Update app.py

app.py

@@ -1,12 +1,87 @@
-import pandas as pd
 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]
 
-# 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'])
+# 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}")
 
-# Plot the test data
-st.line_chart(test_df)
+# 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')