Update app.py

app.py

@@ -1,93 +1,12 @@
-import streamlit as st
 import pandas as pd
-from statsmodels.tsa.holtwinters import ExponentialSmoothing
-import numpy as np
-from itertools import product
-from io import BytesIO
-
-# Function to run the Exponential Smoothing Model
-def run_exp_smoothing(city_data, trend, damped_trend, seasonal, seasonal_period):
-    try:
-        model = ExponentialSmoothing(city_data, trend=trend, damped_trend=damped_trend, seasonal=seasonal, seasonal_periods=seasonal_period)
-        model_fit = model.fit(optimized=True)
-        return model_fit.forecast(steps=6), model_fit.aic
-    except Exception as e:
-        return None, None
-
-def create_data():
-  data = pd.read_csv('accident_count.csv', parse_dates=True, index_col=0)
-  data.index = pd.to_datetime(data.index, format='%Y%m')
-  data = data.groupby('City').resample('M').sum().reset_index()
-  #data = data[data['City'] == 'ARAR']
-  data.index = data['Accident Month Bracket']
-  data = data.drop(['Accident Month Bracket'],axis=1)
-  data.index = data.index.strftime('%Y-%m')
-  return data
-    
-# Function to convert DataFrame to Excel
-def to_excel(df):
-    output = BytesIO()
-    writer = pd.ExcelWriter(output, engine='xlsxwriter')
-    df.to_excel(writer, sheet_name='Sheet1')
-    writer.save()
-    processed_data = output.getvalue()
-    return processed_data
-
-
-st.title("Exponential Smoothing Forecasting")
-# Upload Data Section
-# uploaded_file = st.file_uploader("Choose a file")
-# if uploaded_file is not None:
-
-data = create_data()
-unique_cities = data['City'].unique()
-
-# Select a city
-selected_city = st.selectbox('Select a City', unique_cities)
-
-# Sliders for parameter adjustment
-trend = st.select_slider('Select Trend', options=['add', 'mul', None])
-damped_trend = st.checkbox('Damped Trend')
-seasonal = st.select_slider('Select Seasonal', options=['add', 'mul', None])
-seasonal_period = st.slider('Seasonal Period', 1, 24, 12)
-
-# ... [previous code remains the same]
-
-# Display forecast with current parameters
-city_data = data[data['City'] == selected_city]['Accident Count']
-forecast, aic = run_exp_smoothing(city_data, trend, damped_trend, seasonal, seasonal_period)
-
-if forecast is not None:
-    st.write(f"Best Parameters with AIC: {aic}")
-    st.write(f"Trend: {trend}, Damped Trend: {damped_trend}, Seasonal: {seasonal}, Seasonal Period: {seasonal_period}")
-
-    forecast_index = pd.date_range(start=city_data.index[-1], periods=7, freq='M')[1:]
-    forecast_index = forecast_index.to_period('M')  # Convert to period index with monthly frequency
-    forecast_df = pd.DataFrame(forecast, index=forecast_index, columns=['Forecast'])
-    
-    # Ensure the index is correctly formatted as 'YYYY-MM'
-    forecast_df.index = forecast_df.index.strftime('%Y-%m')
-    
-    st.line_chart(forecast_df)
-
-# ... [rest of the code]
-
-
-# Grid search button
-if st.button('Run Grid Search'):
-    best_aic = float('inf')
-    best_params = None
-    for param_set in product(['add', 'mul', None], [True, False], ['add', 'mul', None], [12]):
-        _, temp_aic = run_exp_smoothing(city_data, *param_set)
-        if temp_aic and temp_aic < best_aic:
-            best_aic = temp_aic
-            best_params = param_set
-    st.write(f"Best Parameters: {best_params} with AIC: {best_aic}")
+import streamlit as st
 
-# Export to Excel button
-# if st.button('Export to Excel'):
-#     df_to_export = pd.DataFrame(forecast)
-#     excel_data = to_excel(df_to_export)
-#     st.download_button(label='📥 Download Excel', data=excel_data, file_name='forecast.xlsx', mime='application/vnd.ms-excel')
+# Generate some test data
+test_index = pd.date_range(start='2023-01-01', periods=6, freq='M')
+test_data = pd.Series([100, 120, 130, 125, 140, 150], index=test_index)
 
+# Create a DataFrame
+test_df = pd.DataFrame(test_data, columns=['Test Forecast'])
 
+# Plot the test data
+st.line_chart(test_df)