browser_agent.py

import logging
import os
from typing import Any, List, Tuple

from browsergym.core.action.highlevel import HighLevelActionSet
from browsergym.utils.obs import (
    flatten_axtree_to_str,
    flatten_dom_to_str,
    prune_html,
)
from browsergym.experiments import Agent

from utils import remove_inline_comments_safe, image_to_jpg_base64_url

import openai


logger = logging.getLogger(__name__)

openai.api_key = os.getenv("OPENAI_API_KEY")



class BrowserAgent(Agent):
    def obs_preprocessor(self, obs: dict) -> dict:
        return {
            "chat_messages": obs["chat_messages"],
            "som_screenshot": obs["som_screenshot"],
            "goal_object": obs["goal_object"],
            "last_action": obs["last_action"],
            "last_action_error": obs["last_action_error"],
            "open_pages_urls": obs["open_pages_urls"],
            "open_pages_titles": obs["open_pages_titles"],
            "active_page_index": obs["active_page_index"],
            "axtree_txt": flatten_axtree_to_str(obs["axtree_object"], filter_visible_only=True, extra_properties=obs['extra_element_properties'], filter_som_only=True),
            "pruned_html": prune_html(flatten_dom_to_str(obs["dom_object"])),
        }

    def __init__(self, model_name: str = "gpt-4o", use_html: bool = False, use_axtree: bool = True, use_screenshot: bool = False):
        super().__init__()
        logger.info(f"Initializing BrowserAgent with model: {model_name}")
        logger.info(f"Observation space: HTML={use_html}, AXTree={use_axtree}, Screenshot={use_screenshot}")
        
        self.model_name = model_name
        self.use_html = use_html
        self.use_axtree = use_axtree
        self.use_screenshot = use_screenshot
        
        if not (use_html or use_axtree):
            raise ValueError("Either use_html or use_axtree must be set to True.")
        
        self.openai_client = openai.OpenAI()
        
        self.action_set = HighLevelActionSet(
            subsets=["chat", "tab", "nav", "bid", "infeas"],
            strict=False,
            multiaction=False,
            demo_mode="default"
        )
        self.action_history = []

    def get_action(self, obs: dict) -> tuple[str, dict]:
        logger.debug("Preparing action request")
        
        system_msgs = [{
            "type": "text",
            "text": """\
# Instructions

You are a UI Assistant, your goal is to help the user perform tasks using a web browser. You can
communicate with the user via a chat, to which the user gives you instructions and to which you
can send back messages. You have access to a web browser that both you and the user can see,
and with which only you can interact via specific commands.

Review the instructions from the user, the current state of the page and all other information
to find the best possible next action to accomplish your goal. Your answer will be interpreted
and executed by a program, make sure to follow the formatting instructions.
"""
        }]

        user_msgs = []
        
        # Add chat messages
        user_msgs.append({
            "type": "text",
            "text": "# Chat Messages\n"
        })
        for msg in obs["chat_messages"]:
            if msg["role"] in ("user", "assistant", "infeasible"):
                user_msgs.append({
                    "type": "text",
                    "text": f"- [{msg['role']}] {msg['message']}\n"
                })
                logger.debug(f"Added chat message: [{msg['role']}] {msg['message']}")
            elif msg["role"] == "user_image":
                user_msgs.append({"type": "image_url", "image_url": msg["message"]})
                logger.debug("Added user image message")

        # Add open tabs info
        user_msgs.append({
            "type": "text",
            "text": "# Currently open tabs\n"
        })
        for page_index, (page_url, page_title) in enumerate(
            zip(obs["open_pages_urls"], obs["open_pages_titles"])
        ):
            user_msgs.append({
                "type": "text",
                "text": f"""\
Tab {page_index}{" (active tab)" if page_index == obs["active_page_index"] else ""}
  Title: {page_title}
  URL: {page_url}
"""
            })
            logger.debug(f"Added tab info: {page_title} ({page_url})")

        # Add accessibility tree if enabled
        if self.use_axtree:
            user_msgs.append({
                "type": "text",
                "text": f"""\
# Current page Accessibility Tree

{obs["axtree_txt"]}

"""
            })
            logger.debug("Added accessibility tree")

        # Add HTML if enabled
        if self.use_html:
            user_msgs.append({
                "type": "text",
                "text": f"""\
# Current page DOM

{obs["pruned_html"]}

"""
            })
            logger.debug("Added HTML DOM")

        # Add screenshot if enabled
        if self.use_screenshot:
            user_msgs.append({
                "type": "text",
                "text": "# Current page Screenshot\n"
            })
            user_msgs.append({
                "type": "image_url",
                "image_url": {
                    "url": image_to_jpg_base64_url(obs["som_screenshot"]),
                    "detail": "auto"
                }
            })
            logger.debug("Added screenshot")

        # Add action space description
        user_msgs.append({
            "type": "text",
            "text": f"""\
# Action Space

{self.action_set.describe(with_long_description=False, with_examples=True)}

Here are examples of actions with chain-of-thought reasoning:

I now need to click on the Submit button to send the form. I will use the click action on the button, which has bid 12.
```click("12")```

I found the information requested by the user, I will send it to the chat.
```send_msg_to_user("The price for a 15\\" laptop is 1499 USD.")```

"""
        })

        # Add action history and errors
        if self.action_history:
            user_msgs.append({
                "type": "text",
                "text": "# History of past actions\n"
            })
            for action in self.action_history:
                user_msgs.append({
                    "type": "text",
                    "text": f"\n{action}\n"
                })
                logger.debug(f"Added past action: {action}")

            if obs["last_action_error"]:
                user_msgs.append({
                    "type": "text",
                    "text": f"""\
# Error message from last action

{obs["last_action_error"]}

"""
                })
                logger.warning(f"Last action error: {obs['last_action_error']}")

        # Ask for next action
        user_msgs.append({
            "type": "text",
            "text": """\
# Next action

You will now think step by step and produce your next best action. Reflect on your past actions, any resulting error message, and the current state of the page before deciding on your next action.
Note: You might use 'goto' action if you're in a blank page.
"""
        })

        # Log the full prompt for debugging
        prompt_text_strings = []
        for message in system_msgs + user_msgs:
            match message["type"]:
                case "text":
                    prompt_text_strings.append(message["text"])
                case "image_url":
                    image_url = message["image_url"]
                    if isinstance(message["image_url"], dict):
                        image_url = image_url["url"]
                    if image_url.startswith("data:image"):
                        prompt_text_strings.append(
                            "image_url: " + image_url[:30] + "... (truncated)"
                        )
                    else:
                        prompt_text_strings.append("image_url: " + image_url)
                case _:
                    raise ValueError(
                        f"Unknown message type {repr(message['type'])} in the task goal."
                    )
        full_prompt_txt = "\n".join(prompt_text_strings)
        logger.debug(full_prompt_txt)

        # Query OpenAI model
        logger.info("Sending request to OpenAI")
        response = self.openai_client.chat.completions.create(
            model=self.model_name,
            messages=[
                {"role": "system", "content": system_msgs},
                {"role": "user", "content": user_msgs}
            ],
            n=20,
            temperature=0.8
        )
        parses = []
        for i, choice in enumerate(response.choices):
            response = choice.message.content
            try:
                parses.append({
                    'response': response,
                    'thought': response.split('```')[0].strip(),
                    'action': remove_inline_comments_safe(response.split('```')[1].strip('`').strip().strip('`').strip()),
                })
            except Exception as e:
                logger.error(f"Error parsing action: {e}")
                logger.error(f"Response: {response}")
                logger.error(f"Choice: {choice}")
                logger.error(f"Index: {i}")
                logger.error(f"Response: {response}")
                
        candidates = self.get_top_k_actions(parses)
        logger.info(f"Received action from OpenAI: {[cand['action'] for cand in candidates]}")
        return candidates, {}

    def get_top_k_actions(self, parses, k=3):
        count_dict = {}
        action_to_parsed = {}
        for parsed in parses:
            action = parsed["action"]
            if action in count_dict:
                count_dict[action] += 1
            else:
                count_dict[action] = 1
                action_to_parsed[action] = parsed.copy()
        
        # Get the top_k most frequent actions
        sorted_actions = sorted(count_dict.items(), key=lambda x: x[1], reverse=True)
        top_k_actions = [action_to_parsed[action] for action, _ in sorted_actions[:k]]
        
        return top_k_actions