"""
PolyCorr Backend Server
Flask API for handling Polymarket correlation analysis
"""

from flask import Flask, request, jsonify
from flask_cors import CORS
from dotenv import load_dotenv
import os
import sys

from polymarket_api import PolymarketAPI
from time_utils import TimeConverter
from llm_service import LLMService
from data_analysis import DataAnalyzer

# Load environment variables
load_dotenv()

app = Flask(__name__)

# Configure CORS to allow requests from frontend
# Allow production (Vercel), preview deployments, and local development
CORS(app, 
     origins=[
         "https://polycorr.vercel.app",
         r"https://.*\.vercel\.app",  # Allow all Vercel preview deployments
         "http://localhost:3000",
         "http://localhost:5173",  # Vite default port
         "http://127.0.0.1:3000",
         "http://127.0.0.1:5173"
     ],
     methods=["GET", "POST", "OPTIONS"],
     allow_headers=["Content-Type", "Authorization"],
     supports_credentials=True)

# Initialize API clients
polymarket_api = PolymarketAPI()
time_converter = TimeConverter()
data_analyzer = DataAnalyzer()

# LLM service will be initialized on first use to avoid startup errors
_llm_service = None

def get_llm_service():
    """Get or create LLM service instance (lazy initialization)"""
    global _llm_service
    if _llm_service is None:
        try:
            _llm_service = LLMService()
        except Exception as e:
            raise Exception(f"Failed to initialize LLM service: {str(e)}. Make sure OPENAI_API_KEY is set correctly.")
    return _llm_service


@app.route('/', methods=['GET'])
def root():
    """Root endpoint - API information"""
    return jsonify({
        "name": "PolyCorr Backend API",
        "status": "running",
        "version": "1.0.0",
        "endpoints": {
            "health": "/health",
            "market_info": "/api/market-info",
            "analyze_full": "/api/analyze-full",
            "add_asset": "/api/add-asset",
            "fetch_asset_prices": "/api/fetch-asset-prices",
            "analyze_correlations": "/api/analyze-correlations",
            "price_history": "/api/price-history",
            "time_conversion": "/api/time/est-to-unix, /api/time/unix-to-est"
        },
        "documentation": "See README.md for API documentation"
    }), 200


@app.route('/health', methods=['GET'])
def health_check():
    """Health check endpoint"""
    return jsonify({"status": "ok"}), 200


@app.route('/api/market-info', methods=['POST'])
def get_market_info():
    """
    Get market information from a Polymarket URL
    
    Request body:
        {
            "url": "https://polymarket.com/event/..."
        }
    
    Response:
        {
            "id": "...",
            "question": "...",
            "conditionId": "...",
            "clobTokenIds": [...],
            "firstTokenId": "..."
        }
    """
    try:
        data = request.get_json()
        url = data.get('url')
        
        if not url:
            return jsonify({"error": "URL is required"}), 400
        
        # Get market information
        market_info = polymarket_api.get_market_info(url)
        
        # Extract first token ID
        first_token_id = polymarket_api.get_first_token_id(market_info)
        
        response = {
            "id": market_info['id'],
            "question": market_info['question'],
            "conditionId": market_info['conditionId'],
            "clobTokenIds": market_info['clobTokenIds'],
            "firstTokenId": first_token_id
        }
        
        return jsonify(response), 200
        
    except ValueError as e:
        return jsonify({"error": str(e)}), 400
    except Exception as e:
        return jsonify({"error": f"Internal server error: {str(e)}"}), 500


@app.route('/api/time/est-to-unix', methods=['POST'])
def est_to_unix():
    """
    Convert EST datetime to Unix timestamp
    
    Request body:
        {
            "datetime": "YYYY-MM-DD HH:MM:SS" or "MM/DD/YYYY HH:MM:SS"
        }
    
    Response:
        {
            "unix_timestamp": 1234567890,
            "est_datetime": "YYYY-MM-DD HH:MM:SS EST"
        }
    """
    try:
        data = request.get_json()
        datetime_str = data.get('datetime')
        
        if not datetime_str:
            return jsonify({"error": "datetime is required"}), 400
        
        unix_timestamp = time_converter.est_to_unix(datetime_str)
        est_str = time_converter.unix_to_est(unix_timestamp)
        
        return jsonify({
            "unix_timestamp": unix_timestamp,
            "est_datetime": est_str
        }), 200
        
    except ValueError as e:
        return jsonify({"error": str(e)}), 400
    except Exception as e:
        return jsonify({"error": f"Internal server error: {str(e)}"}), 500


@app.route('/api/time/unix-to-est', methods=['POST'])
def unix_to_est():
    """
    Convert Unix timestamp to EST datetime
    
    Request body:
        {
            "timestamp": 1234567890
        }
    
    Response:
        {
            "unix_timestamp": 1234567890,
            "est_datetime": "YYYY-MM-DD HH:MM:SS EST"
        }
    """
    try:
        data = request.get_json()
        timestamp = data.get('timestamp')
        
        if timestamp is None:
            return jsonify({"error": "timestamp is required"}), 400
        
        est_str = time_converter.unix_to_est(int(timestamp))
        
        return jsonify({
            "unix_timestamp": timestamp,
            "est_datetime": est_str
        }), 200
        
    except ValueError as e:
        return jsonify({"error": str(e)}), 400
    except Exception as e:
        return jsonify({"error": f"Internal server error: {str(e)}"}), 500


@app.route('/api/price-history', methods=['POST'])
def get_price_history():
    """
    Get price history for a Polymarket token
    
    Request body:
        {
            "token_id": "52607315900507156846622820770453728082833251091510131025984187712529448877245",
            "start_time": "2025-11-15 10:00:00",  // EST datetime
            "end_time": "2025-11-15 18:00:00",    // EST datetime
            "fidelity": 1,  // minutes (optional, default: 1)
            "granularity": 1  // minutes (optional, default: 1)
        }
    
    Response:
        {
            "token_id": "...",
            "start_unix": 1234567890,
            "end_unix": 1234567900,
            "fidelity": 1,
            "raw_history": [...],  // sparse data from API
            "timeseries": [        // reconstructed complete timeseries
                {
                    "timestamp": 1234567890,
                    "price": 0.26
                },
                ...
            ]
        }
    """
    try:
        data = request.get_json()
        
        token_id = data.get('token_id')
        start_time_str = data.get('start_time')
        end_time_str = data.get('end_time')
        fidelity = data.get('fidelity', 1)
        granularity = data.get('granularity', 1)
        
        # Validate required fields
        if not token_id:
            return jsonify({"error": "token_id is required"}), 400
        if not start_time_str:
            return jsonify({"error": "start_time is required"}), 400
        if not end_time_str:
            return jsonify({"error": "end_time is required"}), 400
        
        # Convert times to Unix
        start_unix = time_converter.est_to_unix(start_time_str)
        end_unix = time_converter.est_to_unix(end_time_str)
        
        # Validate time range
        time_converter.validate_time_range(start_unix, end_unix)
        
        # Get price history from CLOB API
        raw_history = polymarket_api.get_price_history(
            token_id, 
            start_unix, 
            end_unix, 
            fidelity
        )
        
        # Reconstruct complete timeseries
        timeseries = polymarket_api.reconstruct_timeseries(
            raw_history,
            start_unix,
            end_unix,
            granularity
        )
        
        response = {
            "token_id": token_id,
            "start_unix": start_unix,
            "end_unix": end_unix,
            "start_time": time_converter.unix_to_est(start_unix),
            "end_time": time_converter.unix_to_est(end_unix),
            "fidelity": fidelity,
            "granularity": granularity,
            "raw_history_count": len(raw_history),
            "timeseries_count": len(timeseries),
            "raw_history": raw_history,
            "timeseries": timeseries
        }
        
        return jsonify(response), 200
        
    except ValueError as e:
        return jsonify({"error": str(e)}), 400
    except Exception as e:
        return jsonify({"error": f"Internal server error: {str(e)}"}), 500


@app.route('/api/analyze-correlations', methods=['POST'])
def analyze_correlations():
    """
    Get correlated and inversely correlated assets for a market question
    
    Request body:
        {
            "question": "Will the Supreme Court rule in favor of Trump's tariffs?"
        }
    
    Response:
        {
            "question": "...",
            "correlated": [
                {"ticker": "AAPL", "reason": "..."},
                ...
            ],
            "inversely_correlated": [
                {"ticker": "GLD", "reason": "..."},
                ...
            ]
        }
    """
    try:
        data = request.get_json()
        question = data.get('question')
        
        if not question:
            return jsonify({"error": "question is required"}), 400
        
        # Get correlated assets from LLM
        llm_service = get_llm_service()
        result = llm_service.get_correlated_assets(question)
        
        response = {
            "question": question,
            "correlated": result['correlated'],
            "inversely_correlated": result['inversely_correlated']
        }
        
        return jsonify(response), 200
        
    except ValueError as e:
        return jsonify({"error": str(e)}), 400
    except Exception as e:
        return jsonify({"error": f"Internal server error: {str(e)}"}), 500


@app.route('/api/fetch-asset-prices', methods=['POST'])
def fetch_asset_prices():
    """
    Fetch asset prices from Yahoo Finance
    
    Request body:
        {
            "ticker": "AAPL",
            "start_time": "2025-11-15 10:00:00",  // EST datetime
            "end_time": "2025-11-15 18:00:00",    // EST datetime
            "interval": "1h"  // optional: 1m, 5m, 15m, 30m, 1h, 1d
        }
    
    Response:
        {
            "ticker": "AAPL",
            "data_points": 150,
            "timeseries": [
                {"timestamp": 1234567890, "price": 150.25},
                ...
            ]
        }
    """
    try:
        data = request.get_json()
        
        ticker = data.get('ticker')
        start_time_str = data.get('start_time')
        end_time_str = data.get('end_time')
        interval = data.get('interval', '1h')
        
        if not ticker:
            return jsonify({"error": "ticker is required"}), 400
        if not start_time_str:
            return jsonify({"error": "start_time is required"}), 400
        if not end_time_str:
            return jsonify({"error": "end_time is required"}), 400
        
        # Convert times to Unix
        start_unix = time_converter.est_to_unix(start_time_str)
        end_unix = time_converter.est_to_unix(end_time_str)
        
        # Fetch asset prices
        timeseries = data_analyzer.fetch_asset_prices(
            ticker,
            start_unix,
            end_unix,
            interval
        )
        
        response = {
            "ticker": ticker,
            "start_time": start_time_str,
            "end_time": end_time_str,
            "interval": interval,
            "data_points": len(timeseries),
            "timeseries": timeseries
        }
        
        return jsonify(response), 200
        
    except ValueError as e:
        return jsonify({"error": str(e)}), 400
    except Exception as e:
        return jsonify({"error": f"Internal server error: {str(e)}"}), 500


@app.route('/api/analyze-full', methods=['POST'])
def analyze_full():
    """
    Complete analysis: Get Polymarket data, LLM correlations, and calculate actual correlations
    
    Request body:
        {
            "url": "https://polymarket.com/event/...",
            "start_time": "2025-11-15 10:00:00",
            "end_time": "2025-11-15 18:00:00",
            "granularity": 60  // minutes (optional, default: 60)
        }
    
    Response:
        {
            "market_info": {...},
            "polymarket_data": [...],
            "correlated_assets": [
                {
                    "ticker": "AAPL",
                    "reason": "...",
                    "correlation": 0.85,
                    "timeseries": [...],
                    "normalized_timeseries": [...]
                },
                ...
            ],
            "inversely_correlated_assets": [...]
        }
    """
    try:
        data = request.get_json()
        
        url = data.get('url')
        start_time_str = data.get('start_time')
        end_time_str = data.get('end_time')
        granularity = data.get('granularity', 60)
        
        # Validate required fields
        if not url:
            return jsonify({"error": "url is required"}), 400
        if not start_time_str:
            return jsonify({"error": "start_time is required"}), 400
        if not end_time_str:
            return jsonify({"error": "end_time is required"}), 400
        
        # Step 1: Get market info
        market_info = polymarket_api.get_market_info(url)
        token_id = polymarket_api.get_first_token_id(market_info)
        
        # Step 2: Convert times
        start_unix = time_converter.est_to_unix(start_time_str)
        end_unix = time_converter.est_to_unix(end_time_str)
        
        # Step 3: Get Polymarket price history
        raw_history = polymarket_api.get_price_history(
            token_id,
            start_unix,
            end_unix,
            fidelity=granularity
        )
        
        polymarket_timeseries = polymarket_api.reconstruct_timeseries(
            raw_history,
            start_unix,
            end_unix,
            granularity
        )
        
        # Step 4: Get correlated assets from LLM
        llm_service = get_llm_service()
        llm_result = llm_service.get_correlated_assets(market_info['question'])
        
        # Step 5: Analyze correlated assets
        # Use appropriate interval based on user's granularity choice
        if granularity >= 60:
            interval = "1h"
        else:
            interval = "5m"
        
        correlated_results = data_analyzer.analyze_multiple_assets(
            polymarket_timeseries,
            llm_result['correlated'],
            start_unix,
            end_unix,
            interval
        )
        
        inversely_correlated_results = data_analyzer.analyze_multiple_assets(
            polymarket_timeseries,
            llm_result['inversely_correlated'],
            start_unix,
            end_unix,
            interval
        )
        
        # Normalize Polymarket data
        normalized_polymarket = data_analyzer.normalize_timeseries(
            polymarket_timeseries,
            "min-max"
        )
        
        response = {
            "market_info": {
                "id": market_info['id'],
                "question": market_info['question'],
                "token_id": token_id
            },
            "time_range": {
                "start": start_time_str,
                "end": end_time_str,
                "start_unix": start_unix,
                "end_unix": end_unix,
                "granularity": granularity
            },
            "polymarket_data": polymarket_timeseries,
            "polymarket_data_normalized": normalized_polymarket,
            "correlated_assets": correlated_results,
            "inversely_correlated_assets": inversely_correlated_results
        }
        
        return jsonify(response), 200
        
    except ValueError as e:
        return jsonify({"error": str(e)}), 400
    except Exception as e:
        return jsonify({"error": f"Internal server error: {str(e)}"}), 500


@app.route('/api/add-asset', methods=['POST'])
def add_asset():
    """
    Add a custom asset to the analysis after initial load.

    Request body:
        {
            "token_id": "...",
            "start_time": "2025-11-10 10:00:00",
            "end_time": "2025-11-14 18:00:00",
            "granularity": 60,
            "ticker": "NVDA",
            "category": "correlated" | "inverse",
            "reason": "User provided reason"
        }

    Response:
        {
            "ticker": "...",
            "reason": "...",
            "correlation": 0.45,
            "data_points": 120,
            "timeseries": [...],
            "normalized_timeseries": [...],
            "aligned_timeseries": [...],
            "aligned_normalized_timeseries": [...],
            "success": true,
            "category": "correlated"
        }
    """
    try:
        data = request.get_json()

        token_id = data.get('token_id')
        start_time_str = data.get('start_time')
        end_time_str = data.get('end_time')
        granularity = data.get('granularity', 60)
        ticker = data.get('ticker')
        category = data.get('category', 'correlated')
        reason = data.get('reason', '')

        if not token_id:
            return jsonify({"error": "token_id is required"}), 400
        if not ticker:
            return jsonify({"error": "ticker is required"}), 400
        if not start_time_str or not end_time_str:
            return jsonify({"error": "start_time and end_time are required"}), 400

        # Convert times
        start_unix = time_converter.est_to_unix(start_time_str)
        end_unix = time_converter.est_to_unix(end_time_str)
        time_converter.validate_time_range(start_unix, end_unix)

        # Fetch Polymarket data
        raw_history = polymarket_api.get_price_history(
            token_id,
            start_unix,
            end_unix,
            granularity
        )

        polymarket_timeseries = polymarket_api.reconstruct_timeseries(
            raw_history,
            start_unix,
            end_unix,
            granularity
        )

        interval = "1h" if granularity >= 60 else "5m"

        assets = [{"ticker": ticker, "reason": reason}]
        results = data_analyzer.analyze_multiple_assets(
            polymarket_timeseries,
            assets,
            start_unix,
            end_unix,
            interval
        )

        if not results:
            return jsonify({"error": "Could not analyze asset"}), 500

        asset_result = results[0]
        asset_result["category"] = category

        return jsonify(asset_result), 200

    except ValueError as e:
        return jsonify({"error": str(e)}), 400
    except Exception as e:
        return jsonify({"error": f"Internal server error: {str(e)}"}), 500


# For local development, run the server
# For HuggingFace Spaces, Gunicorn will handle the server (see Dockerfile)
if __name__ == '__main__':
    port = int(os.environ.get('PORT', 5000))
    app.run(debug=True, host='0.0.0.0', port=port)