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 pickle from date_features import getDateFeatures # Define path for the model and encoder from the pickle file model = pickle.load(open("model.pkl", "rb")) encoder = pickle.load(open("encoder.pkl", "rb")) # Load the model and encoder from the Hugging Face assets folder model = torch.hub.load(assets_dir, 'model') encoder = torch.hub.load(assets_dir, 'encoder') # 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("

Welcome

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This is a Sales Forecasting App.

", 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('''

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.

''', unsafe_allow_html=True) st.markdown("

This Sales Forecasting App is developed using Streamlit and Python.

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It demonstrates how machine learning can be used to predict sales for the next 8 weeks based on historical data.

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