services.py

import pandas as pd
import numpy as np
from fastapi import HTTPException
from models.train_model import (
    load_and_preprocess_data, train_team_performance_model, train_player_score_model,
    predict_player_score, predict_team_performance
)
from groq import Groq

# Global variables to store models and data
TEAM_WIN_MODEL = None
TEAM_SCORE_MODEL = None
TEAM_DATA = None
TEAM_SCALER = None
PLAYER_SCORE_MODEL = None
PLAYER_SCALER = None
PLAYER_DATA = None
MATCH_DF = None
BALL_DF = None

# Initialize Groq client
GROQ_API_KEY = "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"
client = Groq(api_key=GROQ_API_KEY)

# Load data and train models at startup
def initialize_models():
    global TEAM_WIN_MODEL, TEAM_SCORE_MODEL, TEAM_DATA, TEAM_SCALER
    global PLAYER_SCORE_MODEL, PLAYER_SCALER, PLAYER_DATA, MATCH_DF, BALL_DF
    
    MATCH_DF, BALL_DF = load_and_preprocess_data()
    TEAM_WIN_MODEL, TEAM_SCORE_MODEL, TEAM_DATA, TEAM_SCALER = train_team_performance_model(MATCH_DF)
    PLAYER_SCORE_MODEL, PLAYER_SCALER, PLAYER_DATA = train_player_score_model(MATCH_DF, BALL_DF)
    print("Models trained and loaded into memory.")

# Call this at app startup (see main.py below)
initialize_models()

# Player-team mapping
player_team_mapping = BALL_DF.groupby('striker')['batting_team'].agg(lambda x: x.mode()[0] if len(x.mode()) > 0 else None).to_dict()

# Clean JSON data (unchanged)
def clean_json(data):
    if isinstance(data, dict):
        return {k: clean_json(v) for k, v in data.items()}
    elif isinstance(data, list):
        return [clean_json(v) for v in data]
    elif isinstance(data, float):
        return 0.0 if pd.isna(data) or np.isinf(data) else data
    elif pd.isna(data):
        return None
    elif isinstance(data, pd.Timestamp):
        return data.strftime('%Y-%m-%d') if pd.notna(data) else None
    elif isinstance(data, (int, bool)):
        return data
    return str(data)

# Summary generation (unchanged)
def generate_summary(data, context_type):
    prompt = ""
    if context_type == "player_stats":
        prompt = f"Summarize this player data in one sentence: {data}"
    elif context_type == "team_stats":
        prompt = f"Summarize this team data in one sentence: {data}"
    elif context_type == "match_history":
        prompt = f"Summarize this match history between {data['team1']} and {data['team2']} in one sentence: {data['matches']}"
    elif context_type == "prediction_score":
        prompt = f"Summarize this prediction in one sentence: {data}"
    elif context_type == "prediction_team":
        prompt = f"Summarize this team prediction in one sentence: {data}"

    try:
        chat_completion = client.chat.completions.create(
            model="mixtral-8x7b-32768",
            messages=[
                {"role": "system", "content": "You are a concise cricket analyst."},
                {"role": "user", "content": prompt}
            ],
            max_tokens=50,
            temperature=0.7
        )
        return chat_completion.choices[0].message.content.strip()
    except Exception as e:
        return f"Summary unavailable due to error: {str(e)}"

# Player stats (unchanged except using global BALL_DF)
def get_player_stats(player_name: str, season: str = None, role: str = "Batting"):
    player_name = player_name.strip().title()
    name_variations = [player_name, player_name.replace(" ", ""), " ".join(reversed(player_name.split()))]
    player_data = BALL_DF[BALL_DF['striker'].isin(name_variations) | BALL_DF['bowler'].isin(name_variations)]
    if season and 'season' in BALL_DF.columns:
        player_data = player_data[player_data['season'] == season]
    if player_data.empty:
        raise HTTPException(status_code=404, detail=f"Player '{player_name}' not found. Variations tried: {name_variations}")

    if role == "Batting":
        batting_data = player_data[player_data['striker'].isin(name_variations)]
        total_runs = int(batting_data['runs_off_bat'].sum())
        balls_faced = int(batting_data.shape[0])
        strike_rate = float((total_runs / balls_faced * 100) if balls_faced > 0 else 0)
        matches_played = int(len(batting_data['match_id'].unique()))

        stats = {
            "player_name": player_name,
            "role": role,
            "total_runs": total_runs,
            "balls_faced": balls_faced,
            "strike_rate": strike_rate,
            "matches_played": matches_played,
            "season": season if season else "All Seasons"
        }
        stats["summary"] = generate_summary(stats, "player_stats")
        return clean_json(stats)

    elif role == "Bowling":
        bowling_data = player_data[player_data['bowler'].isin(name_variations)]
        bowler_wicket_types = ["caught", "bowled", "lbw", "caught and bowled", "hit wicket"]
        wickets_data = bowling_data[bowling_data['player_dismissed'].notna() & 
                                   bowling_data['wicket_type'].isin(bowler_wicket_types)]
        total_wickets = int(wickets_data.shape[0])
        total_runs_conceded = int(bowling_data['total_runs'].sum())
        total_balls_bowled = int(bowling_data.shape[0])
        total_overs_bowled = float(total_balls_bowled / 6)
        bowling_average = float(total_runs_conceded / total_wickets) if total_wickets > 0 else float('inf')
        economy_rate = float(total_runs_conceded / total_overs_bowled) if total_overs_bowled > 0 else 0
        bowling_strike_rate = float(total_balls_bowled / total_wickets) if total_wickets > 0 else float('inf')
        bowling_matches = int(len(bowling_data['match_id'].unique()))

        stats = {
            "player_name": player_name,
            "role": role,
            "total_wickets": total_wickets,
            "bowling_average": 0.0 if np.isinf(bowling_average) else round(bowling_average, 2),
            "economy_rate": round(economy_rate, 2),
            "bowling_strike_rate": 0.0 if np.isinf(bowling_strike_rate) else round(bowling_strike_rate, 2),
            "overs_bowled": round(total_overs_bowled, 1),
            "bowling_matches": bowling_matches,
            "season": season if season else "All Seasons"
        }
        stats["summary"] = generate_summary(stats, "player_stats")
        return clean_json(stats)

# Team stats (unchanged except using global MATCH_DF)
def get_team_stats(team_name: str, season: str = None):
    team_name = team_name.strip().title()
    team_matches = MATCH_DF[(MATCH_DF['team1'] == team_name) | (MATCH_DF['team2'] == team_name)]
    if season and 'season' in MATCH_DF.columns:
        team_matches = team_matches[team_matches['season'] == season]
    if team_matches.empty:
        raise HTTPException(status_code=404, detail="Team not found")

    wins = int(team_matches[team_matches['winner'] == team_name].shape[0])
    total_matches = int(team_matches.shape[0])

    stats = {
        "total_matches": total_matches,
        "wins": wins,
        "losses": total_matches - wins,
        "win_percentage": float((wins / total_matches * 100) if total_matches > 0 else 0),
        "season": season if season else "All Seasons"
    }
    stats["summary"] = generate_summary(stats, "team_stats")
    return clean_json(stats)

# Match history (unchanged except using global MATCH_DF)
def get_match_history(team1: str, team2: str, season: str = None):
    team1 = team1.strip().title()
    team2 = team2.strip().title()
    available_teams = set(MATCH_DF['team1'].unique().tolist() + MATCH_DF['team2'].unique().tolist())
    if team1 not in available_teams or team2 not in available_teams:
        raise HTTPException(status_code=404, detail=f"Team {team1 if team1 not in available_teams else team2} not found.")

    team_matches = MATCH_DF[
        ((MATCH_DF['team1'] == team1) & (MATCH_DF['team2'] == team2)) |
        ((MATCH_DF['team1'] == team2) & (MATCH_DF['team2'] == team1))
    ].copy()
    if season and 'season' in MATCH_DF.columns:
        team_matches = team_matches[team_matches['season'] == season]
    if team_matches.empty:
        raise HTTPException(status_code=404, detail=f"No match history found between {team1} and {team2}.")
    
    team_matches['date'] = team_matches['date'].apply(lambda x: x.strftime('%Y-%m-%d') if pd.notna(x) else None)
    team_matches['winner'] = team_matches['winner'].fillna("Draw")
    for column in ['team1', 'team2', 'winner']:
        team_matches[column] = team_matches[column].apply(lambda x: str(x) if pd.notna(x) else None)
    history = team_matches[['date', 'team1', 'team2', 'winner']].to_dict(orient='records')

    response = {
        "team1": team1,
        "team2": team2,
        "season": season if season else "All Seasons",
        "matches": history
    }
    response["summary"] = generate_summary(response, "match_history")
    return clean_json(response)

# Prediction functions using in-memory models
def predict_score(player_name: str, opposition_team: str):
    try:
        player_name = player_name.strip().replace("+", " ").title()
        name_variations = [player_name, player_name.replace(" ", ""), " ".join(reversed(player_name.split()))]
        player_team = None
        for name in name_variations:
            if name in player_team_mapping:
                player_team = player_team_mapping[name]
                player_name = name
                break
        if not player_team:
            raise ValueError(f"Player {player_name} not found in historical data")

        predicted_runs = predict_player_score(
            player=player_name,
            team=player_team,
            opponent=opposition_team,
            venue=None,
            city=None,
            toss_winner=None,
            toss_decision=None,
            score_model=PLAYER_SCORE_MODEL,
            scaler=PLAYER_SCALER,
            player_data=PLAYER_DATA
        )
        stats = {
            "player": player_name,
            "team": player_team,
            "opposition": opposition_team,
            "predicted_runs": predicted_runs["expected_score"]
        }
        stats["summary"] = generate_summary(stats, "prediction_score")
        return clean_json(stats)
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error predicting score for {player_name} against {opposition_team}: {str(e)}")

def predict_team_outcome(team1: str, team2: str):
    prediction = predict_team_performance(
        team1=team1,
        team2=team2,
        venue=None,
        city=None,
        toss_winner=None,
        toss_decision=None,
        win_model=TEAM_WIN_MODEL,
        score_model=TEAM_SCORE_MODEL,
        data=TEAM_DATA,
        scaler=TEAM_SCALER
    )
    prediction["summary"] = generate_summary(prediction, "prediction_team")
    return clean_json(prediction)

# Utility functions (unchanged except using global dataframes)
def get_teams():
    return clean_json({"teams": sorted(set(MATCH_DF['team1'].unique().tolist() + MATCH_DF['team2'].unique().tolist()))})

def get_players():
    unique_players = sorted(set(BALL_DF['striker'].dropna().unique().tolist()))
    return clean_json({"players": unique_players})

def get_seasons():
    return clean_json({"seasons": ["All Seasons"] + sorted(MATCH_DF['season'].dropna().unique().tolist())})

# Team trends (unchanged except using global MATCH_DF)
def get_team_trends(team_name: str):
    team_name = team_name.strip().title()
    team_matches = MATCH_DF[(MATCH_DF['team1'] == team_name) | (MATCH_DF['team2'] == team_name)]
    if team_matches.empty:
        raise HTTPException(status_code=404, detail="Team not found")

    trends = []
    for season in MATCH_DF['season'].unique():
        season_matches = team_matches[team_matches['season'] == season]
        if not season_matches.empty:
            wins = season_matches[season_matches['winner'] == team_name].shape[0]
            total_matches = season_matches.shape[0]
            win_percentage = (wins / total_matches * 100) if total_matches > 0 else 0
            trends.append({
                "season": season,
                "wins": wins,
                "total_matches": total_matches,
                "win_percentage": win_percentage
            })

    return {"team_name": team_name, "trends": trends}

# Player trends (unchanged except using global BALL_DF)
def get_player_trends(player_name: str, role: str = "Batting"):
    player_name = player_name.strip().title()
    name_variations = [player_name, player_name.replace(" ", ""), " ".join(reversed(player_name.split()))]
    player_data = BALL_DF[BALL_DF['striker'].isin(name_variations) | BALL_DF['bowler'].isin(name_variations)]
    if player_data.empty:
        raise HTTPException(status_code=404, detail=f"Player '{player_name}' not found")

    trends = []
    for season in BALL_DF['season'].unique():
        season_data = player_data[player_data['season'] == season]
        if not season_data.empty:
            if role == "Batting":
                total_runs = int(season_data['runs_off_bat'].sum())
                balls_faced = int(season_data.shape[0])
                strike_rate = float((total_runs / balls_faced * 100) if balls_faced > 0 else 0)
                matches_played = int(len(season_data['match_id'].unique()))
                trends.append({
                    "season": season,
                    "total_runs": total_runs,
                    "strike_rate": strike_rate,
                    "matches_played": matches_played
                })
            elif role == "Bowling":
                total_wickets = int(season_data[season_data['wicket_type'].notna()].shape[0])
                total_runs_conceded = int(season_data['total_runs'].sum())
                total_overs_bowled = float(season_data.shape[0] / 6)
                bowling_average = float(total_runs_conceded / total_wickets) if total_wickets > 0 else float('inf')
                economy_rate = float(total_runs_conceded / total_overs_bowled) if total_overs_bowled > 0 else 0
                matches_played = int(len(season_data['match_id'].unique()))
                trends.append({
                    "season": season,
                    "total_wickets": total_wickets,
                    "bowling_average": bowling_average,
                    "economy  economy_rate": economy_rate,
                    "matches_played": matches_played
                })

    return {"player_name": player_name, "role": role, "trends": trends}