Create app.py

app.py

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+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
+
+# 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
+
+# Streamlit app starts here
+def main():
+    st.title("Exponential Smoothing Forecasting")
+
+    # Upload Data Section
+    uploaded_file = st.file_uploader("Choose a file")
+    if uploaded_file is not None:
+        data = pd.read_csv(uploaded_file)
+        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)
+
+        # 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"Forecast with AIC: {aic}")
+            st.line_chart(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}")
+
+        # 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')
+
+if __name__ == "__main__":
+    main()