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| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import joblib | |
| #import datasets | |
| #pickle_in = open("./Regressor.pkl","rb") | |
| #pickle_in = "pracheeeeez/Sales_Prediction/Regressor.pkl" | |
| # Regressor=pickle.load(pickle_in) | |
| #pickle_in = open("C:\Users\PRACHI\OneDrive\Documents\ML\Regressor.pkl","rb") | |
| #Regressor = datasets.load_from_disk(f"spaces://{pickle_in}") | |
| Regressor = joblib.load("Regressor.pkl") | |
| def predict_sales(Item_Identifier,Item_Weight, Item_Fat_Content,Item_Visibility,Item_Type,Item_MRP,Outlet_Identifier,Outlet_Establishment_Year,Outlet_Size,Outlet_Location_Type,Outlet_Type): | |
| prediction= Regressor.predict([[Item_Identifier,Item_Weight, Item_Fat_Content,Item_Visibility,Item_Type,Item_MRP,Outlet_Identifier,Outlet_Establishment_Year,Outlet_Size,Outlet_Location_Type,Outlet_Type]]) | |
| print(prediction) | |
| return prediction | |
| def main(): | |
| st.title("Mart Sales Predictor") | |
| html_temp = """ | |
| <div style="background-color:tomato;padding:10px"> | |
| <h2 style="color:white;text-align:center;">Streamlit Sales Predictor ML App </h2> | |
| </div> | |
| """ | |
| st.markdown(html_temp,unsafe_allow_html=True) | |
| Item_Identifier = st.text_input("Item_Identifier","Type Here") | |
| Item_Weight = st.text_input("Item_Weight","Type Here") | |
| Item_Fat_Content = st.text_input("Item_Fat_Content","Type Here") | |
| Item_Visibility = st.text_input("Item_Visibility","Type Here") | |
| Item_Type = st.text_input("Item_Type","Type Here") | |
| Item_MRP = st.text_input("Item_MRP","Type Here") | |
| Outlet_Identifier = st.text_input("Outlet_Identifier","Type Here") | |
| Outlet_Establishment_Year = st.text_input("Outlet_Establishment_Year","Type Here") | |
| Outlet_Size = st.text_input("Outlet_Size","Type Here") | |
| Outlet_Location_Type = st.text_input("Outlet_Location_Type","Type Here") | |
| Outlet_Type = st.text_input("Outlet_Type","Type Here") | |
| result="" | |
| if st.button("Predict"): | |
| result=predict_sales(Item_Identifier,Item_Weight, Item_Fat_Content,Item_Visibility,Item_Type,Item_MRP,Outlet_Identifier,Outlet_Establishment_Year,Outlet_Size,Outlet_Location_Type,Outlet_Type) | |
| st.success('The output is {}'.format(result)) | |
| if st.button("About"): | |
| st.text("Lets LEarn") | |
| st.text("Built with Streamlit") | |
| if __name__=='__main__': | |
| main() | |