Time-Series-Analysis/app.py

from flask import Flask, request, render_template, session
from werkzeug.utils import secure_filename
from models.time_series import process_time_series
from models.plotting import create_comparison_plot
from utils.file_handling import allowed_file, read_file, save_processed_file
from utils.forecast_history import update_forecast_history, download_forecast_history
import os

app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = 'Uploads'
app.config['ALLOWED_EXTENSIONS'] = {'csv', 'xls', 'xlsx'}
app.secret_key = 'your-secret-key'  # Required for session management

# Ensure upload folder exists
os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)


@app.route('/')
def index():
    return render_template('index.html')


@app.route('/upload', methods=['POST'])
def upload_file():
    if 'file' not in request.files:
        return render_template('index.html', error='No file part')

    file = request.files['file']
    if file.filename == '':
        return render_template('index.html', error='No selected file')

    if file and allowed_file(file.filename):
        filename = secure_filename(file.filename)
        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
        file.save(filepath)
        session['filepath'] = filepath  # Store filepath in session
        session['forecast_history'] = []  # Initialize forecast history
        session['selected_indices'] = []  # Initialize selected indices

        # Get user selections
        do_decomposition = 'decomposition' in request.form
        do_forecasting = 'forecasting' in request.form
        do_acf_pacf = 'acf_pacf' in request.form
        train_percent = float(request.form.get('train_percent', 80)) / 100
        test_percent = float(request.form.get('test_percent', 20)) / 100
        forecast_periods = int(request.form.get('forecast_periods', 12))
        model_type = request.form.get('model_type', 'ARIMA')

        # Validate train/test percentages
        if abs(train_percent + test_percent - 1.0) > 0.01:  # Allow small float precision errors
            return render_template('index.html', error='Train and test percentages must sum to 100%')

        session['do_decomposition'] = do_decomposition
        session['do_forecasting'] = do_forecasting
        session['do_acf_pacf'] = do_acf_pacf
        session['train_percent'] = train_percent
        session['test_percent'] = test_percent
        session['forecast_periods'] = forecast_periods
        session['model_type'] = model_type

        result = process_time_series(filepath, do_decomposition, do_forecasting, do_acf_pacf, train_percent,
                                     forecast_periods, model_type)

        if 'error' in result:
            return render_template('index.html', error=result['error'])

        # Update forecast history if unique
        if do_forecasting and result['metrics']:
            update_forecast_history(session, train_percent, test_percent, forecast_periods, model_type,
                                    result['metrics'])

        return render_template('results.html',
                               do_decomposition=do_decomposition,
                               do_forecasting=do_forecasting,
                               do_acf_pacf=do_acf_pacf,
                               train_percent=train_percent * 100,
                               test_percent=test_percent * 100,
                               forecast_periods=forecast_periods,
                               forecast_history=session['forecast_history'],
                               selected_indices=session['selected_indices'],
                               **result)


@app.route('/reforecast', methods=['POST'])
def reforecast():
    filepath = session.get('filepath')
    if not filepath or not os.path.exists(filepath):
        return render_template('index.html', error='Session expired or file not found. Please upload the file again.')

    # Get user selections from reforecast form
    train_percent = float(request.form.get('train_percent', 80)) / 100
    test_percent = float(request.form.get('test_percent', 20)) / 100
    forecast_periods = int(request.form.get('forecast_periods', 12))
    model_type = request.form.get('model_type', 'ARIMA')
    add_to_existing = 'add_to_existing' in request.form

    # Validate train/test percentages
    if abs(train_percent + test_percent - 1.0) > 0.01:  # Allow small float precision errors
        return render_template('index.html', error='Train and test percentages must sum to 100%')

    # Get original selections from session or defaults
    do_decomposition = session.get('do_decomposition', False)
    do_forecasting = True  # Since this is a reforecast
    do_acf_pacf = session.get('do_acf_pacf', False)

    result = process_time_series(filepath, do_decomposition, do_forecasting, do_acf_pacf, train_percent,
                                 forecast_periods, model_type)

    if 'error' in result:
        return render_template('index.html', error=result['error'])

    # Update forecast history if unique
    if do_forecasting and result['metrics']:
        update_forecast_history(session, train_percent, test_percent, forecast_periods, model_type, result['metrics'],
                                add_to_existing)

    # Update session with current parameters
    session['train_percent'] = train_percent
    session['test_percent'] = test_percent
    session['forecast_periods'] = forecast_periods
    session['model_type'] = model_type

    # Generate comparison plot if multiple forecasts are selected
    if len(session.get('selected_indices', [])) > 1:
        result['forecast_html'] = create_comparison_plot(filepath, session['forecast_history'],
                                                         session['selected_indices'])

    return render_template('results.html',
                           do_decomposition=do_decomposition,
                           do_forecasting=do_forecasting,
                           do_acf_pacf=do_acf_pacf,
                           train_percent=train_percent * 100,
                           test_percent=test_percent * 100,
                           forecast_periods=forecast_periods,
                           forecast_history=session['forecast_history'],
                           selected_indices=session['selected_indices'],
                           scroll_to_forecast=True,
                           **result)


@app.route('/compare_forecasts', methods=['POST'])
def compare_forecasts():
    filepath = session.get('filepath')
    if not filepath or not os.path.exists(filepath):
        return render_template('index.html', error='Session expired or file not found. Please upload the file again.')

    # Get selected forecast indices
    selected_indices = [int(idx) for idx in request.form.getlist('selected_forecasts')]
    if not selected_indices:
        return render_template('index.html', error='No forecasts selected for comparison')

    # Update session with selected indices
    session['selected_indices'] = selected_indices
    session.modified = True

    # Get current parameters and settings
    do_decomposition = session.get('do_decomposition', False)
    do_forecasting = session.get('do_forecasting', True)
    do_acf_pacf = session.get('do_acf_pacf', False)
    train_percent = session.get('train_percent', 0.8)
    test_percent = session.get('test_percent', 0.2)
    forecast_periods = session.get('forecast_periods', 12)
    model_type = session.get('model_type', 'ARIMA')

    # Generate comparison plot
    forecast_html = create_comparison_plot(filepath, session['forecast_history'], selected_indices)

    # Re-run the current forecast to maintain other results
    result = process_time_series(filepath, do_decomposition, do_forecasting, do_acf_pacf, train_percent,
                                 forecast_periods, model_type)

    if 'error' in result:
        return render_template('index.html', error=result['error'])

    result['forecast_html'] = forecast_html

    return render_template('results.html',
                           do_decomposition=do_decomposition,
                           do_forecasting=do_forecasting,
                           do_acf_pacf=do_acf_pacf,
                           train_percent=train_percent * 100,
                           test_percent=test_percent * 100,
                           forecast_periods=forecast_periods,
                           forecast_history=session['forecast_history'],
                           selected_indices=selected_indices,
                           scroll_to_forecast=True,
                           **result)


@app.route('/download_forecast_history')
def download_forecast_history():
    return download_forecast_history(session)


@app.route('/download/<filename>')
def download_file(filename):
    filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
    return send_file(filepath, as_attachment=True)


if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000, debug=True)