File size: 1,627 Bytes
f1e0278 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
import streamlit as st
import numpy as np
import pandas as pd
import joblib
from datetime import date
from prophet.plot import plot_plotly, plot_components
##loading my model again
model= joblib.load("models/fbpmodel.joblib")
##loading my test data
test=pd.read_csv("dataframes/test.csv")
test=test.drop(["holiday", "locale", "transferred"], axis= 1)
result= model.predict(test)
##creating my web page
st.set_page_config(page_title="Favorita Stores Sales Prediction App",layout="centered")
#Image loader
st.image("src\screenshots\hour_glass_2.jpg")
##adding my title
st.title("Favorita Store Sales Prediction APP with Facebook Prophet")
##adding my description
st.markdown("Welcome to the sales prediction app for Favorita stores!")
df_ori= pd.read_csv("dataframes/original_dataframe.csv")
df_ori= df_ori.set_index("date")
st.subheader("A Chart of the Daily Sales Across Favorita Stores")
st.line_chart(df_ori["sales"])
##defining my inputs
st.header("Make a Forecast Here: ")
ds= st.date_input(label= "Please enter the date you want to forecast")
transactions= st.number_input(label= "Please enter the total number of expected transactions")
onpromotion= st.number_input(label= "Please enter the total number of expected items to be on promotions")
##creating a dataframe for my inputs
input_data= [ds, onpromotion, transactions]
inputs= pd.DataFrame([input_data], columns=["ds", "onpromotion", "transactions"])
forecast= model.predict(inputs)
##creating an output for my output
st.header("Your Prediction is Displayed Below: ")
##telling my model to return the yhat of my input
st.write(forecast["yhat"])
|