import json
import os
import re
import time
import shutil
from typing import Dict, List, Any
from fastapi.responses import JSONResponse, FileResponse
from gpt_researcher.document.document import DocumentLoader
from backend.utils import write_md_to_pdf, write_md_to_word, write_text_to_md
from pathlib import Path
from datetime import datetime
from fastapi import HTTPException
import logging

logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)

class CustomLogsHandler:
    """Custom handler to capture streaming logs from the research process"""
    def __init__(self, websocket, task: str):
        self.logs = []
        self.websocket = websocket
        sanitized_filename = sanitize_filename(f"task_{int(time.time())}_{task}")
        self.log_file = os.path.join("outputs", f"{sanitized_filename}.json")
        self.timestamp = datetime.now().isoformat()
        # Initialize log file with metadata
        os.makedirs("outputs", exist_ok=True)
        with open(self.log_file, 'w') as f:
            json.dump({
                "timestamp": self.timestamp,
                "events": [],
                "content": {
                    "query": "",
                    "sources": [],
                    "context": [],
                    "report": "",
                    "costs": 0.0
                }
            }, f, indent=2)

    async def send_json(self, data: Dict[str, Any]) -> None:
        """Store log data and send to websocket"""
        # Send to websocket for real-time display
        if self.websocket:
            await self.websocket.send_json(data)
            
        # Read current log file
        with open(self.log_file, 'r') as f:
            log_data = json.load(f)
            
        # Update appropriate section based on data type
        if data.get('type') == 'logs':
            log_data['events'].append({
                "timestamp": datetime.now().isoformat(),
                "type": "event",
                "data": data
            })
        else:
            # Update content section for other types of data
            log_data['content'].update(data)
            
        # Save updated log file
        with open(self.log_file, 'w') as f:
            json.dump(log_data, f, indent=2)
        logger.debug(f"Log entry written to: {self.log_file}")


class Researcher:
    def __init__(self, query: str, report_type: str = "research_report"):
        self.query = query
        self.report_type = report_type
        # Generate unique ID for this research task
        self.research_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{hash(query)}"
        # Initialize logs handler with research ID
        self.logs_handler = CustomLogsHandler(self.research_id)
        self.researcher = GPTResearcher(
            query=query,
            report_type=report_type,
            websocket=self.logs_handler
        )

    async def research(self) -> dict:
        """Conduct research and return paths to generated files"""
        await self.researcher.conduct_research()
        report = await self.researcher.write_report()
        
        # Generate the files
        sanitized_filename = sanitize_filename(f"task_{int(time.time())}_{self.query}")
        file_paths = await generate_report_files(report, sanitized_filename)
        
        # Get the JSON log path that was created by CustomLogsHandler
        json_relative_path = os.path.relpath(self.logs_handler.log_file)
        
        return {
            "output": {
                **file_paths,  # Include PDF, DOCX, and MD paths
                "json": json_relative_path
            }
        }

def sanitize_filename(filename: str) -> str:
    # Split into components
    prefix, timestamp, *task_parts = filename.split('_')
    task = '_'.join(task_parts)
    
    # Calculate max length for task portion
    # 255 - len("outputs/") - len("task_") - len(timestamp) - len("_.json") - safety_margin
    max_task_length = 255 - 8 - 5 - 10 - 6 - 10  # ~216 chars for task
    
    # Truncate task if needed
    truncated_task = task[:max_task_length] if len(task) > max_task_length else task
    
    # Reassemble and clean the filename
    sanitized = f"{prefix}_{timestamp}_{truncated_task}"
    return re.sub(r"[^\w\s-]", "", sanitized).strip()


async def handle_start_command(websocket, data: str, manager):
    json_data = json.loads(data[6:])
    task, report_type, source_urls, document_urls, tone, headers, report_source = extract_command_data(
        json_data)

    if not task or not report_type:
        print("Error: Missing task or report_type")
        return

    # Create logs handler with websocket and task
    logs_handler = CustomLogsHandler(websocket, task)
    # Initialize log content with query
    await logs_handler.send_json({
        "query": task,
        "sources": [],
        "context": [],
        "report": ""
    })

    sanitized_filename = sanitize_filename(f"task_{int(time.time())}_{task}")

    report = await manager.start_streaming(
        task, 
        report_type, 
        report_source, 
        source_urls, 
        document_urls,
        tone, 
        websocket,
        headers
    )
    report = str(report)
    file_paths = await generate_report_files(report, sanitized_filename)
    # Add JSON log path to file_paths
    file_paths["json"] = os.path.relpath(logs_handler.log_file)
    await send_file_paths(websocket, file_paths)


async def handle_human_feedback(data: str):
    feedback_data = json.loads(data[14:])  # Remove "human_feedback" prefix
    print(f"Received human feedback: {feedback_data}")
    # TODO: Add logic to forward the feedback to the appropriate agent or update the research state

async def handle_chat(websocket, data: str, manager):
    json_data = json.loads(data[4:])
    print(f"Received chat message: {json_data.get('message')}")
    await manager.chat(json_data.get("message"), websocket)

async def generate_report_files(report: str, filename: str) -> Dict[str, str]:
    pdf_path = await write_md_to_pdf(report, filename)
    docx_path = await write_md_to_word(report, filename)
    md_path = await write_text_to_md(report, filename)
    return {"pdf": pdf_path, "docx": docx_path, "md": md_path}


async def send_file_paths(websocket, file_paths: Dict[str, str]):
    await websocket.send_json({"type": "path", "output": file_paths})


def get_config_dict(
    langchain_api_key: str, openai_api_key: str, tavily_api_key: str,
    google_api_key: str, google_cx_key: str, bing_api_key: str,
    searchapi_api_key: str, serpapi_api_key: str, serper_api_key: str, searx_url: str
) -> Dict[str, str]:
    return {
        "LANGCHAIN_API_KEY": langchain_api_key or os.getenv("LANGCHAIN_API_KEY", ""),
        "OPENAI_API_KEY": openai_api_key or os.getenv("OPENAI_API_KEY", ""),
        "TAVILY_API_KEY": tavily_api_key or os.getenv("TAVILY_API_KEY", ""),
        "GOOGLE_API_KEY": google_api_key or os.getenv("GOOGLE_API_KEY", ""),
        "GOOGLE_CX_KEY": google_cx_key or os.getenv("GOOGLE_CX_KEY", ""),
        "BING_API_KEY": bing_api_key or os.getenv("BING_API_KEY", ""),
        "SEARCHAPI_API_KEY": searchapi_api_key or os.getenv("SEARCHAPI_API_KEY", ""),
        "SERPAPI_API_KEY": serpapi_api_key or os.getenv("SERPAPI_API_KEY", ""),
        "SERPER_API_KEY": serper_api_key or os.getenv("SERPER_API_KEY", ""),
        "SEARX_URL": searx_url or os.getenv("SEARX_URL", ""),
        "LANGCHAIN_TRACING_V2": os.getenv("LANGCHAIN_TRACING_V2", "true"),
        "DOC_PATH": os.getenv("DOC_PATH", "./my-docs"),
        "RETRIEVER": os.getenv("RETRIEVER", ""),
        "EMBEDDING_MODEL": os.getenv("OPENAI_EMBEDDING_MODEL", "")
    }


def update_environment_variables(config: Dict[str, str]):
    for key, value in config.items():
        os.environ[key] = value


async def handle_file_upload(file, DOC_PATH: str) -> Dict[str, str]:
    file_path = os.path.join(DOC_PATH, os.path.basename(file.filename))
    with open(file_path, "wb") as buffer:
        shutil.copyfileobj(file.file, buffer)
    print(f"File uploaded to {file_path}")

    document_loader = DocumentLoader(DOC_PATH)
    await document_loader.load()

    return {"filename": file.filename, "path": file_path}


async def handle_file_deletion(filename: str, DOC_PATH: str) -> JSONResponse:
    file_path = os.path.join(DOC_PATH, os.path.basename(filename))
    if os.path.exists(file_path):
        os.remove(file_path)
        print(f"File deleted: {file_path}")
        return JSONResponse(content={"message": "File deleted successfully"})
    else:
        print(f"File not found: {file_path}")
        return JSONResponse(status_code=404, content={"message": "File not found"})


async def execute_multi_agents(manager) -> Any:
    websocket = manager.active_connections[0] if manager.active_connections else None
    if websocket:
        report = await run_research_task("Is AI in a hype cycle?", websocket, stream_output)
        return {"report": report}
    else:
        return JSONResponse(status_code=400, content={"message": "No active WebSocket connection"})


async def handle_websocket_communication(websocket, manager):
    while True:
        data = await websocket.receive_text()
        if data.startswith("start"):
            await handle_start_command(websocket, data, manager)
        elif data.startswith("human_feedback"):
            await handle_human_feedback(data)
        elif data.startswith("chat"):
            await handle_chat(websocket, data, manager)
        else:
            print("Error: Unknown command or not enough parameters provided.")


def extract_command_data(json_data: Dict) -> tuple:
    return (
        json_data.get("task"),
        json_data.get("report_type"),
        json_data.get("source_urls"),
        json_data.get("document_urls"),
        json_data.get("tone"),
        json_data.get("headers", {}),
        json_data.get("report_source")
    )