added my project files

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__))
+
+# Load the model from the pickle file
+model_path = os.path.join(current_dir, 'model.pkl')
+model = joblib.load(model_path)
+
+# Load the scaler from the pickle file
+encoder_path = os.path.join(current_dir, 'encoder.pkl')
+encoder = joblib.load(encoder_path)
+
+
+# Set Page Configurations
+st.set_page_config(page_title="Sales Prediction App", page_icon="fas fa-chart-line", layout="wide", initial_sidebar_state="auto")
+
+# Set up sidebar
+st.sidebar.header('Navigation')
+menu = ['Home', 'About']
+choice = st.sidebar.selectbox("Select an option", menu)
+
+# predict function
+def predict(sales_data):
+    if sales_data.empty:
+        raise ValueError("No sales data provided.")
+
+    else:
+        # Perform the necessary data processing steps
+        sales_data = getDateFeatures(sales_data).set_index('date')
+        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']
+        
+        num = sales_data[numeric_columns]
+        encoded_cat = encoder.transform(sales_data[categoric_columns])
+        sales_data = pd.concat([num, encoded_cat], axis=1)
+    
+        # Make predictions using the pre-trained model
+        predicted_sales = model.predict(sales_data)
+    
+        return predicted_sales
+
+# Home section
+if choice == 'Home':
+    # Set Page Title
+    st.title('SEER- A Sales Forecasting APP')
+    
+    # Loading GIF
+    gif_url = "https://i.gifer.com/7BzK.gif"
+    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)
+    
+    st.markdown('Enter the required information to forecast sales:')
+
+    # defining input parameters
+    Store_type = ["Supermarket", "Department Store", "Convenience Store", "Pharmacy", "Clothing Store", "Electronics Store", "Furniture Store", "Hardware Store", "Bookstore", "Jewelry Store", "Toy Store", "Pet Store", "Sporting Goods Store", "Shoe Store", "Dollar Store", "Liquor Store", "Beauty Supply Store", "Home Improvement Store", "Stationery Store", "Gift Shop", "Bakery", "Butcher Shop", "Fish Market", "Vegetable Market", "Farmers Market", "Coffee Shop", "Café", "Restaurant", "Fast Food Restaurant", "Pizza Place", "Burger Joint", "Ice Cream Shop", "Food Truck", "Bar", "Pub", "Nightclub", "Gas Station", "Car Dealership", "Auto Repair Shop", "Car Wash", "Bank", "ATM", "Post Office", "Laundry", "Hair Salon", "Nail Salon", "Spa", "Gym", "Yoga Studio", "Movie Theater", "Bowling Alley", "Arcade", "Museum", "Art Gallery"]
+    Store_id = ['Store_' + str(i) for i in range(0, 5)]
+    cities = ["Lagos", "Abuja", "Kano", "Ibadan", "Kaduna", "Port Harcourt", "Benin City", "Maiduguri", "Zaria", "Aba", "Jos", "Ilorin", "Oyo", "Enugu", "Abeokuta", "Onitsha", "Warri", "Sokoto", "Calabar", "Katsina", "Akure", "Bauchi"]
+    clusters = ["Fashion", "Electronics", "Supermarket", "Home Improvement", "Department Store","Pharmacy", "Furniture", "Sports Goods", "Jewelry", "Cosmetics", "Automotive","Bookstore", "Toy Store", "Pet Store", "Convenience Store", "Hardware Store","Outdoor Recreation"]
+    categories = ["Apparel", "Beauty", "Books", "Electronics", "Furniture", "Grocery", "Health", "Home", "Jewelry", "Kitchen", "Music", "Office", "Outdoors", "Pets", "Shoes", "Sports", "Toys", "Automotive", "Baby", "Computers", "Garden", "Movies", "Tools", "Watches", "Appliances", "Cameras", "Fitness", "Industrial", "Luggage", "Software", "Video Games", "Cell Phones", "Home Improvement"]
+    
+    # Input form
+    col1, col2 = st.columns(2)
+
+    with col1:
+        start_date = st.date_input("Start Date")
+        # Convert the date to datetime format
+        start_date = pd.to_datetime(start_date)
+        end_date = st.date_input("End Date")
+        # Convert the date to datetime format
+        end_date = pd.to_datetime(end_date)
+        onpromotion = st.number_input("How many products are on promotion?", min_value=0, step=1)
+        selected_category = st.selectbox("Product_Category", categories)
+
+
+    with col2:
+        selected_store = st.selectbox("Store_type", Store_type)
+        selected_store1 = st.selectbox("Store_id", Store_id)
+        selected_city = st.selectbox("City", cities)
+        selected_cluster = st.selectbox("Cluster", clusters)
+
+    predicted_data = pd.DataFrame(columns=['Start Date', 'End Date', 'Store', 'Category', 'On Promotion', 'City', 'Cluster', 'Predicted Sales'])
+
+
+    if st.button('Predict'):
+        if start_date > end_date:
+            st.error("Start date should be earlier than the end date.")
+        else:
+            with st.spinner('Predicting sales...'):
+                sales_data = pd.DataFrame({
+                    'date': pd.date_range(start=start_date, end=end_date),
+                    'store_id': [selected_store] * len(pd.date_range(start=start_date, end=end_date)),
+                    'category_id': [selected_category] * len(pd.date_range(start=start_date, end=end_date)),
+                    'onpromotion': [onpromotion] * len(pd.date_range(start=start_date, end=end_date)),
+                    'city': [selected_city] * len(pd.date_range(start=start_date, end=end_date)),
+                    'store_type': [selected_store1] * len(pd.date_range(start=start_date, end=end_date)),
+                    'cluster': [selected_cluster] * len(pd.date_range(start=start_date, end=end_date))
+                })
+                try:
+                    sales = predict(sales_data)
+                    formatted_sales = round(sales[0], 2)
+                    predicted_data = predicted_data.append({
+                         'Start Date': start_date,
+                         'End Date': end_date,
+                         'Store': selected_store,
+                         'Category': selected_category,
+                         'On Promotion': onpromotion,
+                         'City': selected_city,
+                         'Cluster': selected_cluster,
+                         'Predicted Sales': formatted_sales}, ignore_index=True)
+
+                    st.success(f"Total sales for the period is: #{formatted_sales}")
+                except ValueError as e:
+                    st.error(str(e))
+                    
+    if st.button('Clear Data'):
+        predicted_data = pd.DataFrame(columns=['Start Date', 'End Date', 'Store', 'Category', 'On Promotion', 'City', 'Cluster', 'Predicted Sales'])
+        st.success("Data cleared successfully.")
+
+
+
+# About section
+elif choice == 'About':
+    # Load the banner image
+    banner_image_url = "https://guardian.ng/wp-content/uploads/2017/03/Sales-targets.jpg"
+ 
+    # Display the banner image
+    st.image(
+        banner_image_url, 
+        use_column_width=True, 
+        width=400)
+    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)

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

encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:22a1fc015481505b761fab9debd936f8fcd598aefcedd76ada7cf26ad09709ef
+size 16541

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:11ce0047dc890be988faa21eb1054703f2abd0afd4ce7159f9975564a943323f
+size 6544838

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