"""
Purpose: Web research agent template for searching, extracting, and analyzing web data
LLM-Note:
  Dependencies: imports from [os, json, requests, connectonion.Agent, connectonion.llm_do] | template file copied by [cli/commands/init.py, cli/commands/create.py]
  Data flow: user query → Agent.input() → search_web (placeholder) | extract_data fetches URL → returns content preview | analyze_data uses llm_do | save_research writes JSON file
  State/Effects: HTTP requests to external URLs | writes research JSON files | uses MODEL env var or co/gemini-2.5-pro
  Integration: template for 'co create --template web-research' | tools: search_web, extract_data, analyze_data, save_research | extensible for real search APIs
  Performance: HTTP timeout 10s | analysis truncates to 1000 chars for llm_do
  Errors: request exceptions caught and returned | ⚠️ TODO: search_web is placeholder, needs real API integration

Web research agent with data extraction capabilities.
"""

import os
import json
import requests
from typing import Dict, List, Any
from connectonion import Agent, llm_do


def search_web(query: str) -> str:
    """Search the web for information.
    
    Args:
        query: Search query
        
    Returns:
        Search results summary
    """
    # This is a placeholder - you would integrate with a real search API
    return f"Searching for: {query}\n\nResults:\n1. Example result about {query}\n2. Another relevant finding"


def extract_data(url: str, data_type: str = "text") -> Dict[str, Any]:
    """Extract data from a webpage.
    
    Args:
        url: URL to extract data from
        data_type: Type of data to extract (text, links, images)
        
    Returns:
        Extracted data dictionary
    """
    try:
        response = requests.get(url, timeout=10)
        response.raise_for_status()
        
        # Simple extraction logic - expand as needed
        if data_type == "text":
            # In production, use BeautifulSoup or similar
            return {
                "url": url,
                "status": response.status_code,
                "content_length": len(response.text),
                "preview": response.text[:500]
            }
        else:
            return {"url": url, "data_type": data_type, "note": "Extraction not implemented"}
            
    except Exception as e:
        return {"error": str(e), "url": url}


def analyze_data(data: str, analysis_type: str = "summary") -> str:
    """Analyze extracted data.
    
    Args:
        data: Data to analyze
        analysis_type: Type of analysis (summary, sentiment, keywords)
        
    Returns:
        Analysis results
    """
    # Use LLM for analysis
    prompt = f"Perform {analysis_type} analysis on this data: {data[:1000]}"
    return llm_do(prompt)


def save_research(topic: str, findings: List[str], filename: str = None) -> str:
    """Save research findings to a file.
    
    Args:
        topic: Research topic
        findings: List of findings
        filename: Output filename (optional)
        
    Returns:
        Confirmation message
    """
    if not filename:
        filename = f"research_{topic.replace(' ', '_')}.json"
    
    research_data = {
        "topic": topic,
        "findings": findings,
        "timestamp": __import__('datetime').datetime.now().isoformat()
    }
    
    with open(filename, 'w') as f:
        json.dump(research_data, f, indent=2)
    
    return f"Research saved to {filename}"


def main():
    """Run the web research agent."""
    # Create agent with web research tools
    agent = Agent(
        name="web-research-agent",
        tools=[search_web, extract_data, analyze_data, save_research],
        model=os.getenv("MODEL", "co/gemini-2.5-pro")
    )
    
    # Example research task
    response = agent.input(
        "Research the latest trends in AI and summarize the key findings"
    )
    print(response)


if __name__ == "__main__":
    main()