model.py

"""
model.py  –  StockBuddy ML / NLP core
========================================
LIGHTWEIGHT CHANGES vs original:
  [OPT-1]  Removed `transformers` pipeline (was downloading ~1.2 GB BART model at
           runtime).  Replaced with a fast NLTK-based extractive summariser.
  [OPT-2]  Reduced technical indicators: 11 → 6 features (kept only the ones with
           highest predictive signal; fewer features = smaller tensors & faster fits).
  [OPT-3]  LSTM architecture: 4 layers (64/64/32/32 units) → 2 layers (32/16 units).
           Still accurate enough for short-horizon forecasts, ~8× fewer parameters.
  [OPT-4]  time_step: 45 → 30  (shorter look-back window → smaller tensors).
  [OPT-5]  Epochs: 30 → 15,  batch_size: 64 → 32 (free-tier CPU training time).
  [OPT-6]  XGBoost n_estimators: 300 → 100, max_depth 6 → 4.
  [OPT-7]  EarlyStopping patience reduced (5 instead of 10) so training exits fast
           when the model has converged.
  All public function signatures are identical to the original so app.py needs
  only minimal changes.
"""

import numpy as np
import pandas as pd
import requests
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout
import xgboost as xgb
import plotly.graph_objects as go
from datetime import datetime, timedelta
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
# [OPT-1] No longer importing transformers – see generate_sentiment_summary below
import time
import os

# Download VADER lexicon once (tiny file, safe on free tier)
nltk.download("vader_lexicon", quiet=True)

# =============================================================================
#                         API Keys (Replace with your own keys)
# =============================================================================
ALPHAVANTAGE_API_KEY = os.environ.get("ALPHAVANTAGE_API_KEY")
FINNHUB_API_KEY      = os.environ.get("FINNHUB_API_KEY")
# =============================================================================
#                     STOCK PRICE PREDICTION FUNCTIONS
# =============================================================================

def fetch_stock_data(symbol, outputsize="full"):
    url = "https://www.alphavantage.co/query"
    params = {
        "function":   "TIME_SERIES_DAILY",
        "symbol":     symbol,
        "apikey":     ALPHAVANTAGE_API_KEY,
        "outputsize": outputsize,
        "datatype":   "json",
    }
    response = requests.get(url, params=params)
    data = response.json()

    if "Time Series (Daily)" not in data:
        if "Error Message" in data:
            raise ValueError(
                f"Symbol '{symbol}' not found. Please verify the stock symbol.")
        elif "Note" in data:
            raise ValueError("API request limit reached. Please try again in a minute.")
        else:
            raise ValueError(
                f"Unable to fetch data for symbol '{symbol}'. Please verify the symbol.")

    ts = data["Time Series (Daily)"]

    df = pd.DataFrame.from_dict(ts, orient="index")
    df.index = pd.to_datetime(df.index)
    df.sort_index(inplace=True)

    for col in ["1. open", "2. high", "3. low", "4. close", "5. volume"]:
        if col in df.columns:
            df[col] = df[col].astype(float)

    df = df.rename(columns={
        "1. open":   "Open",
        "2. high":   "High",
        "3. low":    "Low",
        "4. close":  "Close",
        "5. volume": "Volume",
    })

    latest_date    = df.index[-1]
    today          = pd.Timestamp.now().normalize()
    market_closed_days = 0
    if today.dayofweek >= 5:
        market_closed_days = today.dayofweek - 4
    elif today.hour < 16:
        market_closed_days = 1
    expected_latest = today - pd.Timedelta(days=market_closed_days)
    date_diff = (expected_latest - latest_date).days
    if date_diff > 5:
        print(f"WARNING: Latest data for {symbol} is from "
              f"{latest_date.strftime('%Y-%m-%d')} ({date_diff} days old).")

    print(f"\nLatest closing price for {symbol} "
          f"(as of {latest_date.strftime('%Y-%m-%d')}): ${df['Close'].iloc[-1]:.2f}")

    # Add lightweight technical indicators
    df = add_technical_indicators(df)
    return df


# [OPT-2] Reduced feature set: 11 → 6  (Close, RSI, SMA5, MACD, Upper_Band, ROC)
def add_technical_indicators(df):
    """Add a compact set of technical indicators (6 features vs 11 original)."""
    try:
        required_cols = ["Close", "Open", "High", "Low"]
        for col in required_cols:
            if col not in df.columns:
                print(f"Warning: {col} missing – falling back to Close-only.")
                return df[["Close"]]

        # RSI (14-period)
        delta = df["Close"].diff()
        gain  = delta.where(delta > 0, 0).rolling(14).mean()
        loss  = -delta.where(delta < 0, 0).rolling(14).mean()
        rs    = gain / loss
        df["RSI"] = 100 - (100 / (1 + rs))

        # Short moving average
        df["SMA5"] = df["Close"].rolling(5).mean()

        # MACD line only (signal line dropped to save a feature)
        ema12       = df["Close"].ewm(span=12).mean()
        ema26       = df["Close"].ewm(span=26).mean()
        df["MACD"]  = ema12 - ema26

        # Upper Bollinger Band as a proxy for volatility
        ma20              = df["Close"].rolling(20).mean()
        df["Upper_Band"]  = ma20 + (df["Close"].rolling(20).std() * 2)

        # Rate-of-change (5-period)
        df["ROC"] = df["Close"].pct_change(periods=5) * 100

        df = df.dropna()

        # [OPT-2] Only 6 features returned
        features = ["Close", "RSI", "SMA5", "MACD", "Upper_Band", "ROC"]
        return df[features]

    except Exception as e:
        print(f"Error adding technical indicators: {e}")
        if "Close" in df.columns:
            return df[["Close"]]
        return df


def preprocess_data(data):
    """Scale each feature independently; return scaled array + Close scaler."""
    features    = data.columns
    scalers     = {}
    scaled_data = np.zeros((len(data), len(features)))

    for i, feature in enumerate(features):
        scalers[feature] = MinMaxScaler(feature_range=(0, 1))
        scaled_data[:, i] = (
            scalers[feature]
            .fit_transform(data[feature].values.reshape(-1, 1))
            .flatten()
        )

    master_scaler = scalers["Close"]
    return scaled_data, master_scaler


def create_sequences(data, time_step=30):
    """Create (X, y) sequences for LSTM training."""
    X, y = [], []
    for i in range(len(data) - time_step - 1):
        X.append(data[i : i + time_step, :])   # all features
        y.append(data[i + time_step, 0])         # Close price only
    return np.array(X), np.array(y)


# [OPT-3] Slimmed LSTM: 2 layers (32 / 16 units) instead of 4 layers (64/64/32/32)
# [OPT-4] time_step default lowered to 30
# [OPT-5] epochs 30 → 15, batch_size 64 → 32, EarlyStopping patience 10 → 5
def train_lstm(X_train, y_train, time_step=30, stop_requested_callback=None):
    """
    Train a lightweight LSTM model.

    Architecture change (OPT-3):
      Original : LSTM(64) → LSTM(64) → Dropout → LSTM(32) → LSTM(32) → Dropout → Dense(16) → Dense(16) → Dense(1)
      Updated  : LSTM(32) → Dropout(0.2) → LSTM(16) → Dropout(0.2) → Dense(1)
    Parameter count drops from ~110 k to ~14 k for a 6-feature, 30-step input.
    """
    from tensorflow.keras.optimizers import Adam
    from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping, Callback

    n_features = X_train.shape[2]
    X_train    = X_train.reshape(X_train.shape[0], time_step, n_features)

    # [OPT-3] Lightweight architecture
    model = Sequential([
        LSTM(32, return_sequences=True,
             input_shape=(time_step, n_features)),
        Dropout(0.2),
        LSTM(16, return_sequences=False),
        Dropout(0.2),
        Dense(1),
    ])

    class StopCallback(Callback):
        def on_epoch_end(self, epoch, logs=None):
            if stop_requested_callback and stop_requested_callback():
                self.model.stop_training = True
                print("Training stopped early by user request.")

    optimizer = Adam(learning_rate=0.001)
    model.compile(optimizer=optimizer, loss="mean_squared_error")

    # [OPT-7] Patience 10 → 5 for faster early exit on free-tier CPU
    reduce_lr     = ReduceLROnPlateau(monitor="val_loss", factor=0.3,
                                      patience=3, min_lr=0.0001, verbose=0)
    early_stop    = EarlyStopping(monitor="val_loss", patience=5,
                                  restore_best_weights=True, verbose=1)
    callbacks     = [reduce_lr, early_stop]
    if stop_requested_callback:
        callbacks.append(StopCallback())

    print(f"Training lightweight LSTM: {X_train.shape[0]} samples, "
          f"{n_features} features, time_step={time_step}")

    # [OPT-5] epochs 30 → 15, batch_size 64 → 32
    model.fit(
        X_train, y_train,
        epochs=15,
        batch_size=32,
        validation_split=0.2,
        callbacks=callbacks,
        verbose=1,
    )
    return model


# [OPT-6] XGBoost: n_estimators 300 → 100, max_depth 6 → 4
def train_xgboost(X_train, residuals, stop_requested_callback=None):
    """Train a leaner XGBoost model on LSTM residuals."""
    if stop_requested_callback and stop_requested_callback():
        print("XGBoost training cancelled due to stop request.")
        return None

    # [OPT-6] Reduced complexity for free-tier memory / speed
    params = {
        "objective":        "reg:squarederror",
        "n_estimators":     100,   # was 300
        "learning_rate":    0.1,
        "max_depth":        4,     # was 6
        "subsample":        0.8,
        "colsample_bytree": 0.8,
        "min_child_weight": 3,
        "gamma":            0.1,
        "reg_alpha":        0.1,
        "reg_lambda":       1.0,
        "tree_method":      "hist",
    }

    if stop_requested_callback:
        class StopCallbackHandler(xgb.callback.TrainingCallback):
            def after_iteration(self, model, epoch, evals_log):
                if stop_requested_callback():
                    print("XGBoost training stopped by user request.")
                    return True
                return False

        xgb_model = xgb.XGBRegressor(**params)
        xgb_model.set_params(callbacks=[StopCallbackHandler()])
        xgb_model.fit(X_train, residuals)
    else:
        xgb_model = xgb.XGBRegressor(**params)
        xgb_model.fit(
            X_train, residuals,
            eval_metric=["rmse"],
            early_stopping_rounds=10,   # was 20 [OPT-6]
            verbose=False,
            eval_set=[(X_train, residuals)],
        )

    return xgb_model


def predict_stock_price(
    lstm_model, xgb_model, data, scaler,
    time_step=30, days_ahead=5, stop_requested_callback=None
):
    """Make predictions using both LSTM and XGBoost with price anchoring."""
    if stop_requested_callback and stop_requested_callback():
        return None

    n_features     = data.shape[1]
    temp_input     = data[-time_step:].tolist()

    last_actual_close = scaler.inverse_transform(
        np.array([[data[-1, 0]]]))[0][0]
    print(f"Base price: ${last_actual_close:.2f}")

    original_prices = scaler.inverse_transform(data[:, 0].reshape(-1, 1))
    daily_returns   = np.diff(original_prices, axis=0) / original_prices[:-1]
    volatility      = np.std(daily_returns)

    # Calibrate model against actual last price
    lstm_input       = np.array(temp_input[-time_step:]).reshape(1, time_step, n_features)
    lstm_pred_cal    = lstm_model.predict(lstm_input, verbose=0)[0][0]
    xgb_input_cal    = np.array(temp_input[-time_step:]).reshape(1, -1)
    try:
        combined_cal = lstm_pred_cal + (xgb_model.predict(xgb_input_cal)[0]
                                        if xgb_model is not None else 0)
    except Exception:
        combined_cal = lstm_pred_cal

    model_current   = scaler.inverse_transform(
        np.array([[combined_cal]]))[0][0]
    correction_factor = (last_actual_close / model_current
                         if model_current > 0 else 1.0)
    print(f"Calibration: model=${model_current:.2f}, "
          f"actual=${last_actual_close:.2f}, factor={correction_factor:.4f}")

    predictions    = []
    prev_day_pred  = combined_cal

    for day in range(days_ahead):
        if stop_requested_callback and stop_requested_callback():
            print(f"Prediction stopped at day {day}/{days_ahead}")
            break

        lstm_input = np.array(temp_input[-time_step:]).reshape(1, time_step, n_features)
        lstm_pred  = lstm_model.predict(lstm_input, verbose=0)[0][0]
        xgb_input  = np.array(temp_input[-time_step:]).reshape(1, -1)

        try:
            combined_pred = (lstm_pred + xgb_model.predict(xgb_input)[0]
                             if xgb_model is not None else lstm_pred)
        except Exception as e:
            print(f"XGBoost predict error: {e}")
            combined_pred = lstm_pred

        prev_unscaled    = scaler.inverse_transform(
            np.array([[prev_day_pred]]))[0][0]
        current_unscaled = scaler.inverse_transform(
            np.array([[combined_pred]]))[0][0]
        price_change     = current_unscaled - prev_unscaled
        trend_direction  = 1 if price_change >= 0 else -1

        day_volatility      = volatility * (1 + day * 0.1)
        adjusted_volatility = min(day_volatility, 0.015)
        random_factor       = np.random.normal(0, adjusted_volatility)

        if trend_direction > 0:
            flux_factor = (abs(random_factor) * trend_direction * 0.15
                           if np.random.random() < 0.7
                           else -abs(random_factor) * trend_direction * 0.3)
        else:
            flux_factor = (abs(random_factor) * trend_direction * 0.25
                           if np.random.random() < 0.8
                           else -abs(random_factor) * trend_direction * 0.1)

        flux_amount      = prev_unscaled * flux_factor
        adjusted_unscaled = current_unscaled + flux_amount
        adjusted_pred     = scaler.transform(
            np.array([[adjusted_unscaled]]))[0][0]

        next_row    = temp_input[-1].copy()
        next_row[0] = adjusted_pred
        prev_day_pred = adjusted_pred

        predictions.append(adjusted_pred)
        temp_input.append(next_row)

    if not predictions:
        return None

    final_predictions    = scaler.inverse_transform(
        np.array(predictions).reshape(-1, 1))
    corrected_predictions = final_predictions * correction_factor

    print("\nPredictions (original → corrected):")
    for i in range(len(final_predictions)):
        print(f"  Day {i+1}: ${final_predictions[i][0]:.2f} "
              f"→ ${corrected_predictions[i][0]:.2f}")

    return corrected_predictions


def plot_prices(data, predictions, symbol, days_ahead):
    """Plot actual + predicted prices (used in standalone main())."""
    fig = go.Figure()
    three_months_ago = data.index[-1] - pd.DateOffset(months=3)
    actual_data = data.loc[three_months_ago:]
    close_prices = (actual_data["Close"]
                    if isinstance(actual_data, pd.DataFrame) and "Close" in actual_data.columns
                    else actual_data.iloc[:, 0])

    future_dates = []
    last_date = data.index[-1]
    for i in range(1, days_ahead + 1):
        next_date = last_date + timedelta(days=i)
        while next_date.weekday() > 4:
            next_date += timedelta(days=1)
        future_dates.append(next_date)
    future_dates    = list(dict.fromkeys(future_dates))
    prediction_data = predictions[: len(future_dates)].flatten()

    fig.add_trace(go.Scatter(
        x=future_dates, y=prediction_data,
        mode="lines+markers", name="Predicted Price",
        line=dict(color="orange", width=3)))
    fig.add_trace(go.Scatter(
        x=close_prices.index, y=close_prices.values,
        mode="lines", name="Actual Price",
        line=dict(color="blue", width=2)))
    fig.add_trace(go.Scatter(
        x=[close_prices.index[-1]], y=[close_prices.values[-1]],
        mode="markers", name="Latest Price",
        marker=dict(color="green", size=10, symbol="circle")))

    fig.update_layout(
        title=f"Stock Price Prediction for {symbol}",
        xaxis_title="Date", yaxis_title="Price (USD)",
        template="plotly_white", hovermode="x unified")
    fig.show()


# =============================================================================
#                   NEWS SENTIMENT ANALYSIS FUNCTIONS
# =============================================================================

def fetch_finnhub_news(company_symbol):
    end_date      = datetime.now()
    start_date    = end_date - timedelta(days=28)
    url = (f"https://finnhub.io/api/v1/company-news"
           f"?symbol={company_symbol}"
           f"&from={start_date.strftime('%Y-%m-%d')}"
           f"&to={end_date.strftime('%Y-%m-%d')}"
           f"&token={FINNHUB_API_KEY}")
    try:
        response = requests.get(url)
        if response.status_code == 200:
            articles  = response.json()
            headlines = [a["headline"] for a in articles if "headline" in a]
            return headlines
        else:
            print(f"Error fetching news: {response.status_code}")
            return []
    except Exception as e:
        print(f"Error parsing news response: {e}")
        return []


def analyze_sentiment(headlines):
    try:
        sid              = SentimentIntensityAnalyzer()
        sentiment_results = []
        sentiment_totals  = {"positive": 0, "negative": 0, "neutral": 0}

        for headline in headlines:
            if not headline or not isinstance(headline, str):
                continue
            sentiment = sid.polarity_scores(headline)
            sentiment_results.append({"headline": headline, "sentiment": sentiment})
            if sentiment["compound"] > 0.05:
                sentiment_totals["positive"] += 1
            elif sentiment["compound"] < -0.05:
                sentiment_totals["negative"] += 1
            else:
                sentiment_totals["neutral"] += 1

        return sentiment_results, sentiment_totals
    except Exception as e:
        print(f"Error in sentiment analysis: {e}")
        return [], {"positive": 0, "negative": 0, "neutral": 0}


def plot_sentiment_pie(sentiment_totals, company_symbol):
    fig = go.Figure(data=[go.Pie(
        labels=["Positive", "Negative", "Neutral"],
        values=[sentiment_totals["positive"],
                sentiment_totals["negative"],
                sentiment_totals["neutral"]],
        marker=dict(colors=["#2ecc71", "#e74c3c", "#95a5a6"],
                    line=dict(color="white", width=0)),
        textinfo="percent+label", textfont_size=20)])
    fig.update_layout(
        title=f"Sentiment Distribution for {company_symbol} (Last 28 Days)",
        showlegend=True)
    fig.show()


# =============================================================================
#          AI SUMMARY FUNCTIONS  [OPT-1] Transformers removed
# =============================================================================

def _extractive_summary(headlines, n=3):
    """
    Lightweight extractive summariser – replaces the BART transformer pipeline.
    [OPT-1] Picks the top-n headlines by absolute VADER compound score so the
    most opinionated sentences surface first.  No heavy model download needed.
    """
    if not headlines:
        return ""
    try:
        sid    = SentimentIntensityAnalyzer()
        scored = [(h, abs(sid.polarity_scores(h)["compound"]))
                  for h in headlines if h and isinstance(h, str)]
        scored.sort(key=lambda x: x[1], reverse=True)
        top    = [h for h, _ in scored[:n]]
        return " | ".join(top)
    except Exception as e:
        print(f"Extractive summary error: {e}")
        return headlines[0] if headlines else ""


def generate_sentiment_summary(sentiment_totals, headlines, company_symbol):
    """
    Generate a human-readable sentiment summary.
    [OPT-1] Uses simple NLTK-based extractive summarisation instead of a
    Transformers pipeline (removes ~1.2 GB BART model download).
    """
    try:
        total   = max(1, sum(sentiment_totals.values()))
        pos_pct = sentiment_totals["positive"] / total * 100
        neg_pct = sentiment_totals["negative"] / total * 100

        summary = (
            f"Over the past 28 days, {len(headlines)} news articles about "
            f"{company_symbol} were analysed. "
            f"{sentiment_totals['positive']} positive ({pos_pct:.0f}%), "
            f"{sentiment_totals['negative']} negative ({neg_pct:.0f}%), "
            f"and {sentiment_totals['neutral']} neutral articles found."
        )

        if headlines:
            key_headlines = _extractive_summary(headlines, n=2)
            if key_headlines:
                summary += f" Key headlines: {key_headlines}"

        return summary
    except Exception as e:
        print(f"Error in generate_sentiment_summary: {e}")
        return f"Unable to generate sentiment summary for {company_symbol}."


def generate_prediction_summary(pred_df, company_symbol):
    first_price = pred_df["Predicted Price"].iloc[0]
    last_price  = pred_df["Predicted Price"].iloc[-1]
    return (
        f"The predicted stock prices for {company_symbol} range from "
        f"${first_price:.2f} to ${last_price:.2f} over the forecast period."
    )


def display_price_table(data, predictions, symbol, days_ahead):
    """Print prediction results as a table (used in standalone main())."""
    if isinstance(data, pd.DataFrame) and "Close" in data.columns:
        last_price = data["Close"].iloc[-1]
        last_date  = data.index[-1]
    else:
        last_price = data.iloc[-1, 0]
        last_date  = data.index[-1]

    future_dates = []
    for i in range(1, days_ahead + 1):
        next_date = last_date + timedelta(days=i)
        while next_date.weekday() > 4:
            next_date += timedelta(days=1)
        future_dates.append(next_date)
    future_dates    = list(dict.fromkeys(future_dates))
    prediction_data = predictions[: len(future_dates)].flatten()

    last_price_row = pd.DataFrame({
        "Date": [last_date.strftime("%Y-%m-%d")],
        "Price": [f"${last_price:.2f}"],
        "Change": ["0.00%"],
        "Note": ["Actual last closing price"],
    })
    pred_rows = []
    for i, (date, price) in enumerate(zip(future_dates, prediction_data)):
        change_pct = ((price - last_price) / last_price) * 100
        pred_rows.append({
            "Date": date.strftime("%Y-%m-%d"),
            "Price": f"${price:.2f}",
            "Change": f"{change_pct:.2f}%",
            "Note": f"Day {i+1} prediction",
        })

    combined_df = pd.concat([last_price_row, pd.DataFrame(pred_rows)],
                            ignore_index=True)
    print(f"\n{symbol} Stock Price Prediction Table:")
    print("=" * 80)
    print(combined_df.to_string(index=False))
    print("=" * 80)

    return pd.DataFrame({
        "Date": [d.strftime("%Y-%m-%d") for d in future_dates],
        "Predicted Price": prediction_data,
    })


# =============================================================================
#                          STANDALONE MAIN
# =============================================================================

def main():
    symbol = input("Enter the stock symbol (e.g., AAPL): ").upper()
    try:
        days_ahead = int(input("Number of future days to predict (e.g., 5): "))
    except ValueError:
        print("Invalid input. Please enter an integer.")
        return

    print(f"\nFetching historical data for {symbol}...")
    data = fetch_stock_data(symbol, outputsize="full")
    if data is None or len(data) < 50:
        print(f"Not enough data points for {symbol}.")
        return

    print("Preprocessing data...")
    scaled_data, scaler = preprocess_data(data)

    # [OPT-4] time_step 60 → 30 in standalone mode too
    time_step = 30
    X, y = create_sequences(scaled_data, time_step)
    if len(X) == 0:
        print("Could not create sequences.")
        return

    train_size       = int(len(X) * 0.8)
    X_train, y_train = X[:train_size], y[:train_size]

    print("Training LSTM model...")
    lstm_model = train_lstm(X_train, y_train, time_step)

    lstm_train_preds = lstm_model.predict(X_train, verbose=0).flatten()
    residuals        = y_train - lstm_train_preds

    print("Training XGBoost model...")
    xgb_model = train_xgboost(X_train.reshape(X_train.shape[0], -1), residuals)

    print(f"Predicting {days_ahead} days ahead...")
    predictions = predict_stock_price(
        lstm_model, xgb_model, scaled_data, scaler, time_step, days_ahead)

    display_price_table(data, predictions, symbol, days_ahead)

    future_dates = []
    last_date    = data.index[-1]
    for i in range(1, days_ahead + 1):
        next_date = last_date + timedelta(days=i)
        while next_date.weekday() > 4:
            next_date += timedelta(days=1)
        future_dates.append(next_date)
    future_dates = list(dict.fromkeys(future_dates))

    pred_df = pd.DataFrame({
        "Date": [d.strftime("%Y-%m-%d") for d in future_dates[: len(predictions)]],
        "Predicted Price": predictions.flatten()[: len(future_dates)],
    })
    print("\nPrediction summary:")
    print(generate_prediction_summary(pred_df, symbol))

    print("\nFetching news for sentiment analysis...")
    headlines = fetch_finnhub_news(symbol)
    if headlines:
        sentiment_results, sentiment_totals = analyze_sentiment(headlines)
        plot_sentiment_pie(sentiment_totals, symbol)
        print(generate_sentiment_summary(sentiment_totals, headlines, symbol))
    else:
        print("No headlines found.")


if __name__ == "__main__":
    main()