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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!
'''
)
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