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| # import pandas as pd | |
| # import numpy as np | |
| import streamlit as st | |
| # import os | |
| # import time | |
| # import pickle | |
| # import seaborn as sns | |
| # import matplotlib.pyplot as plt | |
| # import pip | |
| try: | |
| #insert headers | |
| st.header(" Welcome to Sales Prediction Using Prophet ") | |
| st.subheader("To help you know your future sales📈...") | |
| st.image("future.png", width=500, caption="Sales Prediction") | |
| Disp_results = pd.DataFrame() # Initialize for download | |
| # Take input | |
| with st.form("This form", clear_on_submit=True): | |
| st.subheader("Enter the number of day(s)/Week(s) you want to predict, And the frequency as D for Daily or W for weekly ") | |
| frequency = str(st.text_input("Frequency 'D' for Daily 'W' for weekly ")).upper() # convert to string and change to upper | |
| Number_of_days = int(st.number_input("Number of day(s)/Week(s)")) # convert to int | |
| submit = st.form_submit_button("Predict your sales") | |
| # process the input | |
| if submit: | |
| # check if we have the right data type | |
| if frequency == "D" or frequency == 'W': | |
| st.success("Inputs received successfully ✅") | |
| # import model | |
| with open('prophet_model.pkl', 'rb') as f: | |
| model = pickle.load(f) | |
| # pass inputs to the model(To make predictions, prophet requires number of days and frequency) | |
| future = model.make_future_dataframe(periods=Number_of_days, freq=str(frequency), include_history=False) | |
| # Make prediction | |
| forecast = model.predict(future) | |
| # show results | |
| print(f'[INFO]: The whole results {forecast}') | |
| # pick the relevant columns from the forecast | |
| sales_forecast = forecast[['ds', 'yhat_lower', 'yhat_upper', 'yhat']] | |
| # rename the columns | |
| Disp_results = sales_forecast.rename(columns={'ds': 'Date', 'yhat_lower': 'lowest Expected sales', 'yhat_upper': 'Highest Expected Sales', 'yhat': 'Expected Sales'}) | |
| # print result dataframe to terminal | |
| print(f'[INFO]: results dataframe {Disp_results}') | |
| # show progress | |
| with st.spinner("Prediction in progress..."): | |
| time.sleep(2) | |
| st.balloons() | |
| st.success("Great✅") | |
| # Display results | |
| if frequency == "W": | |
| output_frequency = 'Week(s)' | |
| else: | |
| output_frequency = 'Day(s)' | |
| # Check frequency | |
| st.write(f"These are your predicted sales in the next {Number_of_days} {output_frequency}") | |
| st.dataframe(Disp_results) | |
| # Display the graph of sales | |
| st.title(f"Line Graph Of Predicted Sales Over {Number_of_days} {output_frequency} ") | |
| # Line Graph | |
| st.line_chart(data=Disp_results, x='Date', y='Expected Sales') | |
| print('[INFO]: Line Chart displayed') | |
| else: | |
| st.error("Input the right frequency or Days ⚠") | |
| # Print input to the terminal | |
| print(f'[INFO]: These are the inputs to the model {Number_of_days},{frequency}') | |
| print(f"[INFO]: Inputs received") | |
| # Create a function to convert df to csv | |
| def convert_to_csv(df): | |
| return df.to_csv() | |
| # Create an expander | |
| expand = st.expander('Download Results as CSV') | |
| with expand: | |
| st.download_button( | |
| 'Download results', | |
| convert_to_csv(Disp_results), | |
| 'prediction_results.csv', | |
| 'text/csv', | |
| 'download' | |
| ) | |
| # Create Sidebar for Description | |
| sidebar = st.sidebar.title('Sales Prediction') | |
| # first option | |
| option1 = st.sidebar.button('About', key="About") | |
| # second option | |
| option2 = st.sidebar.button('About the sales prediction', key="sales prediction") | |
| # Display text for a selected option | |
| if option1: | |
| st.sidebar.write('This is a Sales prediction app Using Prophet(Developed by meta), this project was done under the Azubi Africa Data Analysis Training program ') | |
| elif option2: | |
| st.sidebar.write('This is a time series analysis & forecasting problem. In this project, we shalll predict store sales on data from Corporation Favorita, a large Ecuadorian-based grocery retailer. Specifically, this app predicts the sales for up to weeks in advance for Corporation Favorita ') | |
| except: | |
| st.error('''something went wrong: Make sure you entered the correct number of days | |
| otherwise contact admin! | |
| ''' | |
| ) | |