849 lines
37 KiB
Python
849 lines
37 KiB
Python
import ast
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import base64
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import logging
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import math
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import re
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import xml.etree.ElementTree as ET
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from io import BytesIO
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from typing import Dict, List
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import os
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import backoff
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import numpy as np
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from PIL import Image
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from requests.exceptions import SSLError
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import openai
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from openai import OpenAI
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from google.api_core.exceptions import (
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BadRequest,
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InternalServerError,
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InvalidArgument,
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ResourceExhausted,
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)
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import uuid
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import json
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import oss2
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from mm_agents.accessibility_tree_wrap.heuristic_retrieve import (
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filter_nodes,
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)
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def encode_image(image_content):
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return base64.b64encode(image_content).decode("utf-8")
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def decode_image(base64_str, output_path):
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image_data = base64.b64decode(base64_str)
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with open(output_path, 'wb') as file:
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file.write(image_data)
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return output_path
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def push_oss(image_path):
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access_key_id = os.environ['access_key_id']
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access_key_secret = os.environ['access_key_secret']
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endpoint = os.environ['endpoint']
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bucket_name = os.environ['bucket_name']
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auth = oss2.Auth(access_key_id, access_key_secret)
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bucket = oss2.Bucket(auth, endpoint, bucket_name)
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image = Image.open(image_path)
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if image.mode != 'RGB':
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image = image.convert('RGB')
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output_stream = BytesIO()
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image.save(output_stream, format='JPEG')
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unique_string = image_path.split("/")[-1]
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part_img_ossfile_path = f"images/{unique_string}"
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bucket.put_object(part_img_ossfile_path, output_stream.getvalue())
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def get_image_url(image):
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base64_image = image
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image_name = str(uuid.uuid4())
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os.makedirs("images", exist_ok=True)
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image_path = decode_image(base64_image, f"images/{image_name}.png")
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push_oss(image_path)
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url_prefix = os.environ['url_prefix']
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image_url = url_prefix + image_path.split('/')[-1]
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return image_url
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OWL_PROMPT = '\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>\n{"type": "function", "function": {"name": "computer_use", "description": "Use a mouse and keyboard to interact with a computer, and take screenshots.\\n* This is an interface to a desktop GUI. You do not have access to a terminal or applications menu. You must click on desktop icons to start applications.\\n* Some applications may take time to start or process actions, so you may need to wait and take successive screenshots to see the results of your actions. E.g. if you click on Firefox and a window doesn\'t open, try wait and taking another screenshot.\\n* The screen\'s resolution is 1932x1092.\\n* Whenever you intend to move the cursor to click on an element like an icon, you should consult a screenshot to determine the coordinates of the element before moving the cursor.\\n* If you tried clicking on a program or link but it failed to load, even after waiting, try adjusting your cursor position so that the tip of the cursor visually falls on the element that you want to click.\\n* Make sure to click any buttons, links, icons, etc with the cursor tip in the center of the element. Don\'t click boxes on their edges unless asked.", "parameters": {"properties": {"action": {"description": "The action to perform. The available actions are:\\n* `key`: Performs key down presses on the arguments passed in order, then performs key releases in reverse order.\\n* `type`: Input a string of text. Use the `clear` parameter to decide whether to overwrite the existing text, and use the `enter` parameter to decide whether the enter key should be pressed after typing the text.\\n* `mouse_move`: Move the cursor to a specified (x, y) pixel coordinate on the screen.\\n* `click`: Click the left mouse button at a specified (x, y) pixel coordinate on the screen.\\n* `drag`: Click at a specified (x, y) pixel coordinate on the screen, and drag the cursor to another specified (x2, y2) pixel coordinate on the screen.\\n* `right_click`: Click the right mouse button at a specified (x, y) pixel coordinate on the screen.\\n* `middle_click`: Click the middle mouse button at a specified (x, y) pixel coordinate on the screen.\\n* `double_click`: Double-click the left mouse button at a specified (x, y) pixel coordinate on the screen.\\n* `scroll`: Performs a scroll of the mouse scroll wheel.\\n* `wait`: Wait specified seconds for the change to happen.\\n* `terminate`: Terminate the current task and report its completion status.", "enum": ["key", "type", "mouse_move", "click", "drag", "right_click", "middle_click", "double_click", "scroll", "wait", "terminate"], "type": "string"}, "keys": {"description": "Required only by `action=key`.", "type": "array"}, "text": {"description": "Required only by `action=type`.", "type": "string"}, "clear": {"description": "Assign it to 1 if the text should overwrite the existing text, otherwise assign it to 0. Using this argument clears all text in an element. Required only by `action=type`.", "type": "number"}, "enter": {"description": "Assign it to 1 if the enter key should be pressed after typing the text, otherwise assign it to 0. Required only by `action=type`.", "type": "number"}, "coordinate": {"description": "(x, y): The x (pixels from the left edge) and y (pixels from the top edge) coordinates to move the mouse to.", "type": "array"}, "coordinate2": {"description": "(x2, y2): The x2 (pixels from the left edge) and y2 (pixels from the top edge) coordinates to drag the cursor to. Required only by `action=drag`.", "type": "array"}, "pixels": {"description": "The amount of scrolling to perform. Positive values scroll up, negative values scroll down. This value should be between -5 and 5. Required only by `action=scroll`.", "type": "number"}, "time": {"description": "The seconds to wait. Required only by `action=wait`.", "type": "number"}, "status": {"description": "The status of the task. Required only by `action=terminate`.", "type": "string", "enum": ["success", "failure"]}}, "required": ["action"], "type": "object"}}}\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{"name": <function-name>, "arguments": <args-json-object>}\n</tool_call>'
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def convert_point_format(x, y):
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x_ = x * 1920 / 1932
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y_ = y * 1080 / 1092
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return x_, y_
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def get_prompt(width, height, instruction, history, engine="dash"):
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system_message = OWL_PROMPT
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user_prompt = '''Please generate the next move according to the UI screenshot, instruction and previous actions.
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Instruction: {instruction}
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Previous actions:
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{history}
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Before answering, explain your reasoning step-by-step in <thinking></thinking> tags, and insert them before the <tool_call></tool_call> XML tags.\nAfter answering, summarize your action in <conclusion></conclusion> tags, and insert them after the <tool_call></tool_call> XML tags.'''
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messages=[
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{
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"role": "system",
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"content": [
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{"type": "text", "text": "You are a helpful assistant."} if engine=='openai' else {"text": "You are a helpful assistant."},
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{"type": "text", "text": system_message} if engine=='openai' else {"text": system_message}
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],
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},
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{
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"role": "user",
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"content": [
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{"type": "text", "text": user_prompt.format(instruction=instruction, history=history)} if engine=='openai' else {"text": user_prompt.format(instruction=instruction, history=history)}
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]
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}
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]
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return messages
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logger = logging.getLogger("desktopenv.agent")
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FINISH_WORD = "finished"
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WAIT_WORD = "wait"
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ENV_FAIL_WORD = "error_env"
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CALL_USER = "call_user"
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pure_text_settings = ["a11y_tree"]
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attributes_ns_ubuntu = "https://accessibility.windows.example.org/ns/attributes"
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attributes_ns_windows = "https://accessibility.windows.example.org/ns/attributes"
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state_ns_ubuntu = "https://accessibility.ubuntu.example.org/ns/state"
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state_ns_windows = "https://accessibility.windows.example.org/ns/state"
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component_ns_ubuntu = "https://accessibility.ubuntu.example.org/ns/component"
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component_ns_windows = "https://accessibility.windows.example.org/ns/component"
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value_ns_ubuntu = "https://accessibility.ubuntu.example.org/ns/value"
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value_ns_windows = "https://accessibility.windows.example.org/ns/value"
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class_ns_windows = "https://accessibility.windows.example.org/ns/class"
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# More namespaces defined in OSWorld, please check desktop_env/server/main.py
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# 定义一个函数来解析每个 action
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def parse_action(action_str):
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try:
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# 解析字符串为 AST 节点
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node = ast.parse(action_str, mode='eval')
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# 确保节点是一个表达式
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if not isinstance(node, ast.Expression):
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raise ValueError("Not an expression")
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# 获取表达式的主体
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call = node.body
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# 确保主体是一个函数调用
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if not isinstance(call, ast.Call):
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raise ValueError("Not a function call")
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# 获取函数名
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if isinstance(call.func, ast.Name):
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func_name = call.func.id
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elif isinstance(call.func, ast.Attribute):
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func_name = call.func.attr
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else:
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func_name = None
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# 获取关键字参数
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kwargs = {}
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for kw in call.keywords:
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key = kw.arg
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# 处理不同类型的值，这里假设都是常量
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if isinstance(kw.value, ast.Constant):
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value = kw.value.value
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elif isinstance(kw.value, ast.Str): # 兼容旧版本 Python
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value = kw.value.s
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else:
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value = None
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kwargs[key] = value
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return {
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'function': func_name,
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'args': kwargs
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}
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except Exception as e:
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print(f"Failed to parse action '{action_str}': {e}")
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return None
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def escape_single_quotes(text):
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# 匹配未转义的单引号（不匹配 \\'）
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pattern = r"(?<!\\)'"
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return re.sub(pattern, r"\\'", text)
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def parse_action_fncall(text, image_path, height, width):
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thought = ""
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if "<thinking>" in text and "</thinking>" in text:
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thought = text.split("<thinking>")[-1].split("</thinking>")[0]
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elif "<thinking>" in text:
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thought = text.split("<thinking>")[1]
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conclusion = ""
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if "<conclusion>" in text and "</conclusion>" in text:
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conclusion = text.split("<conclusion>")[-1].split("</conclusion>")[0]
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elif "<conclusion>" in text:
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conclusion = text.split("<conclusion>")[1]
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if conclusion == "" and thought != "":
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conclusion = thought
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if "<tool_call>" in text and "</tool_call>" in text:
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action = text.split("<tool_call>")[-1].split("</tool_call>")[0]
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else:
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action = '{"name": "computer_use"' + text.split('{"name": "computer_use"')[1].split("}}")[0] + '}}'
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action_json = json.loads(action.strip('\n'))['arguments']
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if action_json['action'] == "key":
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action_type = 'hotkey'
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keys = action_json['keys']
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keys_str = ""
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for key in keys:
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keys_str += " " + key
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action_inputs = {"hotkey": keys_str}
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elif action_json['action'] == "type":
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action_type = "type"
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if 'clear' not in action_json:
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action_json['clear'] = 0
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if 'enter' not in action_json:
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action_json['enter'] = 0
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action_inputs = {'content': action_json['text'], 'clear': int(action_json['clear']), 'enter': int(action_json['enter'])}
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elif action_json['action'] == "mouse_move":
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action_type = "hover"
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x, y = convert_point_format(action_json['coordinate'][0], action_json['coordinate'][1])
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action_inputs = {'start_box': [x, y]}
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elif action_json['action'] == "left_click_drag" or action_json['action'] == "drag":
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action_type = "drag"
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x, y = convert_point_format(action_json['coordinate'][0], action_json['coordinate'][1])
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x2, y2 = convert_point_format(action_json['coordinate2'][0], action_json['coordinate2'][1])
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action_inputs = {'start_box': [x, y], 'end_box': [x2, y2]}
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elif action_json['action'] == "left_click" or action_json['action'] == "click":
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action_type = "click"
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x, y = convert_point_format(action_json['coordinate'][0], action_json['coordinate'][1])
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action_inputs = {'start_box': [x, y]}
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elif action_json['action'] == "right_click":
|
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action_type = "right_single"
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x, y = convert_point_format(action_json['coordinate'][0], action_json['coordinate'][1])
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action_inputs = {'start_box': [x, y]}
|
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elif action_json['action'] == "double_click":
|
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action_type = "left_double"
|
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x, y = convert_point_format(action_json['coordinate'][0], action_json['coordinate'][1])
|
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action_inputs = {'start_box': [x, y]}
|
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elif action_json['action'] == "scroll":
|
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action_type = "scroll"
|
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action_inputs = {'pixels': action_json['pixels']}
|
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elif action_json['action'] == "terminate":
|
||
if action_json['status'] == 'success':
|
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action_type = "finished"
|
||
else:
|
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action_type = "fail"
|
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action_inputs = {}
|
||
elif action_json['action'] == "wait":
|
||
action_type = "wait"
|
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action_inputs = {'time': action_json['time'] if 'time' in action_json else 1}
|
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|
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actions = []
|
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actions.append({
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"thought": thought,
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"conclusion": conclusion,
|
||
"action_type": action_type,
|
||
"action_inputs": action_inputs,
|
||
"text": text
|
||
})
|
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return actions
|
||
|
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def parsing_response_to_pyautogui_code(responses, image_height: int, image_width:int, input_swap:bool=False) -> str:
|
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'''
|
||
将M模型的输出解析为OSWorld中的action，生成pyautogui代码字符串
|
||
参数:
|
||
response: 包含模型输出的字典，结构类似于：
|
||
{
|
||
"action_type": "hotkey",
|
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"action_inputs": {
|
||
"hotkey": "v ctrl",
|
||
"start_box": None,
|
||
"end_box": None
|
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}
|
||
}
|
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返回:
|
||
生成的pyautogui代码字符串
|
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'''
|
||
|
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pyautogui_code = f"import pyautogui\nimport time\n"
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if isinstance(responses, dict):
|
||
responses = [responses]
|
||
for response_id, response in enumerate(responses):
|
||
if "observation" in response:
|
||
observation = response["observation"]
|
||
else:
|
||
observation = ""
|
||
|
||
if "thought" in response:
|
||
thought = response["thought"]
|
||
else:
|
||
thought = ""
|
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|
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if response_id == 0:
|
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pass
|
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else:
|
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pyautogui_code += f"\ntime.sleep(3)\n"
|
||
|
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action_dict = response
|
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action_type = action_dict.get("action_type")
|
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action_inputs = action_dict.get("action_inputs", {})
|
||
|
||
if action_type == "hotkey":
|
||
# Parsing hotkey action
|
||
if "key" in action_inputs:
|
||
hotkey = action_inputs.get("key", "")
|
||
else:
|
||
hotkey = action_inputs.get("hotkey", "")
|
||
|
||
if hotkey:
|
||
# Handle other hotkeys
|
||
keys = hotkey.split() # Split the keys by space
|
||
pyautogui_code += f"\npyautogui.hotkey({', '.join([repr(k) for k in keys])})"
|
||
|
||
elif action_type == "type":
|
||
# Parsing typing action using clipboard
|
||
content = action_inputs.get("content", "")
|
||
content = escape_single_quotes(content)
|
||
if content:
|
||
if input_swap:
|
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pyautogui_code += f"\nimport pyperclip"
|
||
pyautogui_code += f"\npyperclip.copy('{content.strip()}')"
|
||
pyautogui_code += f"\npyautogui.hotkey('ctrl', 'v')"
|
||
pyautogui_code += f"\ntime.sleep(0.5)\n"
|
||
if content.endswith("\n") or content.endswith("\\n"):
|
||
pyautogui_code += f"\npyautogui.press('enter')"
|
||
else:
|
||
if action_inputs['clear']==1:
|
||
pyautogui_code += f"\npyautogui.hotkey('ctrl', 'a')"
|
||
pyautogui_code += f"\npyautogui.press('backspace')"
|
||
pyautogui_code += f"\npyautogui.write('{content.strip()}', interval=0.1)"
|
||
pyautogui_code += f"\ntime.sleep(0.5)\n"
|
||
if content.endswith("\n") or content.endswith("\\n") or action_inputs['enter']==1:
|
||
pyautogui_code += f"\npyautogui.press('enter')"
|
||
|
||
|
||
elif action_type in ["drag", "select"]:
|
||
# Parsing drag or select action based on start and end_boxes
|
||
start_box = action_inputs.get("start_box")
|
||
end_box = action_inputs.get("end_box")
|
||
if start_box and end_box:
|
||
sx, sy = start_box
|
||
ex, ey = end_box
|
||
pyautogui_code += (
|
||
f"\npyautogui.moveTo({sx}, {sy})\n"
|
||
f"\npyautogui.dragTo({ex}, {ey}, duration=1.0)\n"
|
||
)
|
||
|
||
elif action_type == "scroll":
|
||
# Parsing scroll action
|
||
start_box = action_inputs.get("start_box")
|
||
if start_box:
|
||
x1, y1, x2, y2 = eval(start_box) # Assuming box is in [x1, y1, x2, y2]
|
||
x = round(float((x1 + x2) / 2) * image_width, 3)
|
||
y = round(float((y1 + y2) / 2) * image_height, 3)
|
||
else:
|
||
x = None
|
||
y = None
|
||
pixels = action_inputs.get("pixels")
|
||
pyautogui_code += f"\npyautogui.scroll({pixels})"
|
||
|
||
elif action_type in ["click", "left_single", "left_double", "right_single", "hover"]:
|
||
# Parsing mouse click actions
|
||
start_box = action_inputs.get("start_box")
|
||
start_box = str(start_box)
|
||
if start_box:
|
||
start_box = eval(start_box)
|
||
if len(start_box) == 4:
|
||
x1, y1, x2, y2 = start_box # Assuming box is in [x1, y1, x2, y2]
|
||
elif len(start_box) == 2:
|
||
x1, y1 = start_box
|
||
x2 = x1
|
||
y2 = y1
|
||
x = x1
|
||
y = y1
|
||
|
||
if action_type == "left_single" or action_type == "click":
|
||
pyautogui_code += f"\npyautogui.click({x}, {y}, button='left')"
|
||
elif action_type == "left_double":
|
||
pyautogui_code += f"\npyautogui.doubleClick({x}, {y}, button='left')"
|
||
elif action_type == "right_single":
|
||
pyautogui_code += f"\npyautogui.click({x}, {y}, button='right')"
|
||
elif action_type == "hover":
|
||
pyautogui_code += f"\npyautogui.moveTo({x}, {y})"
|
||
|
||
elif action_type in ["finished"]:
|
||
pyautogui_code = f"DONE"
|
||
|
||
elif action_type in ["fail"]:
|
||
pyautogui_code = f"FAIL"
|
||
|
||
elif action_type in ["wait"]:
|
||
pyautogui_code += f"time.sleep({action_inputs['time']})"
|
||
|
||
else:
|
||
pyautogui_code += f"\n# Unrecognized action type: {action_type}"
|
||
|
||
return pyautogui_code
|
||
|
||
def pil_to_base64(image):
|
||
buffer = BytesIO()
|
||
image.save(buffer, format="PNG")
|
||
return base64.b64encode(buffer.getvalue()).decode("utf-8")
|
||
|
||
def linearize_accessibility_tree(accessibility_tree, platform="ubuntu"):
|
||
|
||
if platform == "ubuntu":
|
||
_attributes_ns = attributes_ns_ubuntu
|
||
_state_ns = state_ns_ubuntu
|
||
_component_ns = component_ns_ubuntu
|
||
_value_ns = value_ns_ubuntu
|
||
elif platform == "windows":
|
||
_attributes_ns = attributes_ns_windows
|
||
_state_ns = state_ns_windows
|
||
_component_ns = component_ns_windows
|
||
_value_ns = value_ns_windows
|
||
else:
|
||
raise ValueError("Invalid platform, must be 'ubuntu' or 'windows'")
|
||
|
||
filtered_nodes = filter_nodes(ET.fromstring(accessibility_tree), platform)
|
||
linearized_accessibility_tree = [
|
||
"tag\tname\ttext\tclass\tdescription\tposition (top-left x&y)\tsize (w&h)"
|
||
]
|
||
|
||
# Linearize the accessibility tree nodes into a table format
|
||
for node in filtered_nodes:
|
||
if node.text:
|
||
text = (
|
||
node.text
|
||
if '"' not in node.text
|
||
else '"{:}"'.format(node.text.replace('"', '""'))
|
||
)
|
||
|
||
elif node.get("{{{:}}}class".format(class_ns_windows), "").endswith(
|
||
"EditWrapper"
|
||
) and node.get("{{{:}}}value".format(_value_ns)):
|
||
node_text = node.get("{{{:}}}value".format(_value_ns), "")
|
||
text = (
|
||
node_text
|
||
if '"' not in node_text
|
||
else '"{:}"'.format(node_text.replace('"', '""'))
|
||
)
|
||
else:
|
||
text = '""'
|
||
|
||
linearized_accessibility_tree.append(
|
||
"{:}\t{:}\t{:}\t{:}\t{:}\t{:}\t{:}".format(
|
||
node.tag,
|
||
node.get("name", ""),
|
||
text,
|
||
(
|
||
node.get("{{{:}}}class".format(_attributes_ns), "")
|
||
if platform == "ubuntu"
|
||
else node.get("{{{:}}}class".format(class_ns_windows), "")
|
||
),
|
||
node.get("{{{:}}}description".format(_attributes_ns), ""),
|
||
node.get("{{{:}}}screencoord".format(_component_ns), ""),
|
||
node.get("{{{:}}}size".format(_component_ns), ""),
|
||
)
|
||
)
|
||
|
||
return "\n".join(linearized_accessibility_tree)
|
||
|
||
def trim_accessibility_tree(linearized_accessibility_tree, max_tokens):
|
||
# enc = tiktoken.encoding_for_model("gpt-4")
|
||
# tokens = enc.encode(linearized_accessibility_tree)
|
||
# if len(tokens) > max_tokens:
|
||
# linearized_accessibility_tree = enc.decode(tokens[:max_tokens])
|
||
# linearized_accessibility_tree += "[...]\n"
|
||
return linearized_accessibility_tree
|
||
|
||
class OwlAgent:
|
||
def __init__(
|
||
self,
|
||
model='',
|
||
api_url='',
|
||
api_key='',
|
||
platform="ubuntu",
|
||
max_tokens=1000,
|
||
history_n=1,
|
||
top_p=0.9,
|
||
top_k=1,
|
||
temperature=0.0,
|
||
action_space="pyautogui",
|
||
observation_type="screenshot",
|
||
max_trajectory_length=50,
|
||
a11y_tree_max_tokens=10000,
|
||
runtime_conf: dict = {
|
||
"infer_mode": "fn_call",
|
||
"input_swap": False,
|
||
"screen_height": 1080,
|
||
"screen_width": 1920,
|
||
},
|
||
engine="openai"
|
||
):
|
||
self.model = model
|
||
self.platform = platform
|
||
self.max_tokens = max_tokens
|
||
self.top_p = top_p
|
||
self.top_k = top_k
|
||
self.temperature = temperature
|
||
self.action_space = action_space
|
||
self.observation_type = observation_type
|
||
self.max_trajectory_length = max_trajectory_length
|
||
self.a11y_tree_max_tokens = a11y_tree_max_tokens
|
||
self.runtime_conf = runtime_conf
|
||
|
||
self.infer_mode = self.runtime_conf["infer_mode"]
|
||
self.input_swap = self.runtime_conf["input_swap"]
|
||
|
||
self.engine = engine
|
||
self.api_key = api_key
|
||
if engine == 'openai':
|
||
self.llm_client = OpenAI(base_url=api_url, api_key=api_key)
|
||
|
||
self.thoughts = []
|
||
self.actions = []
|
||
self.observations = []
|
||
self.history_images = []
|
||
self.history_responses = []
|
||
self.history_conclusion = []
|
||
|
||
self.customize_action_parser = parse_action_fncall
|
||
|
||
if self.infer_mode == "fn_call":
|
||
self.prompt_template = get_prompt
|
||
|
||
self.history_n = history_n
|
||
self.image_format = "url"
|
||
|
||
def predict(
|
||
self, instruction: str, obs: Dict, last_action_after_obs: Dict = None
|
||
) -> List:
|
||
"""
|
||
Predict the next action(s) based on the current observation.
|
||
"""
|
||
|
||
print('---------', self.model)
|
||
model_name = self.model
|
||
|
||
step_idx = 0
|
||
example_result_dir = "abc/osworld"
|
||
instruction_id = example_result_dir.split('/')[-1]
|
||
|
||
assert len(self.observations) == len(self.actions) and len(self.actions) == len(
|
||
self.thoughts
|
||
), "The number of observations and actions should be the same."
|
||
|
||
if len(self.observations) > self.max_trajectory_length:
|
||
if self.max_trajectory_length == 0:
|
||
_observations = []
|
||
_actions = []
|
||
_thoughts = []
|
||
else:
|
||
_observations = self.observations[-self.max_trajectory_length :]
|
||
_actions = self.actions[-self.max_trajectory_length :]
|
||
_thoughts = self.thoughts[-self.max_trajectory_length :]
|
||
else:
|
||
_observations = self.observations
|
||
_actions = self.actions
|
||
_thoughts = self.thoughts
|
||
|
||
for previous_obs, previous_action, previous_thought in zip(
|
||
_observations, _actions, _thoughts
|
||
):
|
||
# {{{1
|
||
if self.observation_type == "screenshot_a11y_tree":
|
||
_screenshot = previous_obs["screenshot"]
|
||
_linearized_accessibility_tree = previous_obs["accessibility_tree"]
|
||
elif self.observation_type == "screenshot":
|
||
_screenshot = previous_obs["screenshot"]
|
||
_linearized_accessibility_tree = None
|
||
else:
|
||
raise ValueError(
|
||
"Invalid observation_type type: " + self.observation_type
|
||
) # 1}}}
|
||
|
||
if last_action_after_obs is not None and self.infer_mode == "double_image":
|
||
self.history_images.append(last_action_after_obs["screenshot"])
|
||
pass
|
||
self.history_images.append(obs["screenshot"])
|
||
|
||
if self.observation_type in ["screenshot", "screenshot_a11y_tree"]:
|
||
base64_image = obs["screenshot"]
|
||
try:
|
||
linearized_accessibility_tree = (
|
||
linearize_accessibility_tree(
|
||
accessibility_tree=obs["accessibility_tree"],
|
||
platform=self.platform,
|
||
)
|
||
if self.observation_type == "screenshot_a11y_tree"
|
||
else None
|
||
)
|
||
except:
|
||
linearized_accessibility_tree = None
|
||
|
||
if linearized_accessibility_tree:
|
||
linearized_accessibility_tree = trim_accessibility_tree(
|
||
linearized_accessibility_tree, self.a11y_tree_max_tokens
|
||
)
|
||
|
||
if self.observation_type == "screenshot_a11y_tree":
|
||
self.observations.append(
|
||
{
|
||
"screenshot": base64_image,
|
||
"accessibility_tree": linearized_accessibility_tree,
|
||
}
|
||
)
|
||
else:
|
||
self.observations.append(
|
||
{"screenshot": base64_image, "accessibility_tree": None}
|
||
)
|
||
|
||
else:
|
||
raise ValueError(
|
||
"Invalid observation_type type: " + self.observation_type
|
||
) # }}}
|
||
|
||
if len(self.history_conclusion) == 0:
|
||
history_str = "No history. This is the first step."
|
||
else:
|
||
history_str = ""
|
||
for idx in range(len(self.history_conclusion)):
|
||
if self.history_conclusion[idx] is not None:
|
||
history_str += "Step %d: "%(idx+1) + self.history_conclusion[idx] + "\n"
|
||
|
||
if self.infer_mode == "fn_call":
|
||
user_prompt = self.prompt_template(
|
||
width=self.runtime_conf["screen_width"],
|
||
height=self.runtime_conf["screen_height"],
|
||
instruction=instruction,
|
||
history=history_str,
|
||
engine=self.engine
|
||
)
|
||
|
||
if len(self.history_images) > self.history_n:
|
||
self.history_images = self.history_images[-self.history_n:]
|
||
|
||
max_pixels = 3000 * 28 * 28
|
||
min_pixels = 100 * 28 * 28
|
||
messages, images = [], []
|
||
if isinstance(self.history_images, bytes):
|
||
self.history_images = [self.history_images]
|
||
elif isinstance(self.history_images, np.ndarray):
|
||
self.history_images = list(self.history_images)
|
||
elif isinstance(self.history_images, list):
|
||
pass
|
||
else:
|
||
raise TypeError(f"Unidentified images type: {type(self.history_images)}")
|
||
max_image_nums_under_32k = int(32768*0.75/max_pixels*28*28)
|
||
if len(self.history_images) > max_image_nums_under_32k:
|
||
num_of_images = min(5, len(self.history_images))
|
||
max_pixels = int(32768*0.75) // num_of_images
|
||
|
||
for turn, image in enumerate(self.history_images):
|
||
if len(images) >= 5:
|
||
break
|
||
try:
|
||
image = Image.open(BytesIO(image))
|
||
except Exception as e:
|
||
raise RuntimeError(f"Error opening image: {e}")
|
||
|
||
if image.width * image.height > max_pixels:
|
||
"""
|
||
如果图片超过/低于像素限制，则计算一个缩放因子resize_factor，使图片的像素数缩小到等于或小于max_pixels。这个缩放因子是通过开平方根计算的，确保纵横比保持不变,这样原始的相对坐标可以不经转换直接复用
|
||
"""
|
||
resize_factor = math.sqrt(max_pixels / (image.width * image.height))
|
||
width, height = int(image.width * resize_factor), int(image.height * resize_factor)
|
||
image = image.resize((width, height))
|
||
if image.width * image.height < min_pixels:
|
||
resize_factor = math.sqrt(min_pixels / (image.width * image.height))
|
||
width, height = math.ceil(image.width * resize_factor), math.ceil(image.height * resize_factor)
|
||
image = image.resize((width, height))
|
||
|
||
if image.mode != "RGB":
|
||
image = image.convert("RGB")
|
||
|
||
images.append(image)
|
||
|
||
messages = user_prompt
|
||
|
||
image_num = 0
|
||
|
||
if len(self.history_responses) > 0:
|
||
for history_idx, history_response in enumerate(self.history_responses):
|
||
# send at most history_n images to the model
|
||
if history_idx + self.history_n > len(self.history_responses):
|
||
|
||
cur_image = images[image_num]
|
||
encoded_string = pil_to_base64(cur_image)
|
||
if self.engine == 'openai':
|
||
messages[-1]['content'].append({"type": "text", "text": "Screenshot of step %d:\n"%(history_idx+1)})
|
||
if self.image_format == 'url':
|
||
messages[-1]['content'].append({"type": "image_url", "image_url": {"url": get_image_url(encoded_string)}})
|
||
else:
|
||
messages[-1]['content'].append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_string}"}})
|
||
else:
|
||
messages[-1]['content'].append({"text": "Screenshot of step %d:\n"%(history_idx+1)})
|
||
messages[-1]['content'].append({"image": f"data:image/png;base64,{encoded_string}"})
|
||
|
||
image_num += 1
|
||
|
||
cur_image = images[image_num]
|
||
encoded_string = pil_to_base64(cur_image)
|
||
if self.engine == 'openai':
|
||
messages[-1]['content'].append({"type": "text", "text": "Current screenshot:\n"})
|
||
if self.image_format == 'url':
|
||
messages[-1]['content'].append({"type": "image_url", "image_url": {"url": get_image_url(encoded_string)}})
|
||
else:
|
||
messages[-1]['content'].append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_string}"}})
|
||
else:
|
||
messages[-1]['content'].append({"text": "Current screenshot:\n"})
|
||
messages[-1]['content'].append({"image": f"data:image/png;base64,{encoded_string}"})
|
||
|
||
image_num += 1
|
||
|
||
else:
|
||
cur_image = images[image_num]
|
||
encoded_string = pil_to_base64(cur_image)
|
||
if self.engine == 'openai':
|
||
messages[-1]['content'].append({"type": "text", "text": "Current screenshot:\n"})
|
||
if self.image_format == 'url':
|
||
messages[-1]['content'].append({"type": "image_url", "image_url": {"url": get_image_url(encoded_string)}})
|
||
else:
|
||
messages[-1]['content'].append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{encoded_string}"}})
|
||
else:
|
||
messages[-1]['content'].append({"text": "Current screenshot:\n"})
|
||
messages[-1]['content'].append({"image": f"data:image/png;base64,{encoded_string}"})
|
||
|
||
image_num += 1
|
||
|
||
try_times = 20
|
||
while True:
|
||
if try_times <= 0:
|
||
print(f"Reach max retry times to fetch response from client, as error flag.")
|
||
return "client error", ["DONE"]
|
||
try:
|
||
if self.engine == 'openai':
|
||
response = self.llm_client.chat.completions.create(
|
||
model=model_name,
|
||
messages=messages,
|
||
max_tokens=2048,
|
||
temperature=self.temperature,
|
||
top_p=self.top_p,
|
||
extra_body={"repetition_penalty": 1.05, "top_k": -1},
|
||
)
|
||
# print(response)
|
||
prediction = response.choices[0].message.content
|
||
else:
|
||
import dashscope
|
||
dashscope.api_key = self.api_key
|
||
if 'pre' in self.model:
|
||
dashscope.base_http_api_url = "https://poc-dashscope.aliyuncs.com/api/v1"
|
||
dashscope.base_websocket_api_url = "https://poc-dashscope.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation"
|
||
response = dashscope.MultiModalConversation.call(
|
||
model=self.model,
|
||
messages=messages,
|
||
max_tokens=2048,
|
||
temperature=self.temperature,
|
||
top_p=self.top_p,
|
||
top_k=self.top_k,
|
||
vl_high_resolution_images=True
|
||
)
|
||
prediction = response.output.choices[0].message.content[0]['text']
|
||
|
||
parsed_responses = self.customize_action_parser(
|
||
prediction,
|
||
None,
|
||
self.runtime_conf["screen_height"],
|
||
self.runtime_conf["screen_width"]
|
||
)
|
||
break
|
||
except Exception as e:
|
||
# print(f"Error when fetching response from client, with response: {response}")
|
||
print(f"Error when fetching response from client, with error: {e}")
|
||
prediction = None
|
||
try_times -= 1
|
||
import time
|
||
time.sleep(1)
|
||
|
||
if prediction is None:
|
||
return "client error", ["DONE"]
|
||
|
||
self.history_responses.append(prediction)
|
||
self.thoughts.append(prediction)
|
||
|
||
try:
|
||
parsed_responses = self.customize_action_parser(
|
||
prediction,
|
||
None,
|
||
self.runtime_conf["screen_height"],
|
||
self.runtime_conf["screen_width"]
|
||
)
|
||
except Exception as e:
|
||
print(f"Parsing action error: {prediction}, with error:\n{e}")
|
||
return f"Parsing action error: {prediction}, with error:\n{e}", ["DONE"]
|
||
|
||
this_step_conclusion = parsed_responses[0]['conclusion']
|
||
self.history_conclusion.append(this_step_conclusion)
|
||
|
||
actions = []
|
||
for parsed_response in parsed_responses:
|
||
if "action_type" in parsed_response:
|
||
if parsed_response["action_type"] == "fail":
|
||
self.actions.append(actions)
|
||
return prediction, ["FAIL"]
|
||
elif parsed_response["action_type"] == "finished":
|
||
self.actions.append(actions)
|
||
return prediction, ["DONE"]
|
||
|
||
pyautogui_code = parsing_response_to_pyautogui_code(
|
||
parsed_response,
|
||
self.runtime_conf["screen_height"],
|
||
self.runtime_conf["screen_width"],
|
||
self.input_swap
|
||
)
|
||
actions.append(pyautogui_code)
|
||
|
||
self.actions.append(actions)
|
||
|
||
if len(self.history_responses) >= self.max_trajectory_length:
|
||
# Default to FAIL if exceed max steps
|
||
actions = ["FAIL"]
|
||
|
||
return prediction, actions
|
||
|
||
@backoff.on_exception(
|
||
backoff.constant,
|
||
# here you should add more model exceptions as you want,
|
||
# but you are forbidden to add "Exception", that is, a common type of exception
|
||
# because we want to catch this kind of Exception in the outside to ensure each example won't exceed the time limit
|
||
(
|
||
# General exceptions
|
||
SSLError,
|
||
# OpenAI exceptions
|
||
openai.RateLimitError,
|
||
openai.BadRequestError,
|
||
openai.InternalServerError,
|
||
# Google exceptions
|
||
InvalidArgument,
|
||
ResourceExhausted,
|
||
InternalServerError,
|
||
BadRequest,
|
||
# Groq exceptions
|
||
# todo: check
|
||
),
|
||
interval=30,
|
||
max_tries=10,
|
||
)
|
||
|
||
def reset(self, runtime_logger):
|
||
self.thoughts = []
|
||
self.actions = []
|
||
self.observations = []
|
||
self.history_images = []
|
||
self.history_responses = []
|
||
self.history_conclusion = []
|