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app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from PIL import Image
+import requests
+from bokeh.plotting import figure
+from bokeh.models import HoverTool
+import joblib
+import os
+from date_features import getDateFeatures
+
+# Get the current directory path
+current_dir = os.path.dirname(os.path.abspath(__file__))
+
+# Define path for the model and encoder from the pickle file
+assets_dir = os.path.abspath(os.path.join(current_dir, "../../assets/ML components"))
+model_path = os.path.join(assets_dir, 'model.pkl')
+encoder_path = os.path.join(assets_dir, 'encoder.pkl')
+# model_path = os.path.join(current_dir, 'model.pkl')
+# encoder_path = os.path.join(current_dir, 'encoder.pkl')
+
+# Load the model and encoder from the pickle file
+model = joblib.load(model_path)
+encoder = joblib.load(encoder_path)
+
+# Set Page Configurations
+st.set_page_config(page_title="ETA Prediction App", page_icon="fas fa-chart-line", layout="wide", initial_sidebar_state="auto")
+
+# Loading GIF
+gif_url = "https://raw.githubusercontent.com/Gilbert-B/Forecasting-Sales/main/app/salesgif.gif"
+
+# Set up sidebar
+st.sidebar.header('Navigation')
+menu = ['Home', 'About']
+choice = st.sidebar.selectbox("Select an option", menu)
+
+def predict(sales_data):
+    sales_data = getDateFeatures(sales_data).set_index('date')
+    # print(sales_data.columns)
+
+    # Make predictions for the next 8 weeks
+    prediction_inputs = []  # Initialize the list for prediction inputs
+
+    # Encode the prediction inputs
+    # numeric_columns = sales_data.select_dtypes(include=['int64', 'float64']).columns.tolist()
+    numeric_columns = ['onpromotion', 'year', 'month', 'dayofmonth', 'dayofweek', 'dayofyear', 'weekofyear', 'quarter', 'year_weekofyear', 'sin(dayofyear)', 'cos(dayofyear)']
+    categoric_columns = ['store_id','category_id','city','store_type','cluster','holiday_type','is_holiday','is_month_start','is_month_end','is_quarter_start','is_quarter_end','is_year_start','is_year_end','is_weekend', 'season'] 
+    print(categoric_columns)
+    # encoder = BinaryEncoder(drop_invariant=False, return_df=True,)
+    # encoder.fit(sales_data[categoric_columns]) 
+    num = sales_data[numeric_columns]
+    encoded_cat = encoder.transform(sales_data[categoric_columns])
+    sales_data = pd.concat([num, encoded_cat], axis=1)
+    
+    # Make the prediction using the loaded machine learning model
+    predicted_sales = model.predict(sales_data)
+
+    return predicted_sales
+
+# Home section
+if choice == 'Home':
+    st.image(gif_url, use_column_width=True)
+    st.markdown("<h1 style='text-align: center;'>Welcome</h1>", unsafe_allow_html=True)
+    st.markdown("<p style='text-align: center;'>This is a Sales Forecasting App.</p>", unsafe_allow_html=True)
+    
+    # Set Page Title
+    st.title('SEER- A Sales Forecasting APP')
+    st.markdown('Enter the required information to forecast sales:')
+
+    
+    # Input form
+    col1, col2 = st.columns(2)
+
+    Stores = ['Store_' + str(i) for i in range(1, 55)]
+    Stores1 = ['Store_' + str(i) for i in range(0, 5)]
+    cities = ['city_' + str(i) for i in range(22)]
+    clusters = ['cluster_' + str(i) for i in range(17)]
+    categories = ['Category_' + str(i) for i in range(33)]
+
+    with col1:
+        date = st.date_input("Date")
+        # Convert the date to datetime format
+        date = pd.to_datetime(date)
+        onpromotion = st.number_input("How many products are on promotion?", min_value=0, step=1)
+        selected_category = st.selectbox("Category", categories)
+
+
+    with col2:
+        selected_store = st.selectbox("Store_type", Stores)
+        selected_store1 = st.selectbox("Store_id", Stores1)
+        selected_city = st.selectbox("City", cities)
+        selected_cluster = st.selectbox("Cluster", clusters)
+
+    # Call getDateFeatures() function on sales_data (replace sales_data with your DataFrame)
+    sales_data = pd.DataFrame({
+        'date': [date],
+        'store_id': [selected_store],
+        'category_id': [selected_category],
+        'onpromotion': [onpromotion],
+        'city' :[selected_city],
+        'store_type': [selected_store1],
+        'cluster':[selected_cluster]
+        })
+    print(sales_data)
+    print(sales_data.info())
+
+
+    if st.button('Predict'):
+        sales = predict(sales_data)
+        formatted_sales = round(sales[0], 2)
+        st.write(f"Total sales for this week is: #{formatted_sales}")
+
+
+    # # Display the forecast results
+    # st.subheader("Sales Forecast for the Next 8 Weeks:")
+    # for week, sales in enumerate(predicted_sales, start=1):
+    #     st.write(f"Week {week}: {sales:.2f} units")
+
+    # # Update the line chart
+    # chart_data = pd.DataFrame({'Week': range(1, 9), 'Sales': predicted_sales})
+    # p = figure(plot_width=600, plot_height=400, title="Sales Forecast",
+    #            x_axis_label="Week", y_axis_label="Sales")
+
+    # p.line(chart_data['Week'], chart_data['Sales'], line_width=2)
+    # p.circle(chart_data['Week'], chart_data['Sales'], fill_color="white", size=6)
+    # p.add_tools(HoverTool(tooltips=[("Week", "@x"), ("Sales", "@y")]))
+    # st.bokeh_chart(p)
+
+# About section
+elif choice == 'About':
+    # Load the banner image
+    banner_image_url = "https://raw.githubusercontent.com/Gilbert-B/Forecasting-Sales/0d7b869515bysBoi5XxNGa3hayALLn9BK1VQqD69Dc/app/seer.png"
+    banner_image = Image.open(requests.get(banner_image_url, stream=True).raw)
+
+    # Display the banner image
+    st.image(banner_image, use_column_width=True)
+    st.markdown('''
+            <p style='font-size: 20px; font-style: italic;font-style: bold;'>
+            SEER is a powerful tool designed to assist businesses in making accurate 
+            and data-driven sales predictions. By leveraging advanced algorithms and 
+            machine learning techniques, our app provides businesses with valuable insights 
+            into future sales trends. With just a few input parameters, such as distance and 
+            average speed, our app generates reliable sales forecasts, enabling businesses
+            to optimize their inventory management, production planning, and resource allocation. 
+            The user-friendly interface and intuitive design make it easy for users to navigate 
+            and obtain actionable predictions. With our Sales Forecasting App, 
+            businesses can make informed decisions, mitigate risks, 
+            and maximize their revenue potential in an ever-changing market landscape.
+            </p>
+            ''', unsafe_allow_html=True)
+    st.markdown("<p style='text-align: center;'>This Sales Forecasting App is developed using Streamlit and Python.</p>", unsafe_allow_html=True)
+    st.markdown("<p style='text-align: center;'>It demonstrates how machine learning can be used to predict sales for the next 8 weeks based on historical data.</p>", unsafe_allow_html=True)

date_features.py

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+import numpy as np
+
+# Define the getDateFeatures() function
+def getDateFeatures(df):
+    df['holiday_type'] = 'Workday'
+    df['is_holiday'] = False
+    
+    df['year'] = df['date'].dt.year
+    df['month'] = df['date'].dt.month
+    df['dayofmonth'] = df['date'].dt.day
+    df['dayofweek'] = df['date'].dt.dayofweek
+    df['weekofyear'] = df['date'].dt.weekofyear
+
+    df['quarter'] = df['date'].dt.quarter
+    df['is_month_start'] = df['date'].dt.is_month_start.astype(int)
+    df['is_month_end'] = df['date'].dt.is_month_end.astype(int)
+    df['is_quarter_start'] = df['date'].dt.is_quarter_start.astype(int)
+    
+    df['is_quarter_end'] = df['date'].dt.is_quarter_end.astype(int)
+    df['is_year_start'] = df['date'].dt.is_year_start.astype(int)
+    df['is_year_end'] = df['date'].dt.is_year_end.astype(int)
+    # Extract the 'year' and 'weekofyear' components from the 'date' column
+    df['year_weekofyear'] = df['date'].dt.year * 100 + df['date'].dt.weekofyear
+
+    # create new coolumns to represent the cyclic nature of a year
+    df['dayofyear'] = df['date'].dt.dayofyear
+    df["sin(dayofyear)"] = np.sin(df["dayofyear"])
+    df["cos(dayofyear)"] = np.cos(df["dayofyear"])
+
+    df["is_weekend"] = np.where(df['dayofweek'] > 4, 1, 0)
+
+    # Define the criteria for each season
+    seasons = {'Winter': [12, 1, 2], 'Spring': [3, 4, 5], 'Summer': [6, 7, 8], 'Autumn': [9, 10, 11]}
+
+    # Create the 'season' column based on the 'date' column
+    df['season'] = df['month'].map({month: season for season, months in seasons.items() for month in months})
+    
+
+    return df

requirements.txt

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+streamlit
+pandas==1.5.3
+numpy==1.24.2
+pillow 
+requests 
+bokeh 
+scikit-learn==1.2.2
+category_encoders