Upload app.py

app.py

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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!
+    '''
+             )
+