Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import pickle
+import os
+from statsmodels.tsa.arima.model import ARIMA
+from sklearn.tree import DecisionTreeRegressor
+from xgboost import XGBRegressor
+st.set_option('deprecation.showPyplotGlobalUse', False)
+# Paths for model directories
+arima_models_folder = 'models/arima_Models'
+dt_models_folder = 'models/decision_tree_Models'
+xgb_models_folder = 'models/xgb_Models'
+
+merged_data = pd.read_csv('./data/merged_data.csv')
+
+# Prepare weekly sales data for top 10 products
+top_10_products = merged_data.groupby('StockCode')['Quantity'].sum().nlargest(10).index
+weekly_sales = merged_data.groupby(['StockCode', 'Year', 'Week'])['Quantity'].sum().reset_index()
+
+# Load product codes (as an example, you can modify this to load from your source)
+product_codes = ['85123A', '85099B', '22197', '84879', '23084', 
+                 '21181', '22423', '21212', '20713', '21915']
+
+# Title of the app
+st.title("Sales Forecasting with ARIMA, Decision Tree, and XGBoost")
+
+# Model selection dropdown
+model_option = st.selectbox("Select Model", ["ARIMA", "Decision Tree", "XGBoost"])
+
+# Product selection dropdown
+selected_product = st.selectbox("Select Product", product_codes)
+
+# Initialize session state to store forecasts
+if 'arima_forecast' not in st.session_state:
+    st.session_state.arima_forecast = None
+if 'dt_forecast' not in st.session_state:
+    st.session_state.dt_forecast = None
+if 'xgb_forecast' not in st.session_state:
+    st.session_state.xgb_forecast = None
+
+# Forecasting button
+if st.button("Forecast"):
+    if model_option == "ARIMA":
+        # Load ARIMA model
+        model_filename = os.path.join(arima_models_folder, f'arima_model_product_{selected_product}.pkl')
+        with open(model_filename, 'rb') as file:
+            arima_model = pickle.load(file)
+
+        # Forecasting with ARIMA
+        st.session_state.arima_forecast = arima_model.forecast(steps=15)
+        st.write(f"ARIMA Forecast for Product {selected_product}:")
+        st.write(st.session_state.arima_forecast)
+
+        # Plot ARIMA results
+        plt.figure(figsize=(10, 6))
+        plt.plot(st.session_state.arima_forecast, label='ARIMA Forecast', color='blue')
+        plt.title(f'ARIMA Forecast for Product {selected_product}')
+        plt.xlabel('Weeks')
+        plt.ylabel('Sales Quantity')
+        plt.legend()
+        st.pyplot()
+
+    elif model_option == "Decision Tree":
+        # Load Decision Tree model
+        dt_model_filename = os.path.join(dt_models_folder, f'decision_tree_model_product_{selected_product}.pkl')
+        with open(dt_model_filename, 'rb') as file:
+            dt_model = pickle.load(file)
+
+        # Prepare input data for Decision Tree
+        future_weeks = np.array([[2023, week] for week in range(40, 55)])  # Future weeks for prediction
+        st.session_state.dt_forecast = dt_model.predict(future_weeks)
+
+        st.write(f"Decision Tree Forecast for Product {selected_product}:")
+        st.write(st.session_state.dt_forecast)
+
+        # Plot Decision Tree results
+        plt.figure(figsize=(10, 6))
+        plt.plot(st.session_state.dt_forecast, label='Decision Tree Forecast', color='green')
+        plt.title(f'Decision Tree Forecast for Product {selected_product}')
+        plt.xlabel('Weeks')
+        plt.ylabel('Sales Quantity')
+        plt.legend()
+        st.pyplot()
+
+    elif model_option == "XGBoost":
+        # Load XGBoost model
+        xgb_model_filename = os.path.join(xgb_models_folder, f'xgb_model_product_{selected_product}.pkl')
+        with open(xgb_model_filename, 'rb') as file:
+            xgb_model = pickle.load(file)
+
+        # Prepare input data for XGBoost
+        future_weeks = np.array([[2023, week] for week in range(40, 55)])  # Future weeks for prediction
+        st.session_state.xgb_forecast = xgb_model.predict(future_weeks)
+
+        st.write(f"XGBoost Forecast for Product {selected_product}:")
+        st.write(st.session_state.xgb_forecast)
+
+        # Plot XGBoost results
+        plt.figure(figsize=(10, 6))
+        plt.plot(st.session_state.xgb_forecast, label='XGBoost Forecast', color='orange')
+        plt.title(f'XGBoost Forecast for Product {selected_product}')
+        plt.xlabel('Weeks')
+        plt.ylabel('Sales Quantity')
+        plt.legend()
+        st.pyplot()
+
+# Comparison button
+if st.button("Compare Models"):
+    # Load and forecast with ARIMA
+    model_filename_arima = os.path.join(arima_models_folder, f'arima_model_product_{selected_product}.pkl')
+    with open(model_filename_arima, 'rb') as file:
+        arima_model = pickle.load(file)
+
+    # Prepare input data for ARIMA (you may need to adjust this based on your data structure)
+    product_sales_series = weekly_sales[weekly_sales['StockCode'] == selected_product].set_index(['Year', 'Week'])['Quantity']
+    arima_result = ARIMA(product_sales_series, order=(1, 1, 1)).fit()
+    st.session_state.arima_forecast = arima_result.forecast(steps=15)
+
+    # Load and forecast with Decision Tree
+    dt_model_filename = os.path.join(dt_models_folder, f'decision_tree_model_product_{selected_product}.pkl')
+    with open(dt_model_filename, 'rb') as file:
+            dt_model = pickle.load(file)
+
+    # Prepare input data for Decision Tree
+    future_data = np.array([[2023, week] for week in range(40, 55)])  # Adjust weeks based on your need
+    st.session_state.dt_forecast = dt_model.predict(future_data)
+
+    # Load and forecast with XGBoost
+    xgb_model_filename = os.path.join(xgb_models_folder, f'xgb_model_product_{selected_product}.pkl')
+    with open(xgb_model_filename, 'rb') as file:
+            xgb_model = pickle.load(file)
+
+    # Forecast using XGBoost
+    st.session_state.xgb_forecast = xgb_model.predict(future_data)
+
+    # Now plot all the forecasts
+    plt.figure(figsize=(10, 6))
+
+    # ARIMA forecast
+    if st.session_state.arima_forecast is not None:
+        plt.plot(st.session_state.arima_forecast, label='ARIMA Forecast', color='blue')
+
+    # Decision Tree forecast
+    if st.session_state.dt_forecast is not None:
+        plt.plot(st.session_state.dt_forecast, label='Decision Tree Forecast', color='green')
+
+    # XGBoost forecast
+    if st.session_state.xgb_forecast is not None:
+        plt.plot(st.session_state.xgb_forecast, label='XGBoost Forecast', color='orange')
+
+    plt.title(f'Comparison of Forecasts for Product {selected_product}')
+    plt.xlabel('Weeks')
+    plt.ylabel('Sales Quantity')
+    plt.legend()
+    st.pyplot()
+