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ilgazca
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app.py
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285
app.py
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from flask import Flask, request, render_template, send_file, session
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import pandas as pd
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import io
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import os
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from statsmodels.tsa.seasonal import seasonal_decompose
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from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
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import pmdarima as pm
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import plotly.express as px
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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import plotly.io as pio
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from werkzeug.utils import secure_filename
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import matplotlib
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matplotlib.use('Agg') # Use non-interactive backend
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import matplotlib.pyplot as plt
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import io
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import base64
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import numpy as np
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app = Flask(__name__)
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app.config['UPLOAD_FOLDER'] = 'Uploads'
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app.config['ALLOWED_EXTENSIONS'] = {'csv', 'xls', 'xlsx'}
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app.secret_key = 'your-secret-key' # Required for session management
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# Ensure upload folder exists
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os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
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def allowed_file(filename):
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return '.' in filename and filename.rsplit('.', 1)[1].lower() in app.config['ALLOWED_EXTENSIONS']
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def create_acf_pacf_plots(data):
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# Create ACF and PACF plots using matplotlib
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))
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plot_acf(data, ax=ax1, lags=40)
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ax1.set_title('Autocorrelation Function')
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plot_pacf(data, ax=ax2, lags=40)
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ax2.set_title('Partial Autocorrelation Function')
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# Convert matplotlib plot to Plotly
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buf = io.BytesIO()
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plt.savefig(buf, format='png')
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plt.close(fig)
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buf.seek(0)
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img_str = base64.b64encode(buf.getvalue()).decode('utf-8')
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# Create Plotly figure with image
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fig_plotly = go.Figure()
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fig_plotly.add_layout_image(
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dict(
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source=f'data:image/png;base64,{img_str}',
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x=0,
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y=1,
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xref="paper",
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yref="paper",
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sizex=1,
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sizey=1,
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sizing="stretch",
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opacity=1,
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layer="below"
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)
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)
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fig_plotly.update_layout(
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height=600,
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showlegend=False,
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xaxis=dict(visible=False),
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yaxis=dict(visible=False)
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)
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return pio.to_html(fig_plotly, full_html=False)
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def process_time_series(filepath, do_decomposition, do_forecasting, do_acf_pacf, train_percent, forecast_periods):
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try:
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# Read file
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if filepath.endswith('.csv'):
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df = pd.read_csv(filepath)
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else:
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df = pd.read_excel(filepath)
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# Ensure datetime column exists
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date_col = df.columns[0] # Assume first column is date
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value_col = df.columns[1] # Assume second column is value
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df[date_col] = pd.to_datetime(df[date_col])
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df.set_index(date_col, inplace=True)
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# Initialize variables
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plot_html = None
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forecast_html = None
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acf_pacf_html = None
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summary = df[value_col].describe().to_dict()
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arima_params = None
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seasonal_params = None
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train_size = None
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test_size = None
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# Save processed data
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processed_df = df.copy()
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# Time series decomposition
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if do_decomposition:
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decomposition = seasonal_decompose(df[value_col], model='additive', period=12)
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fig = make_subplots(rows=4, cols=1,
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subplot_titles=('Original Series', 'Trend', 'Seasonality', 'Residuals'))
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fig.add_trace(go.Scatter(x=df.index, y=df[value_col], name='Original'), row=1, col=1)
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fig.add_trace(go.Scatter(x=df.index, y=decomposition.trend, name='Trend'), row=2, col=1)
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fig.add_trace(go.Scatter(x=df.index, y=decomposition.seasonal, name='Seasonality'), row=3, col=1)
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fig.add_trace(go.Scatter(x=df.index, y=decomposition.resid, name='Residuals'), row=4, col=1)
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fig.update_layout(height=800, showlegend=True)
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plot_html = pio.to_html(fig, full_html=False)
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processed_df['Trend'] = decomposition.trend
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processed_df['Seasonality'] = decomposition.seasonal
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processed_df['Residuals'] = decomposition.resid
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# Forecasting
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if do_forecasting:
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# Split data into train and test
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train_size = int(len(df) * train_percent)
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test_size = len(df) - train_size
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train_data = df[value_col].iloc[:train_size]
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test_data = df[value_col].iloc[train_size:] if test_size > 0 else pd.Series()
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# Auto ARIMA for best parameters
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model = pm.auto_arima(train_data,
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seasonal=True,
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m=12,
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start_p=0, start_q=0,
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max_p=3, max_q=3,
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start_P=0, start_Q=0,
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max_P=2, max_Q=2,
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d=1, D=1,
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trace=False,
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error_action='ignore',
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suppress_warnings=True,
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stepwise=True)
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# Fit ARIMA with best parameters
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model_fit = model.fit(train_data)
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forecast = model_fit.predict(n_periods=forecast_periods)
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# Get ARIMA parameters
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arima_params = model.order
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seasonal_params = model.seasonal_order
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# Forecast plot
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forecast_dates = pd.date_range(start=df.index[-1], periods=forecast_periods + 1,
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freq=df.index.inferred_freq)[1:]
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forecast_fig = go.Figure()
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forecast_fig.add_trace(go.Scatter(x=df.index, y=df[value_col], name='Historical'))
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if test_size > 0:
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forecast_fig.add_trace(
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go.Scatter(x=df.index[train_size:], y=test_data, name='Test Data', line=dict(color='green')))
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forecast_fig.add_trace(go.Scatter(x=forecast_dates, y=forecast, name='Forecast', line=dict(dash='dash')))
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forecast_fig.update_layout(title=f'Forecast (ARIMA{arima_params}, Seasonal{seasonal_params})', height=400)
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forecast_html = pio.to_html(forecast_fig, full_html=False)
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# ACF/PACF plots
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if do_acf_pacf:
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acf_pacf_html = create_acf_pacf_plots(df[value_col])
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# Save processed data
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processed_df.to_csv(os.path.join(app.config['UPLOAD_FOLDER'], 'processed_' + os.path.basename(filepath)))
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return {
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'plot_html': plot_html,
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'forecast_html': forecast_html,
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'acf_pacf_html': acf_pacf_html,
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'summary': summary,
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'filename': 'processed_' + os.path.basename(filepath),
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'arima_params': arima_params,
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'seasonal_params': seasonal_params,
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'train_size': train_size,
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'test_size': test_size
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}
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except Exception as e:
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return {'error': str(e)}
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@app.route('/')
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def index():
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return render_template('index.html')
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@app.route('/upload', methods=['POST'])
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def upload_file():
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if 'file' not in request.files:
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return render_template('index.html', error='No file part')
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file = request.files['file']
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if file.filename == '':
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return render_template('index.html', error='No selected file')
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if file and allowed_file(file.filename):
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filename = secure_filename(file.filename)
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filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
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file.save(filepath)
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session['filepath'] = filepath # Store filepath in session
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# Get user selections
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do_decomposition = 'decomposition' in request.form
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do_forecasting = 'forecasting' in request.form
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do_acf_pacf = 'acf_pacf' in request.form
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train_percent = float(request.form.get('train_percent', 80)) / 100
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test_percent = float(request.form.get('test_percent', 20)) / 100
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# Validate train/test percentages
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if abs(train_percent + test_percent - 1.0) > 0.01: # Allow small float precision errors
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return render_template('index.html', error='Train and test percentages must sum to 100%')
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session['do_decomposition'] = do_decomposition
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session['do_forecasting'] = do_forecasting
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session['do_acf_pacf'] = do_acf_pacf
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result = process_time_series(filepath, do_decomposition, do_forecasting, do_acf_pacf, train_percent,
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forecast_periods=int(request.form.get('forecast_periods', 12)))
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if 'error' in result:
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return render_template('index.html', error=result['error'])
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return render_template('results.html',
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do_decomposition=do_decomposition,
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do_forecasting=do_forecasting,
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do_acf_pacf=do_acf_pacf,
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train_percent=train_percent * 100,
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test_percent=test_percent * 100,
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forecast_periods=int(request.form.get('forecast_periods', 12)),
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**result)
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@app.route('/reforecast', methods=['POST'])
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def reforecast():
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filepath = session.get('filepath')
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if not filepath or not os.path.exists(filepath):
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return render_template('index.html', error='Session expired or file not found. Please upload the file again.')
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# Get user selections from reforecast form
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train_percent = float(request.form.get('train_percent', 80)) / 100
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test_percent = float(request.form.get('test_percent', 20)) / 100
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forecast_periods = int(request.form.get('forecast_periods', 12))
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# Validate train/test percentages
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if abs(train_percent + test_percent - 1.0) > 0.01: # Allow small float precision errors
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return render_template('index.html', error='Train and test percentages must sum to 100%')
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# Get original selections from session or defaults
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do_decomposition = session.get('do_decomposition', False)
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do_forecasting = True # Since this is a reforecast
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do_acf_pacf = session.get('do_acf_pacf', False)
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result = process_time_series(filepath, do_decomposition, do_forecasting, do_acf_pacf, train_percent,
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forecast_periods)
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if 'error' in result:
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return render_template('index.html', error=result['error'])
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# Update session with new parameters
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session['train_percent'] = train_percent
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session['test_percent'] = test_percent
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session['forecast_periods'] = forecast_periods
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return render_template('results.html',
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do_decomposition=do_decomposition,
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do_forecasting=do_forecasting,
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do_acf_pacf=do_acf_pacf,
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train_percent=train_percent * 100,
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test_percent=test_percent * 100,
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forecast_periods=forecast_periods,
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**result)
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@app.route('/download/<filename>')
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def download_file(filename):
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filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
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return send_file(filepath, as_attachment=True)
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if __name__ == '__main__':
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app.run(debug=True)
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