Montréal"],"San Juan",["New York", "New York City", "NYC"],"Barcelona","Toulon","Sydney","Long Beach","Vancouver","Stockholm",["Montreal", "Montréal"],"New Orleans","Long Beach","Vancouver"]
}
},
"result": {
"type": "vm_file",
"path": "C:\\Users\\chenj\\Desktop\\ConferenceCity.xlsx",
"dest": "ConferenceCity.xlsx"
}
}
}

170
evaluation_examples/examples/Windows/multi_app/74d5859f-ed66-4d3e-aa0e-93d7a592ce41.json Normal file
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{
"id": "74d5859f-ed66-4d3e-aa0e-93d7a592ce41",
"snapshot": "chrome",
"instruction": "Help me to set up an initial web extension project with help of the web tool, tagging it \"happy-extension v0.0.1\". Leave description blank for now. Include a background script and browser action, while other features are not required. Remember to unzip the auto-generated folder into \"Documents\\Projects\".",
"source": "authors",
"config": [
{
"type": "launch",
"parameters": {
"command": [
"C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe",
"--remote-debugging-port=1337"
]
}
},
{
"type": "launch",
"parameters": {
"command": [
"ncat.exe", "-k", "-l", "0.0.0.0", "9222",
"--sh-exec", "ncat.exe 127.0.0.1 1337"
]
}
},
{
"type": "chrome_open_tabs",
"parameters": {
"urls_to_open": [
"https://webext.eu"
]
}
},
{
"type": "execute",
"parameters": {
"command": "mkdir C:\\Users\\chenj\\Documents\\Projects",
"shell": "true"
}
},
{
"type": "launch",
"parameters": {
"command": [
"explorer.exe",
"C:\\Users\\chenj\\Documents\\Projects"
]
}
}
],
"trajectory": "trajectories/74d5859f-ed66-4d3e-aa0e-93d7a592ce41",
"related_apps": [
"chrome",
"os"
],
"evaluator": {
"func": [
"check_json",
"diff_text_file",
"diff_text_file",
"diff_text_file",
"diff_text_file"
],
"result": [
{
"type": "vm_file",
"path": "C:\\Users\\chenj\\Documents\\Projects\\happy-extension\\manifest.json",
"dest": "manifest.json"
},
{
"type": "vm_file",
"path": "C:\\Users\\chenj\\Documents\\Projects\\happy-extension\\background_script.js",
"dest": "background_script.js"
},
{
"type": "vm_file",
"path": "C:\\Users\\chenj\\Documents\\Projects\\happy-extension\\browserAction\\index.html",
"dest": "index.html"
},
{
"type": "vm_file",
"path": "C:\\Users\\chenj\\Documents\\Projects\\happy-extension\\browserAction\\style.css",
"dest": "style.css"
},
{
"type": "vm_file",
"path": "C:\\Users\\chenj\\Documents\\Projects\\happy-extension\\browserAction\\script.js",
"dest": "script.js"
}
],
"expected": [
{
"type": "rule",
"rules": {
"expect": [
{
"key": [
"name"
],
"method": "eq",
"ref": "happy-extension"
},
{
"key": [
"version"
],
"method": "eq",
"ref": "0.0.1"
},
{
"key": [
"background",
"scripts"
],
"method": "eq",
"ref": [
"background_script.js"
]
},
{
"key": [
"browser_action",
"default_icon"
],
"method": "eq",
"ref": {
"64": "icons/icon.png"
}
},
{
"key": [
"browser_action",
"default_popup"
],
"method": "eq",
"ref": "browserAction/index.html"
},
{
"key": [
"browser_action",
"default_title"
],
"method": "eq",
"ref": "happy-extension"
}
]
}
},
{
"type": "cloud_file",
"path": "https://drive.google.com/uc?id=1t5Llhn6seDUXVs-eILu6CjwFEQL9Z5Qm&export=download",
"dest": "background_script.js"
},
{
"type": "cloud_file",
"path": "https://drive.google.com/uc?id=19fMAsWd6q4ElLdOceJ-otHbxRJA_pc_U&export=download",
"dest": "index.html"
},
{
"type": "cloud_file",
"path": "https://drive.google.com/uc?id=1fwfiRPjdug8uh6z23RFO1JtlGH_L_Hl_&export=download",
"dest": "style.css"
},
{
"type": "cloud_file",
"path": "https://drive.google.com/uc?id=14YYnhCfRtHQNk8M4fBPaUQeteoFMGBsA&export=download",
"dest": "script.js"
}
]
}
}

96
evaluation_examples/examples/Windows/multi_app/897e3b53-5d4d-444b-85cb-2cdc8a97d903.json Normal file
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{
"id": "897e3b53-5d4d-444b-85cb-2cdc8a97d903",
"snapshot": "chrome",
"instruction": "I have a LibreOffice Writer file form.docx on the desktop. Help me convert it to PDF format and store the PDF in the forms/ folder in my Google Drive.",
"source": "https://marketplace.uipath.com/listings/convert-word-file-to-pdf-and-store-in-onedrive",
"config": [
{
"type": "googledrive",
"parameters": {
"settings_file": "evaluation_examples/settings/googledrive/settings.yml",
"operation": ["delete"],
"args": [
{
"query": "title = 'form.pdf' or title = 'form.docx' or title = 'form.docx.pdf' or title = 'forms'",
"trash": false
}
]
}
},
{
"type": "launch",
"parameters": {
"command": [
"C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe",
"--remote-debugging-port=1337"
]
}
},
{
"type": "launch",
"parameters": {
"command": [
"ncat.exe", "-k", "-l", "0.0.0.0", "9222",
"--sh-exec", "ncat.exe 127.0.0.1 1337"
]
}
},
{
"type": "chrome_open_tabs",
"parameters": {
"urls_to_open": [
"https://www.zhihu.com/",
"https://www.coursera.org/",
"https://www.deepl.com",
"https://www.wikidata.org/wiki/Wikidata:Main_Page"
]
}
},
{
"type": "login",
"parameters": {
"settings_file": "evaluation_examples/settings/google/settings.json",
"platform": "googledrive"
}
},
{
"type": "download",
"parameters": {
"files": [
{
"url": "https://drive.usercontent.google.com/download?id=18TvzE8jnULU2g9XJsT-TaPEKcLGNVfu0&export=download&authuser=0&confirm=t&uuid=d914e031-9aa6-431b-81c0-73fcb87af027&at=APZUnTUx56WM_I3gnhHo-eZX__kx:1706158167271",
"path": "C:\\Users\\chenj\\Desktop\\form.docx"
}
]
}
},
{
"type": "open",
"parameters": {
"path": "C:\\Users\\chenj\\Desktop\\form.docx"
}
}
],
"trajectory": "trajectories/",
"related_apps": [
"libreoffice_writer",
"chrome"
],
"evaluator": {
"func": "compare_pdfs",
"result": {
"type": "googledrive_file",
"settings_file": "evaluation_examples/settings/googledrive/settings.yml",
"query": [
"title = 'forms' and mimeType = 'application/vnd.google-apps.folder' and trashed = false",
"( title = 'form.pdf' or title = 'form.docx.pdf' ) and trashed = false"
],
"dest": "form.pdf"
},
"expected": {
"type": "cloud_file",
"path": "https://drive.usercontent.google.com/download?id=118wb7zmG8yP7DS1cImP9-GcOeKib3fLp&export=download&authuser=0&confirm=t&uuid=b82542fa-7731-4014-8ebc-d940f0fb83fe&at=APZUnTVkmL9rk3EpA0Ak5JLPEnJZ:1706101389421",
"dest": "form_gold.pdf"
}
}
}

241
evaluation_examples/examples/Windows/multi_app/8e116af7-7db7-4e35-a68b-b0939c066c78.json Normal file
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{
"id": "8e116af7-7db7-4e35-a68b-b0939c066c78",
"snapshot": "libreoffice_calc",
"instruction": "Please update my bookkeeping sheet with the recent transactions from the provided folder, detailing my expenses over the past few days.",
"source": "authors",
"config": [
{
"type": "download",
"parameters": {
"files": [
{
"path": "C:\\Users\\chenj\\Desktop\\my_bookkeeping.xlsx",
"url": "https://drive.google.com/uc?id=1QOSpTZPFzFZeC0tng4Gfws544LFln836&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\receipt_0.jpeg",
"url": "https://drive.google.com/uc?id=1b0BRc-BzXObVCUEonJfRbDsrgxZugj3U&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\receipt_1.jpg",
"url": "https://drive.google.com/uc?id=1S-JBDqwEf7Z_JXDItK_F4BOHgScTjlyN&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\receipt_2.jpg",
"url": "https://drive.google.com/uc?id=1Ys2abZi9_0y8sxuj2vCbC0OhjC6YdrC-&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\receipt_3.pdf",
"url": "https://drive.google.com/uc?id=1sKvBbGDpmUkv891xTqX7w5dtEvchQahd&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\receipt_4.jpg",
"url": "https://drive.google.com/uc?id=1kW7xH5bc2jRaKGDKHDrgSehTrPgkyzkc&export=download"
}
]
}
},
{
"type": "open",
"parameters": {
"path": "C:\\Users\\chenj\\Desktop\\my_bookkeeping.xlsx"
}
}
],
"trajectory": "trajectories/8e116af7-7db7-4e35-a68b-b0939c066c78",
"related_apps": [
"libreoffice_calc",
"os",
"image",
"pdf"
],
"evaluator": {
"postconfig": [
{
"type": "activate_window",
"parameters": {
"window_name": "my_bookkeeping - Excel",
"strict": true
}
},
{
"type": "sleep",
"parameters": {
"seconds": 0.5
}
},
{
"type": "execute",
"parameters": {
"command": [
"python",
"-c",
"import pyautogui; import time; pyautogui.hotkey(\"ctrl\", \"s\"); time.sleep(0.5); pyautogui.press(\"enter\");"
]
}
},
{
"type": "sleep",
"parameters": {
"seconds": 1.0
}
}
],
"func": "compare_table",
"result": {
"type": "vm_file",
"path": "C:\\Users\\chenj\\Desktop\\my_bookkeeping.xlsx",
"dest": "my_bookkeeping.xlsx"
},
"expected": {
"type": "cloud_file",
"path": "https://drive.google.com/uc?id=1ygEDdVlkf2ZyqMxJ_ktqo9G_g--rc6co&export=download",
"dest": "my_bookkeeping_gold.xlsx"
},
"options": {
"rules": [
{
"type": "sheet_fuzzy",
"sheet_idx0": "RNSheet1",
"sheet_idx1": "ENSheet1",
"rules": [
{
"range": [
"A1:A8",
"B1:B8",
"C1:C8",
"D1:D8",
"E1:E8"
],
"type": "exact_match"
}
]
},
{
"type": "sheet_fuzzy",
"sheet_idx0": "RNSheet1",
"sheet_idx1": "ENSheet1",
"rules": [
{
"range": [
"C9:C13"
],
"type": "exact_match",
"ignore_case": true
}
]
},
{
"type": "check_cell",
"sheet_idx": 0,
"coordinate": "D9",
"props": {
"value": {
"method": "approx:0.1",
"ref": -186.93
}
}
},
{
"type": "check_cell",
"sheet_idx": 0,
"coordinate": "D10",
"props": {
"value": {
"method": "approx:0.1",
"ref": -3670
}
}
},
{
"type": "check_cell",
"sheet_idx": 0,
"coordinate": "D11",
"props": {
"value": {
"method": "approx:0.1",
"ref": -5.7
}
}
},
{
"type": "check_cell",
"sheet_idx": 0,
"coordinate": "D12",
"props": {
"value": {
"method": "approx:0.1",
"ref": -154.06
}
}
},
{
"type": "check_cell",
"sheet_idx": 0,
"coordinate": "D13",
"props": {
"value": {
"method": "approx:0.1",
"ref": -8.1
}
}
},
{
"type": "check_cell",
"sheet_idx": 0,
"coordinate": "E9",
"props": {
"value": {
"method": "approx:0.1",
"ref": 603.07
}
}
},
{
"type": "check_cell",
"sheet_idx": 0,
"coordinate": "E10",
"props": {
"value": {
"method": "approx:0.1",
"ref": -3066.93
}
}
},
{
"type": "check_cell",
"sheet_idx": 0,
"coordinate": "E11",
"props": {
"value": {
"method": "approx:0.1",
"ref": -3072.63
}
}
},
{
"type": "check_cell",
"sheet_idx": 0,
"coordinate": "E12",
"props": {
"value": {
"method": "approx:0.1",
"ref": -3226.69
}
}
},
{
"type": "check_cell",
"sheet_idx": 0,
"coordinate": "E13",
"props": {
"value": {
"method": "approx:0.1",
"ref": -3234.79
}
}
}
]
}
}
}

91
evaluation_examples/examples/Windows/multi_app/a82b78bb-7fde-4cb3-94a4-035baf10bcf0.json Normal file
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{
"id": "a82b78bb-7fde-4cb3-94a4-035baf10bcf0",
"snapshot": "libreoffice_calc",
"instruction": "I'm really enjoying this paper. Could you please locate the personal webpages of the initial author and the last three authors? Please include them in a browser bookmark folder titled 'Liked Authors.'",
"source": "authors",
"config": [
{
"type": "launch",
"parameters": {
"command": [
"C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe",
"--remote-debugging-port=1337"
]
}
},
{
"type": "launch",
"parameters": {
"command": [
"ncat.exe", "-k", "-l", "0.0.0.0", "9222",
"--sh-exec", "ncat.exe 127.0.0.1 1337"
]
}
},
{
"type": "sleep",
"parameters": {
"seconds": 2
}
},
{
"type": "download",
"parameters": {
"files": [
{
"url": "https://drive.usercontent.google.com/download?id=1IlJ8kU5MlR6OqJHchsSUJzLCmcrG-8N7&export=download&authuser=0&confirm=t&uuid=d2a1810f-edea-4bfd-9d79-e668b9f11876&at=APZUnTVv_eqtC86YzkEU8_jIhC9W:1709522229162",
"path": "C:\\Users\\chenj\\Desktop\\2206.08853.pdf"
}
]
}
},
{
"type": "open",
"parameters": {
"path": "C:\\Users\\chenj\\Desktop\\2206.08853.pdf"
}
},
{
"type": "sleep",
"parameters": {
"seconds": 2
}
},
{
"type": "execute",
"parameters": {
"command": [
"python",
"-c",
"import pyautogui; import time; pyautogui.hotkey('f11'); time.sleep(0.5); pyautogui.click(960, 540); time.sleep(0.5); pyautogui.scroll(-20)"
]
}
}
],
"trajectory": "trajectories/a82b78bb-7fde-4cb3-94a4-035baf10bcf0",
"related_apps": [
"chrome",
"pdf"
],
"evaluator": {
"func": "is_expected_bookmarks",
"result": {
"type": "bookmarks"
},
"expected": {
"type": "rule",
"rules": {
"type": "liked_authors_websites_urls",
"names": [
"Liked Authors"
],
"urls": [
["https://jimfan.me/", "https://research.nvidia.com/person/linxi-jim-fan"],
["https://research.nvidia.com/person/de-an-huang", "https://ai.stanford.edu/~dahuang/"],
["https://yukezhu.me/", "https://www.cs.utexas.edu/people/faculty-researchers/yuke-zhu", "https://experts.utexas.edu/yuke_zhu", "https://research.nvidia.com/person/yuke-zhu"],
["http://tensorlab.cms.caltech.edu/users/anima/", "https://www.eas.caltech.edu/people/anima"]
]
}
}
}
}

94
evaluation_examples/examples/Windows/multi_app/b5062e3e-641c-4e3a-907b-ac864d2e7652.json Normal file
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{
"id": "b5062e3e-641c-4e3a-907b-ac864d2e7652",
"snapshot": "libreoffice_calc",
"instruction": "Please help me to extract the name, e-mail, and affiliation of the first author from each paper in the folder and organize them in an Excel table. Include headers for each field. Sort the authors by their full names alphabetically and save the table as \"Documents\\authors.xlsx\".",
"source": "authors",
"config": [
{
"type": "command",
"parameters": {
"command": "mkdir C:\\Users\\chenj\\Documents\\Papers",
"shell": true
}
},
{
"type": "download",
"parameters": {
"files": [
{
"path": "C:\\Users\\chenj\\Documents\\Papers\\zhang_appagent.pdf",
"url": "https://arxiv.org/pdf/2312.13771.pdf"
},
{
"path": "C:\\Users\\chenj\\Documents\\Papers\\niu_screenagent.pdf",
"url": "https://arxiv.org/pdf/2402.07945.pdf"
},
{
"path": "C:\\Users\\chenj\\Documents\\Papers\\koh_visualwebarena.pdf",
"url": "https://arxiv.org/pdf/2401.13649.pdf"
},
{
"path": "C:\\Users\\chenj\\Documents\\Papers\\deng_mind2web.pdf",
"url": "https://papers.nips.cc/paper_files/paper/2023/file/5950bf290a1570ea401bf98882128160-Paper-Datasets_and_Benchmarks.pdf"
}
]
}
},
{
"type": "launch",
"parameters": {
"command": [
"explorer.exe",
"C:\\Users\\chenj\\Documents\\Papers"
]
}
}
],
"trajectory": "trajectories/b5062e3e-641c-4e3a-907b-ac864d2e7652",
"related_apps": [
"libreoffice_calc",
"os"
],
"evaluator": {
"func": "compare_table",
"result": {
"type": "vm_file",
"path": "C:\\Users\\chenj\\authors.xlsx",
"dest": "authors.xlsx"
},
"expected": {
"type": "cloud_file",
"path": "https://drive.google.com/uc?id=1fttbvfHuoQfsQUk3fVXkJsCu231jhnQj&export=download",
"dest": "authors-gt.xlsx"
},
"options": {
"rules": [
{
"type": "sheet_fuzzy",
"sheet_idx0": "RNSheet1",
"sheet_idx1": "ENSheet1",
"rules": [
{
"range": ["A1:C1"],
"type": "includes",
"ignore_case": true
},
{
"range": ["A2:B5"],
"type": "exact_match",
"trim_leadings": " ",
"trim_trailings": " "
},
{
"range": ["C2:C5"],
"type": "exact_match",
"trim_leadings": " ",
"trim_trailings": " ",
"ignore_case": true
}
]
}
]
}
}
}

106
evaluation_examples/examples/Windows/multi_app/c867c42d-a52d-4a24-8ae3-f75d256b5618.json Normal file
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{
"id": "c867c42d-a52d-4a24-8ae3-f75d256b5618",
"snapshot": "thunderbird",
"instruction": "Please assist me in exporting my contacts of Personal Address Book from Thunderbird into contacts.csv file in the desktop and convert it to .xlsx with Libreoffice Calc.",
"source": "https://www.sync.blue/en/sync/mozilla-thunderbird/google-sheets/",
"config": [
{
"type": "launch",
"parameters": {
"command": [
"C:\\Program Files\\Microsoft Office\\root\\Office16\\EXCEL.EXE"
]
}
},
{
"type": "download",
"parameters": {
"files": [
{
"url": "https://drive.google.com/uc?id=1njAaNiujlh1DZzGK7nL5iZsppsNAMkH7&export=download",
"path": "C:\\Users\\chenj\\thunderbird-profile.7z"
}
]
}
},
{
"type": "execute",
"parameters": {
"command": [
"C:\\Program Files\\7-Zip\\7z.exe",
"x", "C:\\Users\\chenj\\thunderbird-profile.7z"
]
}
},
{
"type": "execute",
"parameters": {
"command": "rd /s /q C:\\Users\\chenj\\AppData\\Roaming\\Thunderbird",
"shell": true
}
},
{
"type": "execute",
"parameters": {
"command": "move C:\\Users\\chenj\\Thunderbird C:\\Users\\chenj\\AppData\\Roaming\\Thunderbird",
"shell": true
}
},
{
"type": "launch",
"parameters": {
"command": [
"C:\\Program Files\\Mozilla Thunderbird\\thunderbird.exe"
]
}
}
],
"trajectory": "trajectories/",
"related_apps": [
"thunderbird",
"libreoffice_calc"
],
"evaluator": {
"func": [
"compare_csv",
"compare_table"
],
"conj": "and",
"result": [
{
"type": "vm_file",
"path": "C:\\Users\\chenj\\Desktop\\contacts.csv",
"dest": "contacts.csv"
},
{
"type": "vm_file",
"path": "C:\\Users\\chenj\\Desktop\\contacts.xlsx",
"dest": "contacts.xlsx"
}
],
"expected": [
{
"type": "cloud_file",
"path": "https://drive.usercontent.google.com/download?id=1StwASpAR2ALq2Y1vugGsdUJptg6FwjEm&export=download&authuser=0&confirm=t&uuid=56339e19-b889-4da1-ab72-5e0b90f13fff&at=APZUnTVWFF2pBrtWU_hXgzfbrWP2:1706719668676",
"dest": "contacts_gold.csv"
},
{
"type": "cloud_file",
"path": "https://drive.usercontent.google.com/download?id=1s25eUpvkMzSm6p_WA7O13t6mVqmkxr2C&export=download&authuser=0&confirm=t&uuid=901cbd32-6026-4391-a5cc-989e1047cf7c&at=APZUnTUs27mZceDshB_f9Tx4PFyz:1706719610831",
"dest": "contacts_gold.xlsx"
}
],
"options": [
{},
{
"rules": [
{
"type": "sheet_data",
"sheet_idx0": "RI0",
"sheet_idx1": "EI0"
}
]
}
]
}
}

128
evaluation_examples/examples/Windows/multi_app/d1acdb87-bb67-4f30-84aa-990e56a09c92.json Normal file
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{
"id": "d1acdb87-bb67-4f30-84aa-990e56a09c92",
"snapshot": "libreoffice_calc",
"instruction": "Hello! I'm eagerly planning a culinary adventure to Hong Kong and have curated a list of must-visit restaurants that I've been longing to explore. However, I could use some assistance in compiling a few essential details about these establishments. Would you be so kind as to help me out? It would be fantastic if you could search for these restaurants on Google Maps. I'm particularly interested in obtaining their addresses, any available websites, and contact phone numbers. If you could gather this information and input it into my form file, I would be immensely grateful. Many thanks in advance!",
"source": "authors",
"config": [
{
"type": "download",
"parameters": {
"files": [
{
"path": "C:\\Users\\chenj\\Desktop\\restaurants.txt",
"url": "https://drive.google.com/uc?id=1IehFLJPZcFv8Ujk31ExbyGLji9AylmmJ&export=download"
},
{
"path": "C:\\Users\\chenj\\Desktop\\MUST_VISIT.xlsx",
"url": "https://drive.google.com/uc?id=1fXmjvZcwkIcckMIAXi3Hv_JAbVWpgs_l&export=download"
}
]
}
},
{
"type": "open",
"parameters": {
"path": "C:\\Users\\chenj\\Desktop\\MUST_VISIT.xlsx"
}
},
{
"type": "open",
"parameters": {
"path": "C:\\Users\\chenj\\Desktop\\restaurants.txt"
}
},
{
"type": "sleep",
"parameters": {
"seconds": 5
}
},
{
"type": "activate_window",
"parameters": {
"window_name": "restaurants.txt"
}
}
],
"trajectory": "trajectories/d1acdb87-bb67-4f30-84aa-990e56a09c92",
"related_apps": [
"os",
"chrome",
"libreoffice_calc"
],
"evaluator": {
"postconfig": [
{
"type": "activate_window",
"parameters": {
"window_name": "MUST_VISIT - Excel",
"strict": true
}
},
{
"type": "sleep",
"parameters": {
"seconds": 0.5
}
},
{
"type": "execute",
"parameters": {
"command": [
"python",
"-c",
"import pyautogui; import time; pyautogui.hotkey(\"ctrl\", \"s\"); time.sleep(0.5); pyautogui.press(\"enter\");"
]
}
},
{
"type": "sleep",
"parameters": {
"seconds": 1.0
}
}
],
"func": "compare_table",
"result": {
"type": "vm_file",
"path": "C:\\Users\\chenj\\Desktop\\MUST_VISIT.xlsx",
"dest": "MUST_VISIT.xlsx"
},
"expected": {
"type": "cloud_file",
"path": "https://drive.google.com/uc?id=1MV6jBvRbbYwPqeFTd_nX40xzyltNhphl&export=download",
"dest": "MUST_VISIT-gt.xlsx"
},
"options": {
"rules": [
{
"type": "sheet_fuzzy",
"sheet_idx0": "RNSheet1",
"sheet_idx1": "ENSheet1",
"rules": [
{
"range": ["A1:A6", "D1:D6"],
"type": "exact_match"
},
{
"range": ["B1:B6"],
"type": "fuzzy_match",
"threshold": 85,
"normalization": [
["Rd", "Road"],
["St", "Street"]
],
"ignore_case": true
},
{
"range": ["C1:C6"],
"type": "includes",
"trim_leadings": "+ ",
"ignore_chars": " ()-"
}
]
}
]
}
}
}

79
evaluation_examples/examples/Windows/multi_app/da52d699-e8d2-4dc5-9191-a2199e0b6a9b.json Normal file
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{
"id": "da52d699-e8d2-4dc5-9191-a2199e0b6a9b",
"snapshot": "libreoffice_calc",
"instruction": "Examine the spreadsheet on the desktop, which contains a record of books read in 2022. Take the website https://howlongtoread.com/ as a reference to identify the book with the slowest reading pace, measured in words per day. I have an empty document named 'book_list_result.docx' on the desktop; please open it and record the title there.",
"source": "GAIA",
"config": [
{
"type": "download",
"parameters": {
"files": [
{
"url": "https://drive.usercontent.google.com/download?id=1JGZNCShtmpu7A8Z8lkjc8hdFEAMXZVvh&export=download&authuser=0&confirm=t&uuid=67063da6-2a72-4ed2-92b2-ade508439ce4&at=APZUnTUgS17YjX-D0oSvALwnPosB:1709368886960",
"path": "C:\\Users\\chenj\\Desktop\\2023_validation_Book_Reading_Rate.xlsx"
},
{
"url": "https://drive.usercontent.google.com/download?id=1iySmK8zvTzgmERH7KQuESP05NBsMunhV&export=download&authuser=0&confirm=t&uuid=130f6cee-0f9a-4f2e-a84d-89a3b302f350&at=APZUnTXugQOTOApe1_zxUbafo2Sp:1709369519349",
"path": "C:\\Users\\chenj\\Desktop\\book_list_result.docx"
}
]
}
},
{
"type": "open",
"parameters": {
"path": "C:\\Users\\chenj\\Desktop\\2023_validation_Book_Reading_Rate.xlsx"
}
}
],
"trajectory": "trajectories/da52d699-e8d2-4dc5-9191-a2199e0b6a9b",
"related_apps": [
"libreoffice_calc",
"chrome",
"libreoffice_writer"
],
"evaluator": {
"func": "compare_docx_files",
"postconfig": [
{
"type": "activate_window",
"parameters": {
"window_name": "book_list_result - Word",
"strict": true
}
},
{
"type": "sleep",
"parameters": {
"seconds": 0.5
}
},
{
"type": "execute",
"parameters": {
"command": [
"python",
"-c",
"import pyautogui; import time; pyautogui.hotkey(\"ctrl\", \"s\"); time.sleep(0.5); pyautogui.press(\"enter\");"
]
}
},
{
"type": "sleep",
"parameters": {
"seconds": 0.5
}
}
],
"expected": {
"type": "cloud_file",
"path": "https://drive.usercontent.google.com/download?id=1rpvOlHZO0AqC85od8pJtx8YcDPljcejN&export=download&authuser=0&confirm=t&uuid=24a3a5e3-a188-4a41-ad01-a4709dc1c0b6&at=APZUnTWx56rr8-iTuXkfV5poOK-I:1709369145446",
"dest": "book_list_result_Gold.docx"
},
"result": {
"type": "vm_file",
"path": "C:\\Users\\chenj\\Desktop\\book_list_result.docx",
"dest": "book_list_result.docx"
}
}
}

101
evaluation_examples/examples/Windows/multi_app/deec51c9-3b1e-4b9e-993c-4776f20e8bb2.json Normal file
View File

@@ -0,0 +1,101 @@
{
"id": "deec51c9-3b1e-4b9e-993c-4776f20e8bb2",
"snapshot": "libreoffice_calc",
"instruction": "Find a paper list of all the new foundation language models issued on 11st Oct. 2023 via arxiv daily, and organize it into the sheet I opened.",
"source": "authors",
"config": [
{
"type": "download",
"parameters": {
"files": [
{
"path": "C:\\Users\\chenj\\Desktop\\New Large Language Models.xlsx",
"url": "https://drive.google.com/uc?id=1NJFAUDzatd5TbBqXeCy3-ok4BWj-xayT&export=download"
}
]
}
},
{
"type": "open",
"parameters": {
"path": "C:\\Users\\chenj\\Desktop\\New Large Language Models.xlsx"
}
}
],
"trajectory": "trajectories/deec51c9-3b1e-4b9e-993c-4776f20e8bb2",
"related_apps": [
"libreoffice_calc",
"chrome",
"os"
],
"evaluator": {
"postconfig": [
{
"type": "activate_window",
"parameters": {
"window_name": "New Large Language Models - Excel",
"strict": true
}
},
{
"type": "sleep",
"parameters": {
"seconds": 0.5
}
},
{
"type": "execute",
"parameters": {
"command": [
"python",
"-c",
"import pyautogui; import time; pyautogui.hotkey(\"ctrl\", \"s\"); time.sleep(0.5); pyautogui.press(\"enter\");"
]
}
},
{
"type": "sleep",
"parameters": {
"seconds": 1.0
}
}
],
"func": "compare_table",
"result": {
"type": "vm_file",
"path": "C:\\Users\\chenj\\Desktop\\New Large Language Models.xlsx",
"dest": "New Large Language Models.xlsx"
},
"expected": {
"type": "cloud_file",
"path": "https://drive.google.com/uc?id=1BHOyjFo72b74YKWTqPMaoNvCzICkos-G&export=download",
"dest": "New Large Language Models Gold.xlsx"
},
"options": {
"rules": [
{
"type": "sheet_fuzzy",
"sheet_idx0": "RNSheet1",
"sheet_idx1": "ENSheet1",
"rules": [
{
"range": [
"B2:B5",
"C2:C5"
],
"type": "exact_match"
},
{
"range": [
"A2:A5"
],
"type": "fuzzy_match",
"threshold": 90,
"ignore_case": true
}
]
}
]
}
}
}

121
evaluation_examples/examples/Windows/multi_app/e2392362-125e-4f76-a2ee-524b183a3412.json Normal file
View File

@@ -0,0 +1,121 @@
{
"id": "e2392362-125e-4f76-a2ee-524b183a3412",
"snapshot": "chrome",
"instruction": "I recently started using the famous personal academic homepage template from academicpages.github.io to build my own personal homepage, and I have cloned it to my local Documents\\Code\\Website folder. According to an online tutorial, I can configure my name and contact information in the _config.yaml file. However, I am not familiar with the YAML file format. Please help me find the sections related to the name and contact information in this file and change them to \"Test Account\" and \"Test@gmail.com\".",
"source": "authors",
"config": [
{
"type": "command",
"parameters": {
"command": "mkdir C:\\Users\\chenj\\Documents\\Code\\Website",
"shell": true
}
},
{
"type": "download",
"parameters": {
"files": [
{
"path": "C:\\Users\\chenj\\.tmp.7z",
"url": "https://drive.google.com/uc?id=1LYc6rBSuCNBtTQIg-m9zP6KmlEB_Zfdo&export=download"
}
]
}
},
{
"type": "execute",
"parameters": {
"command": [
"C:\\Program Files\\7-Zip\\7z.exe",
"x", "-oC:\\Users\\chenj\\Documents\\Code\\Website",
"C:\\Users\\chenj\\.tmp.7z"
]
}
},
{
"type": "launch",
"parameters": {
"command": [
"C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe",
"--remote-debugging-port=1337"
]
}
},
{
"type": "launch",
"parameters": {
"command": [
"ncat.exe", "-k", "-l", "0.0.0.0", "9222",
"--sh-exec", "ncat.exe 127.0.0.1 1337"
]
}
},
{
"type": "chrome_open_tabs",
"parameters": {
"urls_to_open": [
"https://academicpages.github.io/"
]
}
}
],
"trajectory": "trajectories/e2392362-125e-4f76-a2ee-524b183a3412",
"related_apps": [
"chrome",
"os",
"vscode"
],
"evaluator": {
"postconfig": [
{
"type": "execute",
"parameters": {
"command": [
"python",
"-c",
"import pyautogui; import time; pyautogui.hotkey('ctrl', 's'); time.sleep(0.5);"
]
}
}
],
"func": "check_json",
"options": {
"is_yaml": true
},
"expected": {
"type": "rule",
"rules": {
"expect": [
{
"key": [
"name"
],
"method": "eq",
"ref": "Test Account"
},
{
"key": [
"author",
"name"
],
"method": "eq",
"ref": "Test Account"
},
{
"key": [
"author",
"email"
],
"method": "eq",
"ref": "Test@gmail.com"
}
]
}
},
"result": {
"type": "vm_file",
"path": "C:\\Users\\chenj\\Documents\\Code\\Website\\academicpages.github.io\\_config.yml",
"dest": "_config.yaml"
}
}
}

82
evaluation_examples/examples/Windows/multi_app/eb303e01-261e-4972-8c07-c9b4e7a4922a.json Normal file
View File

@@ -0,0 +1,82 @@
{
"id": "eb303e01-261e-4972-8c07-c9b4e7a4922a",
"snapshot": "libreoffice_impress",
"instruction": "Tomorrow, I'm scheduled to deliver a talk, and my PowerPoint slides and speaking notes are saved on the desktop. Help me insert my planned remarks for each slide into the \"note\" section of the PowerPoint as a reminder. I've completed this task for some slides; assist me in completing the remaining part.",
"source": "authors",
"config": [
{
"type": "download",
"parameters": {
"files": [
{
"url": "https://drive.usercontent.google.com/download?id=1MdgN8ECxzLgHgjq8eKqrNQt3MPDjnKwa&export=download&authuser=0&confirm=t&uuid=ed5c37db-c565-4ca1-bbd1-bbdba13e9306&at=APZUnTUNi8YTLlZqMZ0r--bBpBEG:1709449877819",
"path": "C:\\Users\\chenj\\Desktop\\lecture1-2021-with-ink.pptx"
},
{
"url": "https://drive.usercontent.google.com/download?id=1FkPOcsWpsjUXSUld1NblwyVzcsE19uIe&export=download&authuser=0&confirm=t&uuid=27501bc0-732b-4ff7-abf4-a52427aea264&at=APZUnTWleaafIVF2iZkiuHo0vQ66:1709449873140",
"path": "C:\\Users\\chenj\\Desktop\\notes.docx"
}
]
}
},
{
"type": "open",
"parameters": {
"path": "C:\\Users\\chenj\\Desktop\\lecture1-2021-with-ink.pptx"
}
}
],
"trajectory": "trajectories/eb303e01-261e-4972-8c07-c9b4e7a4922a",
"related_apps": [
"libreoffice_impress",
"libreoffice_writer"
],
"evaluator": {
"postconfig": [
{
"type": "activate_window",
"parameters": {
"window_name": "lecture1-2021-with-ink - PowerPoint",
"strict": true
}
},
{
"type": "sleep",
"parameters": {
"seconds": 5
}
},
{
"type": "execute",
"parameters": {
"command": [
"python",
"-c",
"import pyautogui; import time; pyautogui.hotkey(\"ctrl\", \"s\"); time.sleep(0.5); pyautogui.press(\"enter\");"
]
}
},
{
"type": "sleep",
"parameters": {
"seconds": 0.5
}
}
],
"func": "compare_pptx_files",
"expected": {
"type": "cloud_file",
"path": "https://drive.usercontent.google.com/download?id=18orj_0q6N4w7ijADOJeU5ZkDDw-RdFUl&export=download&authuser=0&confirm=t&uuid=c05d2bce-bccb-4504-8fe4-7c409788d727&at=APZUnTVlCicnIm0cMdJ9FrZg4MSN:1709453015475",
"dest": "lecture1-2021-with-ink_Gold.pptx"
},
"result": {
"type": "vm_file",
"path": "C:\\Users\\chenj\\Desktop\\lecture1-2021-with-ink.pptx",
"dest": "lecture1-2021-with-ink.pptx"
},
"options": {
"examine_shape": false,
"examine_bullets": false
}
}
}

53
evaluation_examples/examples/Windows/multi_app/f918266a-b3e0-4914-865d-4faa564f1aef.json Normal file
View File

@@ -0,0 +1,53 @@
{
"id": "f918266a-b3e0-4914-865d-4faa564f1aef",
"snapshot": "vscode",
"instruction": "Please complete the code and retrieve the output from the Python script 'calculator.py' located on the desktop and save it as 'log.txt' in the same directory as the Python file.",
"source": "GAIA",
"config": [
{
"type": "download",
"parameters": {
"files": [
{
"url": "https://drive.usercontent.google.com/download?id=1l09TnSiXo-qOK2UazcIdrT_M6JwTfzq7&export=download&authuser=0&confirm=t&uuid=80bd550f-f3a6-4b69-ae0f-221c12b11fd9&at=APZUnTWgUlKuIDJZmkr0Q9Bze3w_:1709784652645",
"path": "C:\\Users\\chenj\\Desktop\\calculator.zip"
}
]
}
},
{
"type": "execute",
"parameters": {
"command": [
"C:\\Program Files\\7-Zip\\7z.exe",
"C:\\Users\\chenj\\Desktop\\calculator.zip"
]
}
},
{
"type": "execute",
"parameters": {
"command": "del C:\\Users\\chenj\\Desktop\\calculator.zip",
"shell": true
}
}
],
"trajectory": "trajectories/f918266a-b3e0-4914-865d-4faa564f1aef",
"related_apps": [
"vscode",
"os"
],
"evaluator": {
"func": "compare_text_file",
"expected": {
"type": "cloud_file",
"path": "https://drive.usercontent.google.com/download?id=1-14AgA1nHNL22VD_3QtRzWaMjIBa3RvJ&export=download&authuser=0&confirm=t&uuid=6aa05bf1-4964-4f7b-8983-d28540b4053b&at=APZUnTXuJgDHIYA2FZl3A_OQJEOF:1709881263131",
"dest": "log_Gold.txt"
},
"result": {
"type": "vm_file",
"path": "C:\\Users\\chenj\\Desktop\\log.txt",
"dest": "log.txt"
}
}
}

16
evaluation_examples/examples/gimp/d16c99dc-2a1e-46f2-b350-d97c86c85c15.json
View File

@@ -86,13 +86,14 @@
],
"func": [
"check_image_size",
"check_structure_sim"
"check_structure_sim_resized"
],
"expected": [
{
"type": "vm_file",
"path": "/home/user/Desktop/dog_with_background.png",
"dest": "dog_with_background.png"
"type": "rule",
"rules": {
"height": 512
}
},
{
"type": "vm_file",
@@ -102,10 +103,9 @@
],
"result": [
{
"type": "rule",
"rules": {
"height": 512
}
"type": "vm_file",
"path": "/home/user/Desktop/dog_with_background.png",
"dest": "dog_with_background.png"
},
{
"type": "vm_file",

6
evaluation_examples/examples/libreoffice_impress/ac9bb6cb-1888-43ab-81e4-a98a547918cd.json
View File

@@ -63,6 +63,12 @@
"type": "vm_file",
"path": "/home/user/Desktop/saa-format-guide.pptx",
"dest": "saa-format-guide.pptx"
},
"expected": {
"type": "rule",
"rules": {
"color": "red"
}
}
}
}

4
evaluation_examples/examples/multi_apps/4c26e3f3-3a14-4d86-b44a-d3cedebbb487.json
View File

@@ -30,12 +30,12 @@
],
"evaluator": {
"func": "check_brightness_decrease_and_structure_sim",
"expected": {
"result": {
"type": "vm_file",
"path": "/home/user/Desktop/background.png",
"dest": "background.png"
},
"result": {
"expected": {
"type": "cloud_file",
"path": "https://drive.usercontent.google.com/download?id=13if1UwZ5ay6ADAVW2jp3rcyvAEBse6MJ&export=download&authuser=0&confirm=t&uuid=2ea03068-1874-4240-baa1-f8bb2f917a99&at=APZUnTXq6dVlASg819jCaI1A-rm2:1710136385956",
"dest": "image_original.png"

4
evaluation_examples/examples/multi_apps/51f5801c-18b3-4f25-b0c3-02f85507a078.json
View File

@@ -9,7 +9,7 @@
"parameters": {
"files": [
{
"url": "https://drive.usercontent.google.com/download?id=1e12nL_V7bffaLSocQ86EiGCdygzggWeu&export=download",
"url": "https://drive.usercontent.google.com/download?id=1epTcblcYh8j_wFtA-aiXPIF2Oo1IVw8A&export=download",
"path": "/home/user/Desktop/Dickinson_Slides.pptx"
}
]
@@ -36,7 +36,7 @@
},
"expected": {
"type": "cloud_file",
"path": "https://drive.usercontent.google.com/download?id=1Xl6tgQ0K5qA1BDA2fKTK2xFLzXwbtkZ6&export=download",
"path": "https://drive.usercontent.google.com/download?id=1vUvaQLJUtFgbZi7lSzl0y0TS_WecFczm&export=download",
"dest": "notes_gold.docx"
},
"options": {

122
evaluation_examples/examples/multi_apps/6f4073b8-d8ea-4ade-8a18-c5d1d5d5aa9a.json
View File

@@ -1,50 +1,50 @@
{
"id": "6f4073b8-d8ea-4ade-8a18-c5d1d5d5aa9a",
"snapshot": "multiapps",
"instruction": "I now want to count the meeting cities of the three machine learning conferences in the past ten years from 2013 to 2019(including 2013 and 2019). I have listed the names and years of the conferences in excel. Please fill in the vacant locations.",
"source": "author",
"config": [
{
"type": "download",
"parameters": {
"files": [
{
"url": "https://drive.google.com/uc?export=download&id=19wUxTQeoKr6ihJWJ_9cu2tzKQH0cnxWH",
"path": "/home/user/Desktop/ConferenceCity.xlsx"
}
]
}
},
{
"type": "open",
"parameters": {
"path": "/home/user/Desktop/ConferenceCity.xlsx"
}
}
],
"trajectory": "trajectories/",
"related_apps": [
"calc", "chrome", "os"
],
"evaluator": {
"postconfig":[
{
"type": "download",
"parameters": {
"files": [
{
"url": "https://drive.google.com/uc?export=download&id=1ZcITkIOs2Z86S5L6MShSohFs3_xVfeCP",
"path": "/home/user/Desktop/ConferenceCity_Gold.xlsx"
}
]
}
},
{
"type": "activate_window",
"parameters": {
"window_name": "ConferenceCity.xlsx - LibreOffice Calc"
}
},
"id": "6f4073b8-d8ea-4ade-8a18-c5d1d5d5aa9a",
"snapshot": "multiapps",
"instruction": "I now want to count the meeting cities of the three machine learning conferences in the past ten years from 2013 to 2019(including 2013 and 2019). I have listed the names and years of the conferences in excel. Please fill in the vacant locations.",
"source": "author",
"config": [
{
"type": "download",
"parameters": {
"files": [
{
"url": "https://drive.google.com/uc?export=download&id=19wUxTQeoKr6ihJWJ_9cu2tzKQH0cnxWH",
"path": "/home/user/Desktop/ConferenceCity.xlsx"
}
]
}
},
{
"type": "open",
"parameters": {
"path": "/home/user/Desktop/ConferenceCity.xlsx"
}
}
],
"trajectory": "trajectories/",
"related_apps": [
"calc", "chrome", "os"
],
"evaluator": {
"postconfig":[
{
"type": "download",
"parameters": {
"files": [
{
"url": "https://drive.google.com/uc?export=download&id=1ZcITkIOs2Z86S5L6MShSohFs3_xVfeCP",
"path": "/home/user/Desktop/ConferenceCity_Gold.xlsx"
}
]
}
},
{
"type": "activate_window",
"parameters": {
"window_name": "ConferenceCity.xlsx - LibreOffice Calc"
}
},
{
"type": "execute",
"parameters": {
@@ -55,18 +55,18 @@
]
}
}
],
"func": "compare_conference_city_in_order",
"expected": {
"type": "rule",
"rules":{
"expected": ["Scottsdale","Atlanta","Lake Tahoe","Banff","Beijing",["Montreal", "Montréal"],"San Diego","Lille",["Montreal", "Montréal"],"San Juan",["New York", "New York City", "NYC"],"Barcelona","Toulon","Sydney","Long Beach","Vancouver","Stockholm",["Montreal", "Montréal"],"New Orleans","Long Beach","Vancouver"]
}
},
"result": {
"type": "vm_file",
"path": "/home/user/Desktop/ConferenceCity.xlsx",
"dest": "ConferenceCity.xlsx"
}
}
}
],
"func": "compare_conference_city_in_order",
"expected": {
"type": "rule",
"rules":{
"expected": ["Scottsdale","Atlanta","Lake Tahoe","Banff","Beijing",["Montreal", "Montréal"],"San Diego","Lille",["Montreal", "Montréal"],"San Juan",["New York", "New York City", "NYC"],"Barcelona","Toulon","Sydney","Long Beach","Vancouver","Stockholm",["Montreal", "Montréal"],"New Orleans","Long Beach","Vancouver"]
}
},
"result": {
"type": "vm_file",
"path": "/home/user/Desktop/ConferenceCity.xlsx",
"dest": "ConferenceCity.xlsx"
}
}
}

4
evaluation_examples/examples/multi_apps/b5062e3e-641c-4e3a-907b-ac864d2e7652.json
View File

@@ -38,7 +38,7 @@
}
},
{
"type": "execute",
"type": "launch",
"parameters": {
"command": [
"nautilus",
@@ -109,4 +109,4 @@
]
}
}
}
}

19
evaluation_examples/examples/multi_apps/demo.py
View File

@@ -1,19 +0,0 @@
import pandas as pd
file_path = "/Users/lxc/Downloads/Speedtest.csv"
# 找到csv第二行的第二个数据格里的值
# with open(file_path, "r") as f:
# for i, line in enumerate(f):
# if i == 1:
# data = line.split(",")[1]
# break
# print(data)
with open(file_path, "r") as f:
reader = pd.read_csv(f, sep=',', header=None)
# for column in reader.columns:
# if column.startswith("TEST_DATE"):
# data_col = column
# break
for data in reader['TEST_DATE']:
print(data)

4
evaluation_examples/examples/multi_apps/e2392362-125e-4f76-a2ee-524b183a3412.json
View File

@@ -31,7 +31,7 @@
"command": [
"tar",
"-xJvf",
".tmp.tar.xz",
"/home/user/.tmp.tar.xz",
"-C",
"/home/user/Code/Website/"
]
@@ -124,4 +124,4 @@
"dest": "_config.yaml"
}
}
}
}

2
evaluation_examples/examples/vs_code/70745df8-f2f5-42bd-8074-fbc10334fcc5.json
View File

@@ -62,4 +62,4 @@
"dest": "settings.json"
}
}
}
}

2
evaluation_examples/examples/vs_code/c6bf789c-ba3a-4209-971d-b63abf0ab733.json
View File

@@ -63,4 +63,4 @@
"dest": "settings.json"
}
}
}
}

4
evaluation_examples/settings/google/settings.json
View File

@@ -1,4 +1,4 @@
{
"email": "xlang2024anonym@gmail.com",
"password": "q]wN~0iD>H:6"
}
"password": "Evt5LLj!VJ6Y!C$B"
}

1
evaluation_examples/settings/googledrive/credentials.json
View File

@@ -1 +0,0 @@
{"access_token": "ya29.a0Ad52N382_JIl2nZBNpJCgoU3HXk2Kz7CArVYn_PGI8pXFucAozry1Vmp5QolzGrnl4UChZswJDOgcdPm5Ew-NbdHPX95wxknoG1oJKqjWYtjl3mw433hiGtriuKWKnXcz1NUf8ewqqq458tJLLDhbbZFW7eZRQrdJzmrGAaCgYKAZ4SARISFQHGX2Mik2MQ5qx0goIypVyzbcUmYw0173", "client_id": "786888752612-rgng5v9hcq4as7pn0b40gt9r5lekmht9.apps.googleusercontent.com", "client_secret": "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", "refresh_token": "1//0eVpYfdSAjvbCCgYIARAAGA4SNwF-L9IrAgL6KVceiEVTjtQdmPki2I3m8ejP3lzTLL2Wa3-rdrYfU7eYeKDVCS5KRxa_xCE_pPY", "token_expiry": "2024-03-13T10:09:01Z", "token_uri": "https://oauth2.googleapis.com/token", "user_agent": null, "revoke_uri": "https://oauth2.googleapis.com/revoke", "id_token": null, "id_token_jwt": null, "token_response": {"access_token": "ya29.a0Ad52N382_JIl2nZBNpJCgoU3HXk2Kz7CArVYn_PGI8pXFucAozry1Vmp5QolzGrnl4UChZswJDOgcdPm5Ew-NbdHPX95wxknoG1oJKqjWYtjl3mw433hiGtriuKWKnXcz1NUf8ewqqq458tJLLDhbbZFW7eZRQrdJzmrGAaCgYKAZ4SARISFQHGX2Mik2MQ5qx0goIypVyzbcUmYw0173", "expires_in": 3599, "scope": "https://www.googleapis.com/auth/drive", "token_type": "Bearer"}, "scopes": ["https://www.googleapis.com/auth/drive"], "token_info_uri": "https://oauth2.googleapis.com/tokeninfo", "invalid": false, "_class": "OAuth2Credentials", "_module": "oauth2client.client"}

1
evaluation_examples/test_all.json
View File

@@ -286,7 +286,6 @@
"788b3701-3ec9-4b67-b679-418bfa726c22",
"48c46dc7-fe04-4505-ade7-723cba1aa6f6",
"42d25c08-fb87-4927-8b65-93631280a26f",
"bb7db4c2-30b5-4be7-8dd7-b8c4ec7d3108",
"e8172110-ec08-421b-a6f5-842e6451911f",
"42f4d1c7-4521-4161-b646-0a8934e36081",
"3c8f201a-009d-4bbe-8b65-a6f8b35bb57f",

1
evaluation_examples/test_small.json
View File

@@ -70,7 +70,6 @@
"c2751594-0cd5-4088-be1b-b5f2f9ec97c4",
"48c46dc7-fe04-4505-ade7-723cba1aa6f6",
"42d25c08-fb87-4927-8b65-93631280a26f",
"bb7db4c2-30b5-4be7-8dd7-b8c4ec7d3108",
"3c8f201a-009d-4bbe-8b65-a6f8b35bb57f",
"d68204bf-11c1-4b13-b48b-d303c73d4bf6",
"91190194-f406-4cd6-b3f9-c43fac942b22",

19
lib_run_single.py
View File

@@ -2,6 +2,7 @@ import datetime
import json
import logging
import os
# import wandb
from wrapt_timeout_decorator import *
@@ -13,7 +14,6 @@ with open("./settings.json", "r") as file:
data = json.load(file)
time_limit = data["time_limit"]
@timeout(time_limit, use_signals=False)
def run_single_example(agent, env, example, max_steps, instruction, args, example_result_dir, scores):
agent.reset()
@@ -21,9 +21,9 @@ def run_single_example(agent, env, example, max_steps, instruction, args, exampl
done = False
step_idx = 0
env.controller.start_recording()
# str_table = wandb.Table(columns=["Screenshot", "A11T", "Modle Response", "Action", "Action timestamp", "Done"])
while not done and step_idx < max_steps:
actions = agent.predict(
response, actions = agent.predict(
instruction,
obs
)
@@ -31,20 +31,22 @@ def run_single_example(agent, env, example, max_steps, instruction, args, exampl
# Capture the timestamp before executing the action
action_timestamp = datetime.datetime.now().strftime("%Y%m%d@%H%M%S")
logger.info("Step %d: %s", step_idx + 1, action)
obs, reward, done, info = env.step(action, args.sleep_after_execution)
logger.info("Reward: %.2f", reward)
logger.info("Done: %s", done)
logger.info("Info: %s", info)
# Save screenshot and trajectory information
with open(os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"),
"wb") as _f:
with open(obs['screenshot'], "rb") as __f:
screenshot = __f.read()
_f.write(screenshot)
# get a11tree and save to wandb
# thisrun_a11tree = env.controller.get_accessibility_tree()
# str_table.add_data(wandb.Image(data_or_path=os.path.join(example_result_dir, f"step_{step_idx + 1}_{action_timestamp}.png"), caption=f"step_{step_idx + 1}_{action_timestamp}"),
# thisrun_a11tree,
# response, action, action_timestamp, done)
# run.log({"Reward": reward})
with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
f.write(json.dumps({
"step_num": step_idx + 1,
@@ -56,14 +58,15 @@ def run_single_example(agent, env, example, max_steps, instruction, args, exampl
"screenshot_file": f"step_{step_idx + 1}_{action_timestamp}.png"
}))
f.write("\n")
if done:
logger.info("The episode is done.")
break
step_idx += 1
# run.log({"str_trajectory": str_table})
result = env.evaluate()
logger.info("Result: %.2f", result)
scores.append(result)
with open(os.path.join(example_result_dir, "result.txt"), "w", encoding="utf-8") as f:
f.write(f"{result}\n")
env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
# run.log({"Result": result})

32
main.py
View File

@@ -70,38 +70,6 @@ def human_agent():
done = False
logger.info('\x1b[32m[TASK INSTRUCTION]: \x1b[32;3m%s\x1b[0m', example["instruction"])
trajectory = [
{
"action_type": "MOVE_TO", #
"parameters": {
"x": 754,
"y": 1057
}
},
{"action_type": "CLICK", "parameters": {"button": "right", "num_clicks": 1}}
]
for i in range(len(trajectory)):
# action = get_human_action()
# action = {
# "action_type": 0,
# "click_type": 3,
# }
logger.info(trajectory[i])
observation, reward, done, info = env.step(trajectory[i])
observation.pop("accessibility_tree")
logger.info("Observation: %s", observation)
logger.info("Reward: %.2f", reward)
logger.info("Info: %s", info)
logger.info("================================\n")
if done:
logger.info("The episode is done.")
break
input("Press Enter to start human operation...")
human_start_time = time.time()
input("Press Enter to finish human operation.")

39
mm_agents/accessibility_tree_wrap/heuristic_retrieve.py
View File

@@ -2,7 +2,7 @@ import xml.etree.ElementTree as ET
from PIL import Image, ImageDraw, ImageFont
from typing import Tuple
from typing import Tuple, List
def find_leaf_nodes(xlm_file_str):
if not xlm_file_str:
@@ -40,7 +40,7 @@ def judge_node(node: ET, platform="ubuntu", check_image=False) -> bool:
or node.tag.endswith("textfield")\
or node.tag.endswith("textarea")\
or node.tag.endswith("menu")\
or node.tag in [ "alert", "canvas", "check-box"
or node.tag in { "alert", "canvas", "check-box"
, "combo-box", "entry", "icon"
, "image", "paragraph", "scroll-bar"
, "section", "slider", "static"
@@ -48,7 +48,7 @@ def judge_node(node: ET, platform="ubuntu", check_image=False) -> bool:
, "netuiribbontab", "start", "trayclockwclass"
, "traydummysearchcontrol", "uiimage", "uiproperty"
, "uiribboncommandbar"
]
}
keeps = keeps and ( platform=="ubuntu"\
and node.get("{{{:}}}showing".format(state_ns), "false")=="true"\
and node.get("{{{:}}}visible".format(state_ns), "false")=="true"\
@@ -66,7 +66,7 @@ def judge_node(node: ET, platform="ubuntu", check_image=False) -> bool:
coordinates: Tuple[int, int] = eval(node.get("{{{:}}}screencoord".format(component_ns), "(-1, -1)"))
sizes: Tuple[int, int] = eval(node.get("{{{:}}}size".format(component_ns), "(-1, -1)"))
keeps = keeps and coordinates[0]>0 and coordinates[1]>0 and sizes[0]>0 and sizes[1]>0
keeps = keeps and coordinates[0]>=0 and coordinates[1]>=0 and sizes[0]>0 and sizes[1]>0
return keeps
def filter_nodes(root: ET, platform="ubuntu", check_image=False):
@@ -86,6 +86,7 @@ def draw_bounding_boxes(nodes, image_file_path, output_image_file_path):
draw = ImageDraw.Draw(image)
marks = []
drew_nodes = []
text_informations: List[str] = ["index\ttag\tname\ttext"]
try:
# Adjust the path to the font file you have or use a default one
@@ -135,18 +136,38 @@ def draw_bounding_boxes(nodes, image_file_path, output_image_file_path):
#draw.rectangle([text_position, (text_position[0] + 25, text_position[1] + 18)], fill='black')
draw.rectangle(text_bbox, fill='black')
draw.text(text_position, str(index), font=font, anchor="lb", fill="white")
index += 1
# each mark is an x, y, w, h tuple
marks.append([coords[0], coords[1], size[0], size[1]])
drew_nodes.append(_node)
if _node.text:
node_text = ( _node.text if '"' not in _node.text\
else '"{:}"'.format(_node.text.replace('"', '""'))
)
elif _node.get("{uri:deskat:uia.windows.microsoft.org}class", "").endswith("EditWrapper") \
and _node.get("{uri:deskat:value.at-spi.gnome.org}value"):
node_text: str = _node.get("{uri:deskat:value.at-spi.gnome.org}value")
node_text = (node_text if '"' not in node_text\
else '"{:}"'.format(node_text.replace('"', '""'))
)
else:
node_text = '""'
text_information: str = "{:d}\t{:}\t{:}\t{:}"\
.format( index, _node.tag
, _node.get("name", "")
, node_text
)
text_informations.append(text_information)
index += 1
except ValueError:
pass
# Save the result
image.save(output_image_file_path)
return marks, drew_nodes
return marks, drew_nodes, "\n".join(text_informations)
def print_nodes_with_indent(nodes, indent=0):
@@ -157,12 +178,12 @@ def print_nodes_with_indent(nodes, indent=0):
if __name__ == '__main__':
import json
with open('selection_sorted(imaged).xml', 'r', encoding='utf-8') as f:
with open('3.xml', 'r', encoding='utf-8') as f:
xml_file_str = f.read()
filtered_nodes = filter_nodes(ET.fromstring(xml_file_str))
print(len(filtered_nodes))
masks = draw_bounding_boxes( filtered_nodes, 'selection_sorted(imaged).png'
, 'selection_sorted(imaged).ai.png'
masks = draw_bounding_boxes( filtered_nodes, '3.a.png'
, '3.png'
)
# print(masks)

290
mm_agents/agent.py
View File

@@ -5,19 +5,22 @@ import os
import re
import time
import uuid
import openai
import xml.etree.ElementTree as ET
import numpy as np
from http import HTTPStatus
from io import BytesIO
from typing import Dict, List
from google.api_core.exceptions import InvalidArgument
from typing import Dict, List, Tuple, Union
import backoff
import dashscope
import google.generativeai as genai
import openai
import requests
import cv2
from PIL import Image
from google.api_core.exceptions import InvalidArgument
from mm_agents.accessibility_tree_wrap.heuristic_retrieve import find_leaf_nodes, filter_nodes, draw_bounding_boxes
from mm_agents.accessibility_tree_wrap.heuristic_retrieve import filter_nodes, draw_bounding_boxes
from mm_agents.prompts import SYS_PROMPT_IN_SCREENSHOT_OUT_CODE, SYS_PROMPT_IN_SCREENSHOT_OUT_ACTION, \
SYS_PROMPT_IN_A11Y_OUT_CODE, SYS_PROMPT_IN_A11Y_OUT_ACTION, \
SYS_PROMPT_IN_BOTH_OUT_CODE, SYS_PROMPT_IN_BOTH_OUT_ACTION, \
@@ -25,6 +28,14 @@ from mm_agents.prompts import SYS_PROMPT_IN_SCREENSHOT_OUT_CODE, SYS_PROMPT_IN_S
logger = logging.getLogger("desktopenv.agent")
def downsample_image(img: Union[str, np.ndarray], ratio: Tuple[float, float]):
fx, fy = ratio
if isinstance(img, str):
img = cv2.imread(img)
resized = cv2.resize(img, None, fx=fx, fy=fy, interpolation=cv2.INTER_AREA)
return resized
# Function to encode the image
def encode_image(image_path):
@@ -36,27 +47,36 @@ def linearize_accessibility_tree(accessibility_tree):
# leaf_nodes = find_leaf_nodes(accessibility_tree)
filtered_nodes = filter_nodes(ET.fromstring(accessibility_tree))
linearized_accessibility_tree = "tag\tname\ttext\tposition (top-left x&y)\tsize (w&h)\n"
linearized_accessibility_tree = ["tag\tname\ttext\tposition (top-left x&y)\tsize (w&h)"]
# Linearize the accessibility tree nodes into a table format
for node in filtered_nodes:
linearized_accessibility_tree += node.tag + "\t"
linearized_accessibility_tree += node.attrib.get('name') + "\t"
#linearized_accessibility_tree += node.tag + "\t"
#linearized_accessibility_tree += node.attrib.get('name') + "\t"
if node.text:
linearized_accessibility_tree += (node.text if '"' not in node.text else '"{:}"'.format(
node.text.replace('"', '""'))) + "\t"
text = ( node.text if '"' not in node.text\
else '"{:}"'.format(node.text.replace('"', '""'))
)
elif node.get("{uri:deskat:uia.windows.microsoft.org}class", "").endswith("EditWrapper") \
and node.get("{uri:deskat:value.at-spi.gnome.org}value"):
text: str = node.get("{uri:deskat:value.at-spi.gnome.org}value")
linearized_accessibility_tree += (text if '"' not in text else '"{:}"'.format(
text.replace('"', '""'))) + "\t"
text = (text if '"' not in text\
else '"{:}"'.format(text.replace('"', '""'))
)
else:
linearized_accessibility_tree += '""\t'
linearized_accessibility_tree += node.attrib.get(
'{uri:deskat:component.at-spi.gnome.org}screencoord', "") + "\t"
linearized_accessibility_tree += node.attrib.get('{uri:deskat:component.at-spi.gnome.org}size', "") + "\n"
text = '""'
#linearized_accessibility_tree += node.attrib.get(
#, "") + "\t"
#linearized_accessibility_tree += node.attrib.get('{uri:deskat:component.at-spi.gnome.org}size', "") + "\n"
linearized_accessibility_tree.append(
"{:}\t{:}\t{:}\t{:}\t{:}".format(
node.tag, node.get("name", ""), text
, node.get('{uri:deskat:component.at-spi.gnome.org}screencoord', "")
, node.get('{uri:deskat:component.at-spi.gnome.org}size', "")
)
)
return linearized_accessibility_tree
return "\n".join(linearized_accessibility_tree)
def tag_screenshot(screenshot, accessibility_tree):
@@ -67,9 +87,9 @@ def tag_screenshot(screenshot, accessibility_tree):
# nodes = filter_nodes(find_leaf_nodes(accessibility_tree))
nodes = filter_nodes(ET.fromstring(accessibility_tree), check_image=True)
# Make tag screenshot
marks, drew_nodes = draw_bounding_boxes(nodes, screenshot, tagged_screenshot_file_path)
marks, drew_nodes, element_list = draw_bounding_boxes(nodes, screenshot, tagged_screenshot_file_path)
return marks, drew_nodes, tagged_screenshot_file_path
return marks, drew_nodes, tagged_screenshot_file_path, element_list
def parse_actions_from_string(input_string):
@@ -255,7 +275,6 @@ class PromptAgent:
if self.observation_type == "screenshot_a11y_tree":
_screenshot = previous_obs["screenshot"]
_linearized_accessibility_tree = previous_obs["accessibility_tree"]
logger.debug("LINEAR AT: %s", _linearized_accessibility_tree)
messages.append({
"role": "user",
@@ -341,7 +360,8 @@ class PromptAgent:
# {{{1
if self.observation_type in ["screenshot", "screenshot_a11y_tree"]:
base64_image = encode_image(obs["screenshot"])
linearized_accessibility_tree = linearize_accessibility_tree(accessibility_tree=obs["accessibility_tree"])
linearized_accessibility_tree = linearize_accessibility_tree(accessibility_tree=obs["accessibility_tree"]) if self.observation_type == "screenshot_a11y_tree" else None
logger.debug("LINEAR AT: %s", linearized_accessibility_tree)
if self.observation_type == "screenshot_a11y_tree":
self.observations.append({
@@ -375,6 +395,7 @@ class PromptAgent:
})
elif self.observation_type == "a11y_tree":
linearized_accessibility_tree = linearize_accessibility_tree(accessibility_tree=obs["accessibility_tree"])
logger.debug("LINEAR AT: %s", linearized_accessibility_tree)
self.observations.append({
"screenshot": None,
@@ -393,11 +414,13 @@ class PromptAgent:
})
elif self.observation_type == "som":
# Add som to the screenshot
masks, drew_nodes, tagged_screenshot = tag_screenshot(obs["screenshot"], obs["accessibility_tree"])
masks, drew_nodes, tagged_screenshot, linearized_accessibility_tree = tag_screenshot(obs["screenshot"], obs["accessibility_tree"])
base64_image = encode_image(tagged_screenshot)
logger.debug("LINEAR AT: %s", linearized_accessibility_tree)
self.observations.append({
"screenshot": base64_image
"screenshot": base64_image,
"accessibility_tree": linearized_accessibility_tree
})
messages.append({
@@ -405,7 +428,8 @@ class PromptAgent:
"content": [
{
"type": "text",
"text": "Given the tagged screenshot as below. What's the next step that you will do to help with the task?"
"text": "Given the tagged screenshot and info from accessibility tree as below:\n{}\nWhat's the next step that you will do to help with the task?".format(
linearized_accessibility_tree)
},
{
"type": "image_url",
@@ -422,7 +446,8 @@ class PromptAgent:
# with open("messages.json", "w") as f:
# f.write(json.dumps(messages, indent=4))
logger.info("Generating content with GPT model: %s", self.model)
#logger.info("PROMPT: %s", messages)
response = self.call_llm({
"model": self.model,
"messages": messages,
@@ -441,7 +466,7 @@ class PromptAgent:
actions = None
self.thoughts.append("")
return actions
return response, actions
@backoff.on_exception(
backoff.expo,
@@ -461,7 +486,7 @@ class PromptAgent:
"Content-Type": "application/json",
"Authorization": f"Bearer {os.environ['OPENAI_API_KEY']}"
}
# logger.info("Generating content with GPT model: %s", self.model)
logger.info("Generating content with GPT model: %s", self.model)
response = requests.post(
"https://api.openai.com/v1/chat/completions",
headers=headers,
@@ -495,7 +520,7 @@ class PromptAgent:
temperature = payload["temperature"]
claude_messages = []
for i, message in enumerate(messages):
claude_message = {
"role": message["role"],
@@ -503,17 +528,17 @@ class PromptAgent:
}
assert len(message["content"]) in [1, 2], "One text, or one text with one image"
for part in message["content"]:
if part['type'] == "image_url":
image_source = {}
image_source["type"] = "base64"
image_source["media_type"] = "image/png"
image_source["data"] = part['image_url']['url'].replace("data:image/png;base64,", "")
claude_message['content'].append({"type": "image", "source": image_source})
if part['type'] == "text":
claude_message['content'].append({"type": "text", "text": part['text']})
claude_messages.append(claude_message)
# the claude not support system message in our endpoint, so we concatenate it at the first user message
@@ -522,83 +547,155 @@ class PromptAgent:
claude_messages[1]['content'].insert(0, claude_system_message_item)
claude_messages.pop(0)
logger.debug("CLAUDE MESSAGE: %s", repr(claude_messages))
# headers = {
# "x-api-key": os.environ["ANTHROPIC_API_KEY"],
# "anthropic-version": "2023-06-01",
# "content-type": "application/json"
# }
# headers = {
# "Accept": "application / json",
# "Authorization": "Bearer " + os.environ["ANTHROPIC_API_KEY"],
# "User-Agent": "Apifox/1.0.0 (https://apifox.com)",
# "Content-Type": "application/json"
# }
headers = {
"x-api-key": os.environ["ANTHROPIC_API_KEY"],
"anthropic-version": "2023-06-01",
"content-type": "application/json"
"Authorization": os.environ["ANTHROPIC_API_KEY"],
"Content-Type": "application/json"
}
payload = {
"model": self.model,
"max_tokens": max_tokens,
"messages": claude_messages
"messages": claude_messages,
"temperature": temperature,
"top_p": top_p
}
response = requests.post(
"https://api.anthropic.com/v1/messages",
headers=headers,
json=payload
)
if response.status_code != 200:
logger.error("Failed to call LLM: " + response.text)
time.sleep(5)
return ""
max_attempts = 20
attempt = 0
while attempt < max_attempts:
# response = requests.post("https://api.aigcbest.top/v1/chat/completions", headers=headers, json=payload)
response = requests.post("https://token.cluade-chat.top/v1/chat/completions", headers=headers, json=payload)
if response.status_code == 200:
result = response.json()['choices'][0]['message']['content']
break
else:
logger.error(f"Failed to call LLM: {response.text}")
time.sleep(10)
attempt += 1
else:
return response.json()['content'][0]['text']
print("Exceeded maximum attempts to call LLM.")
result = ""
return result
# elif self.model.startswith("mistral"):
# print("Call mistral")
# messages = payload["messages"]
# max_tokens = payload["max_tokens"]
#
# misrtal_messages = []
#
# for i, message in enumerate(messages):
# mistral_message = {
# "role": message["role"],
# "content": []
# }
#
# for part in message["content"]:
# mistral_message['content'] = part['text'] if part['type'] == "text" else None
#
# misrtal_messages.append(mistral_message)
#
# # the mistral not support system message in our endpoint, so we concatenate it at the first user message
# if misrtal_messages[0]['role'] == "system":
# misrtal_messages[1]['content'] = misrtal_messages[0]['content'] + "\n" + misrtal_messages[1]['content']
# misrtal_messages.pop(0)
#
# # openai.api_base = "http://localhost:8000/v1"
# # openai.api_key = "test"
# # response = openai.ChatCompletion.create(
# # messages=misrtal_messages,
# # model="Mixtral-8x7B-Instruct-v0.1"
# # )
#
# from openai import OpenAI
# TOGETHER_API_KEY = "d011650e7537797148fb6170ec1e0be7ae75160375686fae02277136078e90d2"
#
# client = OpenAI(api_key=TOGETHER_API_KEY,
# base_url='https://api.together.xyz',
# )
# logger.info("Generating content with Mistral model: %s", self.model)
# response = client.chat.completions.create(
# messages=misrtal_messages,
# model="mistralai/Mixtral-8x7B-Instruct-v0.1",
# max_tokens=1024
# )
#
# try:
# # return response['choices'][0]['message']['content']
# return response.choices[0].message.content
# except Exception as e:
# print("Failed to call LLM: " + str(e))
# return ""
elif self.model.startswith("mistral"):
print("Call mistral")
messages = payload["messages"]
max_tokens = payload["max_tokens"]
top_p = payload["top_p"]
temperature = payload["temperature"]
mistral_messages = []
for i, message in enumerate(messages):
mistral_message = {
"role": message["role"],
"content": ""
}
for part in message["content"]:
mistral_message['content'] = part['text'] if part['type'] == "text" else ""
mistral_messages.append(mistral_message)
from openai import OpenAI
client = OpenAI(api_key=os.environ["TOGETHER_API_KEY"],
base_url='https://api.together.xyz',
)
logger.info("Generating content with Mistral model: %s", self.model)
flag = 0
while True:
try:
if flag > 20: break
response = client.chat.completions.create(
messages=mistral_messages,
model=self.model,
max_tokens=max_tokens
)
break
except:
if flag == 0:
mistral_messages = [mistral_messages[0]] + mistral_messages[-1:]
else:
mistral_messages[-1]["content"] = ' '.join(mistral_messages[-1]["content"].split()[:-500])
flag = flag + 1
try:
return response.choices[0].message.content
except Exception as e:
print("Failed to call LLM: " + str(e))
return ""
elif self.model.startswith("THUDM"):
# THUDM/cogagent-chat-hf
print("Call CogAgent")
messages = payload["messages"]
max_tokens = payload["max_tokens"]
top_p = payload["top_p"]
temperature = payload["temperature"]
cog_messages = []
for i, message in enumerate(messages):
cog_message = {
"role": message["role"],
"content": []
}
for part in message["content"]:
if part['type'] == "image_url":
cog_message['content'].append(
{"type": "image_url", "image_url": {"url": part['image_url']['url']}})
if part['type'] == "text":
cog_message['content'].append({"type": "text", "text": part['text']})
cog_messages.append(cog_message)
# the cogagent not support system message in our endpoint, so we concatenate it at the first user message
if cog_messages[0]['role'] == "system":
cog_system_message_item = cog_messages[0]['content'][0]
cog_messages[1]['content'].insert(0, cog_system_message_item)
cog_messages.pop(0)
payload = {
"model": self.model,
"max_tokens": max_tokens,
"messages": cog_messages
}
base_url = "http://127.0.0.1:8000"
response = requests.post(f"{base_url}/v1/chat/completions", json=payload, stream=False)
if response.status_code == 200:
decoded_line = response.json()
content = decoded_line.get("choices", [{}])[0].get("message", "").get("content", "")
return content
else:
print("Failed to call LLM: ", response.status_code)
return ""
elif self.model.startswith("gemini"):
def encoded_img_to_pil_img(data_str):
@@ -674,6 +771,7 @@ class PromptAgent:
try:
return response.text
except Exception as e:
logger.error("Meet exception when calling Gemini API, " + str(e))
return ""
elif self.model.startswith("qwen"):
messages = payload["messages"]
@@ -706,7 +804,7 @@ class PromptAgent:
if response.status_code == HTTPStatus.OK:
try:
return response.json()['output']['choices'][0]['message']['content']
except Exception as e:
except Exception:
return ""
else:
print(response.code) # The error code.

3
mm_agents/download_ckpt.sh
View File

@@ -1,3 +0,0 @@
wget https://github.com/UX-Decoder/Semantic-SAM/releases/download/checkpoint/swinl_only_sam_many2many.pth
wget https://huggingface.co/xdecoder/SEEM/resolve/main/seem_focall_v1.pt
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth

405
mm_agents/llm_server/CogAgent/CogAgent.py Normal file
View File

@@ -0,0 +1,405 @@
import os
import gc
import time
import base64
from contextlib import asynccontextmanager
from typing import List, Literal, Union, Tuple, Optional
import torch
import uvicorn
from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from loguru import logger
from pydantic import BaseModel, Field
from sse_starlette.sse import EventSourceResponse
from transformers import AutoModelForCausalLM, LlamaTokenizer, PreTrainedModel, PreTrainedTokenizer, \
TextIteratorStreamer
from PIL import Image
from io import BytesIO
MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/cogvlm-chat-hf')
TOKENIZER_PATH = os.environ.get("TOKENIZER_PATH", 'lmsys/vicuna-7b-v1.5')
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
if os.environ.get('QUANT_ENABLED'):
QUANT_ENABLED = True
else:
with torch.cuda.device(DEVICE):
__, total_bytes = torch.cuda.mem_get_info()
total_gb = total_bytes / (1 << 30)
if total_gb < 40:
QUANT_ENABLED = True
else:
QUANT_ENABLED = False
@asynccontextmanager
async def lifespan(app: FastAPI):
"""
An asynchronous context manager for managing the lifecycle of the FastAPI app.
It ensures that GPU memory is cleared after the app's lifecycle ends, which is essential for efficient resource management in GPU environments.
"""
yield
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
app = FastAPI(lifespan=lifespan)
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
class ModelCard(BaseModel):
"""
A Pydantic model representing a model card, which provides metadata about a machine learning model.
It includes fields like model ID, owner, and creation time.
"""
id: str
object: str = "model"
created: int = Field(default_factory=lambda: int(time.time()))
owned_by: str = "owner"
root: Optional[str] = None
parent: Optional[str] = None
permission: Optional[list] = None
class ModelList(BaseModel):
object: str = "list"
data: List[ModelCard] = []
class ImageUrl(BaseModel):
url: str
class TextContent(BaseModel):
type: Literal["text"]
text: str
class ImageUrlContent(BaseModel):
type: Literal["image_url"]
image_url: ImageUrl
ContentItem = Union[TextContent, ImageUrlContent]
class ChatMessageInput(BaseModel):
role: Literal["user", "assistant", "system"]
content: Union[str, List[ContentItem]]
name: Optional[str] = None
class ChatMessageResponse(BaseModel):
role: Literal["assistant"]
content: str = None
name: Optional[str] = None
class DeltaMessage(BaseModel):
role: Optional[Literal["user", "assistant", "system"]] = None
content: Optional[str] = None
class ChatCompletionRequest(BaseModel):
model: str
messages: List[ChatMessageInput]
temperature: Optional[float] = 0.8
top_p: Optional[float] = 0.8
max_tokens: Optional[int] = None
stream: Optional[bool] = False
# Additional parameters
repetition_penalty: Optional[float] = 1.0
class ChatCompletionResponseChoice(BaseModel):
index: int
message: ChatMessageResponse
class ChatCompletionResponseStreamChoice(BaseModel):
index: int
delta: DeltaMessage
class UsageInfo(BaseModel):
prompt_tokens: int = 0
total_tokens: int = 0
completion_tokens: Optional[int] = 0
class ChatCompletionResponse(BaseModel):
model: str
object: Literal["chat.completion", "chat.completion.chunk"]
choices: List[Union[ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice]]
created: Optional[int] = Field(default_factory=lambda: int(time.time()))
usage: Optional[UsageInfo] = None
@app.get("/v1/models", response_model=ModelList)
async def list_models():
"""
An endpoint to list available models. It returns a list of model cards.
This is useful for clients to query and understand what models are available for use.
"""
model_card = ModelCard(id="cogvlm-chat-17b") # can be replaced by your model id like cogagent-chat-18b
return ModelList(data=[model_card])
@app.post("/v1/chat/completions", response_model=ChatCompletionResponse)
async def create_chat_completion(request: ChatCompletionRequest):
global model, tokenizer
if len(request.messages) < 1 or request.messages[-1].role == "assistant":
raise HTTPException(status_code=400, detail="Invalid request")
gen_params = dict(
messages=request.messages,
temperature=request.temperature,
top_p=request.top_p,
max_tokens=request.max_tokens or 1024,
echo=False,
stream=request.stream,
)
if request.stream:
generate = predict(request.model, gen_params)
return EventSourceResponse(generate, media_type="text/event-stream")
response = generate_cogvlm(model, tokenizer, gen_params)
usage = UsageInfo()
message = ChatMessageResponse(
role="assistant",
content=response["text"],
)
logger.debug(f"==== message ====\n{message}")
choice_data = ChatCompletionResponseChoice(
index=0,
message=message,
)
task_usage = UsageInfo.model_validate(response["usage"])
for usage_key, usage_value in task_usage.model_dump().items():
setattr(usage, usage_key, getattr(usage, usage_key) + usage_value)
return ChatCompletionResponse(model=request.model, choices=[choice_data], object="chat.completion", usage=usage)
async def predict(model_id: str, params: dict):
"""
Handle streaming predictions. It continuously generates responses for a given input stream.
This is particularly useful for real-time, continuous interactions with the model.
"""
global model, tokenizer
choice_data = ChatCompletionResponseStreamChoice(
index=0,
delta=DeltaMessage(role="assistant"),
finish_reason=None
)
chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
yield "{}".format(chunk.model_dump_json(exclude_unset=True))
previous_text = ""
for new_response in generate_stream_cogvlm(model, tokenizer, params):
decoded_unicode = new_response["text"]
delta_text = decoded_unicode[len(previous_text):]
previous_text = decoded_unicode
delta = DeltaMessage(
content=delta_text,
role="assistant",
)
choice_data = ChatCompletionResponseStreamChoice(
index=0,
delta=delta,
)
chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
yield "{}".format(chunk.model_dump_json(exclude_unset=True))
choice_data = ChatCompletionResponseStreamChoice(
index=0,
delta=DeltaMessage(),
)
chunk = ChatCompletionResponse(model=model_id, choices=[choice_data], object="chat.completion.chunk")
yield "{}".format(chunk.model_dump_json(exclude_unset=True))
def generate_cogvlm(model: PreTrainedModel, tokenizer: PreTrainedTokenizer, params: dict):
"""
Generates a response using the CogVLM model. It processes the chat history and image data, if any,
and then invokes the model to generate a response.
"""
for response in generate_stream_cogvlm(model, tokenizer, params):
pass
return response
def process_history_and_images(messages: List[ChatMessageInput]) -> Tuple[
Optional[str], Optional[List[Tuple[str, str]]], Optional[List[Image.Image]]]:
"""
Process history messages to extract text, identify the last user query,
and convert base64 encoded image URLs to PIL images.
Args:
messages(List[ChatMessageInput]): List of ChatMessageInput objects.
return: A tuple of three elements:
- The last user query as a string.
- Text history formatted as a list of tuples for the model.
- List of PIL Image objects extracted from the messages.
"""
formatted_history = []
image_list = []
last_user_query = ''
for i, message in enumerate(messages):
role = message.role
content = message.content
if isinstance(content, list): # text
text_content = ' '.join(item.text for item in content if isinstance(item, TextContent))
else:
text_content = content
if isinstance(content, list): # image
for item in content:
if isinstance(item, ImageUrlContent):
image_url = item.image_url.url
if image_url.startswith("data:image/jpeg;base64,"):
base64_encoded_image = image_url.split("data:image/jpeg;base64,")[1]
image_data = base64.b64decode(base64_encoded_image)
image = Image.open(BytesIO(image_data)).convert('RGB')
image_list.append(image)
elif image_url.startswith("data:image/png;base64,"):
base64_encoded_image = image_url.split("data:image/png;base64,")[1]
image_data = base64.b64decode(base64_encoded_image)
image = Image.open(BytesIO(image_data)).convert('RGB')
image_list.append(image)
if role == 'user':
if i == len(messages) - 1: # 最后一条用户消息
last_user_query = text_content
else:
formatted_history.append((text_content, ''))
elif role == 'assistant':
if formatted_history:
if formatted_history[-1][1] != '':
assert False, f"the last query is answered. answer again. {formatted_history[-1][0]}, {formatted_history[-1][1]}, {text_content}"
formatted_history[-1] = (formatted_history[-1][0], text_content)
else:
assert False, f"assistant reply before user"
else:
assert False, f"unrecognized role: {role}"
return last_user_query, formatted_history, image_list
@torch.inference_mode()
def generate_stream_cogvlm(model: PreTrainedModel, tokenizer: PreTrainedTokenizer, params: dict):
"""
Generates a stream of responses using the CogVLM model in inference mode.
It's optimized to handle continuous input-output interactions with the model in a streaming manner.
"""
messages = params["messages"]
temperature = float(params.get("temperature", 1.0))
repetition_penalty = float(params.get("repetition_penalty", 1.0))
top_p = float(params.get("top_p", 1.0))
max_new_tokens = int(params.get("max_tokens", 256))
query, history, image_list = process_history_and_images(messages)
logger.debug(f"==== request ====\n{query}")
input_by_model = model.build_conversation_input_ids(tokenizer, query=query, history=history,
images=[image_list[-1]])
inputs = {
'input_ids': input_by_model['input_ids'].unsqueeze(0).to(DEVICE),
'token_type_ids': input_by_model['token_type_ids'].unsqueeze(0).to(DEVICE),
'attention_mask': input_by_model['attention_mask'].unsqueeze(0).to(DEVICE),
'images': [[input_by_model['images'][0].to(DEVICE).to(torch_type)]],
}
if 'cross_images' in input_by_model and input_by_model['cross_images']:
inputs['cross_images'] = [[input_by_model['cross_images'][0].to(DEVICE).to(torch_type)]]
input_echo_len = len(inputs["input_ids"][0])
streamer = TextIteratorStreamer(
tokenizer=tokenizer,
timeout=60.0,
skip_prompt=True,
skip_special_tokens=True
)
gen_kwargs = {
"repetition_penalty": repetition_penalty,
"max_new_tokens": max_new_tokens,
"do_sample": True if temperature > 1e-5 else False,
"top_p": top_p if temperature > 1e-5 else 0,
'streamer': streamer,
}
if temperature > 1e-5:
gen_kwargs["temperature"] = temperature
total_len = 0
generated_text = ""
with torch.no_grad():
model.generate(**inputs, **gen_kwargs)
for next_text in streamer:
generated_text += next_text
yield {
"text": generated_text,
"usage": {
"prompt_tokens": input_echo_len,
"completion_tokens": total_len - input_echo_len,
"total_tokens": total_len,
},
}
ret = {
"text": generated_text,
"usage": {
"prompt_tokens": input_echo_len,
"completion_tokens": total_len - input_echo_len,
"total_tokens": total_len,
},
}
yield ret
gc.collect()
torch.cuda.empty_cache()
if __name__ == "__main__":
tokenizer = LlamaTokenizer.from_pretrained(
TOKENIZER_PATH,
trust_remote_code=True)
if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8:
torch_type = torch.bfloat16
else:
torch_type = torch.float16
print("========Use torch type as:{} with device:{}========\n\n".format(torch_type, DEVICE))
if 'cuda' in DEVICE:
if QUANT_ENABLED:
model = AutoModelForCausalLM.from_pretrained(
MODEL_PATH,
load_in_4bit=True,
trust_remote_code=True,
torch_dtype=torch_type,
low_cpu_mem_usage=True
).eval()
else:
model = AutoModelForCausalLM.from_pretrained(
MODEL_PATH,
load_in_4bit=False,
trust_remote_code=True,
torch_dtype=torch_type,
low_cpu_mem_usage=True
).to(DEVICE).eval()
else:
model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, trust_remote_code=True).float().to(DEVICE).eval()
uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)

7
mm_agents/llm_server/CogAgent/README.md Normal file
View File

@@ -0,0 +1,7 @@
## Deploy CogAgent as server
```
python CogAgent.py
```
The CogAgent LLM will be deployed on http://127.0.0.1:8000

2
mm_agents/prompts.py
View File

@@ -801,7 +801,7 @@ You CAN predict multiple actions at one step, but you should only return one act
SYS_PROMPT_IN_SOM_OUT_TAG = """
You are an agent which follow my instruction and perform desktop computer tasks as instructed.
You have good knowledge of computer and good internet connection and assume your code will run on a computer for controlling the mouse and keyboard.
For each step, you will get an observation of the desktop by a screenshot with interact-able elements marked with numerical tags. And you will predict the action of the computer based on the image.
For each step, you will get an observation of the desktop by 1) a screenshot with interact-able elements marked with numerical tags; and 2) accessibility tree, which is based on AT-SPI library. And you will predict the action of the computer based on the image and text information.
You are required to use `pyautogui` to perform the action grounded to the observation, but DONOT use the `pyautogui.locateCenterOnScreen` function to locate the element you want to operate with since we have no image of the element you want to operate with. DONOT USE `pyautogui.screenshot()` to make screenshot.
You can replace x, y in the code with the tag of the element you want to operate with. such as:

3
requirements.txt
View File

@@ -48,4 +48,5 @@ easyocr
borb
pypdf2
pdfplumber
wandb
wrapt_timeout_decorator

79
run.py
View File

@@ -6,7 +6,9 @@ import datetime
import json
import logging
import os
import random
import sys
# import wandb
from tqdm import tqdm
@@ -48,6 +50,11 @@ logger.addHandler(sdebug_handler)
logger = logging.getLogger("desktopenv.experiment")
# wandb config
### set your wandb api key here
# os.environ["WANDB_API_KEY"] = "48ec18fb4da7087238c6d6833eab9907565adbf3"
# wandb.login(key=os.environ.get("WANDB_API_KEY", None))
def config() -> argparse.Namespace:
parser = argparse.ArgumentParser(
@@ -69,7 +76,7 @@ def config() -> argparse.Namespace:
"screenshot_a11y_tree",
"som"
],
default="som",
default="a11y_tree",
help="Observation type",
)
parser.add_argument("--screen_width", type=int, default=1920)
@@ -82,12 +89,16 @@ def config() -> argparse.Namespace:
parser.add_argument("--test_config_base_dir", type=str, default="evaluation_examples")
# lm config
parser.add_argument("--model", type=str, default="gpt-4-vision-preview")
parser.add_argument("--model", type=str, default="gpt-4-0125-preview")
parser.add_argument("--temperature", type=float, default=1.0)
parser.add_argument("--top_p", type=float, default=0.9)
parser.add_argument("--max_tokens", type=int, default=1500)
parser.add_argument("--stop_token", type=str, default=None)
# example config
parser.add_argument("--domain", type=str, default="all")
parser.add_argument("--test_all_meta_path", type=str, default="evaluation_examples/test_all.json")
# logging related
parser.add_argument("--result_dir", type=str, default="./results")
args = parser.parse_args()
@@ -104,6 +115,25 @@ def test(
# log args
logger.info("Args: %s", args)
# set wandb project
cfg_args = \
{
"path_to_vm": args.path_to_vm,
"headless": args.headless,
"action_space": args.action_space,
"observation_type": args.observation_type,
"screen_width": args.screen_width,
"screen_height": args.screen_height,
"sleep_after_execution": args.sleep_after_execution,
"max_steps": args.max_steps,
"max_trajectory_length": args.max_trajectory_length,
"model": args.model,
"temperature": args.temperature,
"top_p": args.top_p,
"max_tokens": args.max_tokens,
"stop_token": args.stop_token,
"result_dir": args.result_dir
}
agent = PromptAgent(
model=args.model,
@@ -118,10 +148,13 @@ def test(
action_space=agent.action_space,
screen_size=(args.screen_width, args.screen_height),
headless=args.headless,
require_a11y_tree=args.observation_type in ["a11y_tree", "screenshot_a11y_tree", "som"],
)
for domain in tqdm(test_all_meta, desc="Domain"):
for example_id in tqdm(test_all_meta[domain], desc="Example", leave=False):
# run = wandb.init(project=f"OSworld-{args.action_space}-{args.observation_type}-{args.model}", group=f"{domain}",
# name=f"{example_id}")
# example setting
config_file = os.path.join(args.test_config_base_dir, f"examples/{domain}/{example_id}.json")
with open(config_file, "r", encoding="utf-8") as f:
@@ -133,6 +166,10 @@ def test(
instruction = example["instruction"]
logger.info(f"[Instruction]: {instruction}")
# wandb each example config settings
cfg_args["instruction"] = instruction
cfg_args["start_time"] = datetime.datetime.now().strftime("%Y:%m:%d-%H:%M:%S")
# run.config.update(cfg_args)
example_result_dir = os.path.join(
args.result_dir,
@@ -148,13 +185,20 @@ def test(
lib_run_single.run_single_example(agent, env, example, max_steps, instruction, args, example_result_dir,
scores)
except Exception as e:
logger.error(f"Exception in {domain}/{example_id}: {e}")
# wandb.log({"Exception": wandb.Table(data=[[f"Exception in {domain}/{example_id}: {e}"]], columns=["Error"])})
env.controller.end_recording(os.path.join(example_result_dir, "recording.mp4"))
logger.error(f"Time limit exceeded in {domain}/{example_id}")
with open(os.path.join(example_result_dir, "traj.jsonl"), "a") as f:
f.write(json.dumps({
"Error": f"Time limit exceeded in {domain}/{example_id}"
}))
f.write("\n")
# wandb settings
# os.mkdir(os.path.join(wandb.run.dir, "results/"))
# for file in os.listdir(example_result_dir):
# # move file to just under the root dir
# os.rename(os.path.join(example_result_dir, file), os.path.join(wandb.run.dir, f"./results/{file}"))
# wandb.finish()
env.close()
logger.info(f"Average score: {sum(scores) / len(scores)}")
@@ -193,15 +237,13 @@ def get_unfinished(action_space, use_model, observation_type, result_dir, total_
def get_result(action_space, use_model, observation_type, result_dir, total_file_json):
target_dir = os.path.join(result_dir, action_space, observation_type, use_model)
if not os.path.exists(target_dir):
print("New experiment, no result yet.")
return None
all_result = []
if not os.path.exists(target_dir):
return total_file_json
finished = {}
for domain in os.listdir(target_dir):
finished[domain] = []
domain_path = os.path.join(target_dir, domain)
if os.path.isdir(domain_path):
for example_id in os.listdir(domain_path):
@@ -209,10 +251,17 @@ def get_result(action_space, use_model, observation_type, result_dir, total_file
if os.path.isdir(example_path):
if "result.txt" in os.listdir(example_path):
# empty all files under example_id
all_result.append(float(open(os.path.join(example_path, "result.txt"), "r").read()))
try:
all_result.append(float(open(os.path.join(example_path, "result.txt"), "r").read()))
except:
all_result.append(0.0)
print("Success Rate:", sum(all_result) / len(all_result) * 100, "%")
return all_result
if not all_result:
print("New experiment, no result yet.")
return None
else:
print("Current Success Rate:", sum(all_result) / len(all_result) * 100, "%")
return all_result
if __name__ == '__main__':
@@ -220,9 +269,12 @@ if __name__ == '__main__':
os.environ["TOKENIZERS_PARALLELISM"] = "false"
args = config()
with open("evaluation_examples/test_all.json", "r", encoding="utf-8") as f:
with open(args.test_all_meta_path, "r", encoding="utf-8") as f:
test_all_meta = json.load(f)
if args.domain != "all":
test_all_meta = {args.domain: test_all_meta[args.domain]}
test_file_list = get_unfinished(
args.action_space,
args.model,
@@ -241,5 +293,4 @@ if __name__ == '__main__':
args.result_dir,
test_all_meta
)
# test(args, test_all_meta)
test(args, test_file_list)

2
settings.json
View File

@@ -1,3 +1,3 @@
{
"time_limit": "1200"
"time_limit": "1800"
}