reconnect moved files to git repo

app.py

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+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)