app.py

import json
import time
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
from enum import Enum

# Assuming these libraries are available for browser automation and LLM interaction
# from playwright.sync_api import sync_playwright
# import openai  # or another LLM API client

# Define enums for status tracking
class Status(Enum):
    SUCCESS = "success"
    FAILURE = "failure"
    PENDING = "pending"

# Data classes for structured data handling
@dataclass
class SubGoal:
    id: str
    description: str
    expected_state: str
    status: Status = Status.PENDING

@dataclass
class TaskResult:
    status: Status
    output_data: Optional[Dict] = None
    error_message: Optional[str] = None

# Core modules of the agent
class PlannerModule:
    """
    Planner Module: Breaks down user commands into executable sub-goals.
    """
    def __init__(self, llm_client):
        self.llm_client = llm_client
        self.working_memory = {
            "current_state": "",
            "completed_subgoals": [],
            "pending_subgoals": []
        }

    def create_plan(self, user_command: str) -> List[SubGoal]:
        """
        Uses LLM to generate a step-by-step plan from the user command.
        Returns a list of sub-goals.
        """
        prompt = f"""
        Convert the following user command into discrete browser automation steps:
        "{user_command}"
        
        Return a JSON array of steps with these keys:
        - id: unique identifier for the step
        - description: detailed description of the action to take
        - expected_state: what the page should look like after completion
        
        Example format:
        [
            {{
                "id": "1",
                "description": "Navigate to https://example.com",
                "expected_state": "Homepage with logo visible"
            }},
            {{
                "id": "2",
                "description": "Click on the 'Login' button",
                "expected_state": "Login form appears with username and password fields"
            }}
        ]
        """
        
        # response = self.llm_client.generate_response(prompt)
        # For demonstration, returning a mock plan
        mock_plan = [
            SubGoal(id="1", description="Navigate to website", expected_state="Page loaded"),
            SubGoal(id="2", description="Click sign-in button", expected_state="Login form visible"),
            SubGoal(id="3", description="Enter credentials", expected_state="User logged in")
        ]
        self.working_memory["pending_subgoals"] = mock_plan
        return mock_plan

    def replan(self, failed_subgoal: SubGoal, error_reason: str) -> List[SubGoal]:
        """
        Re-generates plan based on failure feedback from Validator.
        """
        prompt = f"""
        The following step failed: "{failed_subgoal.description}"
        Reason: "{error_reason}"
        
        Generate alternative steps to achieve the same goal.
        """
        
        # response = self.llm_client.generate_response(prompt)
        # For demonstration, returning a mock replan
        mock_replan = [
            SubGoal(id="2a", description="Click alternative sign-in button", expected_state="Login form visible"),
            SubGoal(id="2b", description="Wait for 5 seconds and retry click", expected_state="Login form visible")
        ]
        return mock_replan


class ActorModule:
    """
    Actor Module: Executes browser actions based on sub-goals.
    """
    def __init__(self, browser_controller):
        self.browser = browser_controller

    def execute_action(self, subgoal: SubGoal) -> TaskResult:
        """
        Performs the specified action in the browser.
        """
        try:
            # Parse subgoal description and perform corresponding action
            if "navigate" in subgoal.description.lower():
                url = subgoal.description.split(" ")[-1]
                self.browser.navigate(url)
            elif "click" in subgoal.description.lower():
                element = self.browser.find_element_by_text("Sign In")
                self.browser.click(element)
            elif "enter" in subgoal.description.lower():
                input_field = self.browser.find_element_by_label("username")
                self.browser.type(input_field, "user@example.com")
            
            return TaskResult(status=Status.SUCCESS)
        except Exception as e:
            return TaskResult(status=Status.FAILURE, error_message=str(e))


class ValidatorModule:
    """
    Validator Module: Verifies if actions were successful.
    """
    def __init__(self, llm_client):
        self.llm_client = llm_client

    def validate(self, subgoal: SubGoal, browser_state: Dict) -> Tuple[Status, str]:
        """
        Compares current browser state with expected state using LLM.
        Returns validation status and optional message.
        """
        prompt = f"""
        Goal: {subgoal.description}
        Expected State: {subgoal.expected_state}
        Current State: {json.dumps(browser_state)}
        
        Has the goal been successfully achieved? Respond with YES or NO followed by reason.
        """
        
        # response = self.llm_client.generate_response(prompt)
        # For demonstration, returning mock validation
        if subgoal.id == "2":
            return (Status.SUCCESS, "Login form is visible")
        else:
            return (Status.FAILURE, "Element not found or page not loaded as expected")


# Main automation agent using planner-actor-validator loop
class SkyvernAgent:
    """
    Main automation agent implementing the Planner-Actor-Validator loop.
    """
    def __init__(self):
        # self.llm_client = openai.Client(api_key="YOUR_API_KEY")
        # self.browser_controller = sync_playwright()
        self.planner = PlannerModule(llm_client=None)
        self.actor = ActorModule(browser_controller=None)
        self.validator = ValidatorModule(llm_client=None)
        self.settings = {}

    def run_task(self, user_command: str, settings: Dict) -> TaskResult:
        """
        Main automation loop that coordinates all modules.
        """
        self.settings = settings
        plan = self.planner.create_plan(user_command)
        output_data = {}
        
        step_count = 0
        max_steps = settings.get("max_steps", 100)
        
        while plan and step_count < max_steps:
            current_subgoal = plan.pop(0)
            step_count += 1
            
            # Actor executes the action
            result = self.actor.execute_action(current_subgoal)
            
            if result.status == Status.SUCCESS:
                # Validator checks if the action was successful
                browser_state = self._get_browser_state()
                validation_status, message = self.validator.validate(current_subgoal, browser_state)
                
                if validation_status == Status.SUCCESS:
                    self.planner.working_memory["completed_subgoals"].append(current_subgoal)
                    # Extract data if defined in schema
                    if "data_schema" in settings:
                        output_data = self._extract_data(browser_state, settings["data_schema"])
                else:
                    # Replanning when validation fails
                    new_plan = self.planner.replan(current_subgoal, message)
                    plan = new_plan + plan  # Prepend new steps to existing plan
            else:
                # Replanning when action fails
                new_plan = self.planner.replan(current_subgoal, result.error_message)
                plan = new_plan + plan  # Prepend new steps to existing plan
        
        # Return final result
        final_status = Status.SUCCESS if not plan else Status.FAILURE
        return TaskResult(status=final_status, output_data=output_data)

    def _get_browser_state(self) -> Dict:
        """
        Captures current browser state (screenshot, HTML, URL, etc.)
        """
        return {
            "url": "https://example.com",
            "html": "<html>...</html>",
            "screenshot": "base64-encoded-screenshot"
        }

    def _extract_data(self, browser_state: Dict, schema: Dict) -> Dict:
        """
        Extracts data based on provided schema.
        """
        # Using LLM to extract structured data according to schema
        prompt = f"""
        Extract data from the following browser state according to this schema:
        Schema: {json.dumps(schema)}
        State: {json.dumps(browser_state)}
        
        Return a JSON object with extracted data.
        """
        # response = self.llm.generate_response(prompt)
        # return json.loads(response)
        return {"extracted": "data"}  # Mock response

# Example usage
if __name__ == "__main__":
    agent = SkyvernAgent()
    command = "Add a product to the cart"
    settings = {
        "webhook_url": "https://your-webhook-url.com",
        "proxy_type": "residential",
        "session_id": "session_12345",
        "two_factor_id": "2fa_67890",
        "http_headers": {"User-Agent": "Custom Browser"},
        "publish_workflow": True,
        "max_steps": 50,
        "data_schema": {"product_name": "string", "price": "number"},
        "max_scrolls": 5
    }
    
    result = agent.run_task(command, settings)
    print(json.dumps(result.__dict__, indent=2))