#!/usr/bin/env python3
"""
linkedin-parser.py - Structure LinkedIn profile data

Inputs:
  - profile_url: string
  - or name + company: strings

Outputs:
  - contact:
      name: string
      title: string
      tenure: string
      previous_roles: [role objects]
      mutual_connections: [string]
      recent_activity: [post summaries]

Dependencies:
  - requests
"""

# Requirements: requests

import json
from typing import Any
from dataclasses import dataclass, asdict
from datetime import datetime


@dataclass
class PreviousRole:
    title: str
    company: str
    duration: str
    description: str


@dataclass
class RecentPost:
    date: str
    content_preview: str
    engagement: int
    topic: str


@dataclass
class ContactProfile:
    name: str
    title: str
    company: str
    location: str
    tenure: str
    previous_roles: list[dict]
    education: list[str]
    mutual_connections: list[str]
    recent_activity: list[dict]
    profile_url: str
    headline: str


def search_linkedin_profile(name: str = None, company: str = None, profile_url: str = None) -> dict:
    """
    Search for LinkedIn profile information.
    In production, would use LinkedIn API or Sales Navigator.
    """
    # TODO: Implement actual LinkedIn API integration
    # Note: LinkedIn's API has strict terms of service

    return {
        "found": False,
        "name": name or "Unknown",
        "title": "Unknown",
        "company": company or "Unknown",
        "location": "Unknown",
        "headline": "",
        "tenure": "Unknown",
        "profile_url": profile_url or ""
    }


def get_career_history(profile_data: dict) -> list[dict]:
    """
    Extract career history from profile.
    """
    # TODO: Implement career extraction
    return []


def get_mutual_connections(profile_data: dict, user_network: list = None) -> list[str]:
    """
    Find mutual connections.
    """
    # TODO: Implement mutual connection detection
    return []


def get_recent_activity(profile_data: dict, days: int = 30) -> list[dict]:
    """
    Get recent posts and activity.
    """
    # TODO: Implement activity extraction
    return []


def main(
    name: str = None,
    company: str = None,
    profile_url: str = None
) -> dict[str, Any]:
    """
    Structure LinkedIn profile data for sales prep.

    Args:
        name: Person's name
        company: Company they work at
        profile_url: Direct LinkedIn profile URL

    Returns:
        dict with structured contact profile
    """
    if not profile_url and not (name and company):
        return {"error": "Provide either profile_url or name + company"}

    # Search for profile
    profile_data = search_linkedin_profile(
        name=name,
        company=company,
        profile_url=profile_url
    )

    if not profile_data.get("found"):
        return {
            "found": False,
            "name": name or "Unknown",
            "company": company or "Unknown",
            "message": "Profile not found or limited access",
            "suggestions": [
                "Try searching directly on LinkedIn",
                "Check for alternative spellings",
                "Verify the person still works at this company"
            ]
        }

    # Get career history
    previous_roles = get_career_history(profile_data)

    # Find mutual connections
    mutual_connections = get_mutual_connections(profile_data)

    # Get recent activity
    recent_activity = get_recent_activity(profile_data)

    # Compile contact profile
    contact = ContactProfile(
        name=profile_data["name"],
        title=profile_data["title"],
        company=profile_data["company"],
        location=profile_data["location"],
        tenure=profile_data["tenure"],
        previous_roles=previous_roles,
        education=[],  # Would be extracted from profile
        mutual_connections=mutual_connections,
        recent_activity=recent_activity,
        profile_url=profile_data["profile_url"],
        headline=profile_data["headline"]
    )

    return {
        "found": True,
        "contact": asdict(contact),
        "research_date": datetime.now().isoformat(),
        "data_completeness": calculate_completeness(contact)
    }


def calculate_completeness(contact: ContactProfile) -> dict:
    """Calculate how complete the profile data is."""
    fields = {
        "basic_info": bool(contact.name and contact.title and contact.company),
        "career_history": len(contact.previous_roles) > 0,
        "mutual_connections": len(contact.mutual_connections) > 0,
        "recent_activity": len(contact.recent_activity) > 0,
        "education": len(contact.education) > 0
    }

    complete_count = sum(fields.values())
    return {
        "fields": fields,
        "score": f"{complete_count}/{len(fields)}",
        "percentage": int((complete_count / len(fields)) * 100)
    }


if __name__ == "__main__":
    import sys

    # Example usage
    result = main(
        name="Sarah Chen",
        company="DataFlow Systems"
    )
    print(json.dumps(result, indent=2))