test_app.py

# import streamlit as st
# import pandas as pd
# import numpy as np
# from sklearn.preprocessing import LabelEncoder
# import joblib

# # Load your trained model
# model = joblib.load('models\model1.pkl')



# # Function to predict sales
# def predict_sales(input_data):
#     sales_prediction = model.predict(input_data)
#     return sales_prediction



# # Streamlit app
# def main():
#     st.title('Sales Prediction App')

#     # Input widgets
#     PromoInterval = st.selectbox("Promo Interval", ['No Promotion', 'Jan,Apr,Jul,Oct', 'Feb,May,Aug,Nov', 'Mar,Jun,Sept,Dec'])
#     StoreType = st.radio("StoreType", ["a", "b", "c", "d"])
#     Assortment = st.radio("Assortment", ["a", "b", "c"])
#     StateHoliday = st.radio("State Holiday", ["1", "0"])
#     SchoolHoliday = st.radio("School Holiday", ["1", "0"])
#     Promo = st.radio("Promo", ["1", "0"])
#     Store = st.slider("Store", 1, 1115)
#     Customers = st.slider("Customers", 0, 7388)
#     CompetitionDistance = st.slider("Competition Distance", 20, 75860)
#     CompetitionOpenSinceMonth = st.slider("Competition Open Since Month", 1, 12)
#     CompetitionOpenSinceYear = st.slider("Competition Open Since Year", 1998, 2015)

#    	# PromoInterval	StoreType	Assortment	StateHoliday	Store	Customers	Promo	SchoolHoliday	CompetitionDistance	CompetitionOpenSinceMonth	CompetitionOpenSinceYear
#     # Store user inputs
#     input_data = pd.DataFrame({
#         'PromoInterval': [PromoInterval],
#         'StoreType': [StoreType],
#         'Assortment': [Assortment],
#         'StateHoliday': [StateHoliday],
#         'Store': [Store],

#         'Customers': [Customers],
#         'Promo': [Promo],

#         'SchoolHoliday': [SchoolHoliday],
#         'CompetitionDistance': [CompetitionDistance],
#         'CompetitionOpenSinceMonth': [CompetitionOpenSinceMonth],
#         'CompetitionOpenSinceYear': [CompetitionOpenSinceYear]
#     })

#     # Display input data
#     st.subheader('Input Data:')
#     st.write(input_data)
    
#     # Predict sales
#     if st.button('Predict Sales'):
#         prediction = predict_sales(input_data)
#         st.write('Predicted Sales:', prediction)

# if __name__ == '__main__':
#     main()

# import streamlit as st
# import pandas as pd
# import numpy as np
# from sklearn.preprocessing import LabelEncoder
# import joblib

# # Load your trained model
# model = joblib.load('models\model1.pkl')

# # Function to predict sales
# def predict_sales(input_data):
#     sales_prediction = model.predict(input_data)
#     return sales_prediction

# # Function for data analysis
# def perform_analysis(data):
#     # Add your analysis code here
#     st.subheader("Analysis")
#     st.write("Performing analysis...")

# # Function for data overview
# def show_data_overview():
#     # Load data from CSV file
#     data = pd.read_csv('Dataset/rossmann.csv')
    
#     # Display data overview
#     st.subheader("Data Overview")
#     st.write(data)

# # Streamlit app
# def main():
#     st.title('Sales Prediction App')

#     # Sidebar options
#     sidebar_option = st.sidebar.radio("Navigation", ["Main", "Analysis", "Data Overview"])

#     if sidebar_option == "Main":
#         # Input widgets
#         PromoInterval = st.selectbox("Promo Interval", ['No Promotion', 'Jan,Apr,Jul,Oct', 'Feb,May,Aug,Nov', 'Mar,Jun,Sept,Dec'])
#         StoreType = st.radio("StoreType", ["a", "b", "c", "d"])
#         Assortment = st.radio("Assortment", ["a", "b", "c"])
#         # StateHoliday = st.radio("State Holiday", ["1", "0"])

#         # -------------------------------------------------------------------------------

#         # Define the options and their corresponding labels
#         options = ["1", "0"]
#         labels = ["Yes", "No"]

#         # Create the radio button with labels
#         StateHoliday = st.radio("State Holiday", labels)

#         # Convert the selected label back to its corresponding option value
#         # selected_option = options[labels.index(state_holiday)]
#         # -------------------------------------------------------------------------------

#         SchoolHoliday = st.radio("School Holiday", ["1", "0"])
#         Promo = st.radio("promotion", ["1", "0"])
#         Store = st.slider("Store", 1, 1115)
#         Customers = st.slider("Customers", 0, 7388)
#         CompetitionDistance = st.slider("Competition Distance", 20, 75860)
#         CompetitionOpenSinceMonth = st.slider("Competition Open Since Month", 1, 12)
#         CompetitionOpenSinceYear = st.slider("Competition Open Since Year", 1998, 2015)

#         # Store user inputs
#         input_data = pd.DataFrame({
#             'PromoInterval': [PromoInterval],
#             'StoreType': [StoreType],
#             'Assortment': [Assortment],
#             'StateHoliday': [StateHoliday],
#             'Store': [Store],

#             'Customers': [Customers],
#             'Promo': [Promo],

#             'SchoolHoliday': [SchoolHoliday],
#             'CompetitionDistance': [CompetitionDistance],
#             'CompetitionOpenSinceMonth': [CompetitionOpenSinceMonth],
#             'CompetitionOpenSinceYear': [CompetitionOpenSinceYear]
#         })

#         # Display input data
#         st.subheader('Input Data:')
#         st.write(input_data)
        
#         # Predict sales
#         if st.button('Predict Sales'):
#             prediction = predict_sales(input_data)
#             st.write('Predicted Sales:', prediction)

#     elif sidebar_option == "Analysis":
#         perform_analysis(None)  # Pass data for analysis if needed

#     elif sidebar_option == "Data Overview":
#         show_data_overview()  # Pass data for overview if needed

# if __name__ == '__main__':
#     main()

import streamlit as st
import pandas as pd
import joblib

# Load your trained model
model = joblib.load('models\model2.pkl')

# Function to predict sales
def predict_sales(input_data):
    # Make predictions using the loaded model
    sales_prediction = model.predict(input_data)
    return sales_prediction

# Streamlit app
def main():
    st.title('Sales Prediction App')

    # Input widgets
    PromoInterval = st.selectbox("Promo Interval", ['No Promotion', 'Jan,Apr,Jul,Oct', 'Feb,May,Aug,Nov', 'Mar,Jun,Sept,Dec'])

    # -----------------------------------------------------------------------------------------------
    StoreType = st.radio("StoreType", ["Small Shop", "Medium Store", "Large Store", "Hypermarket"])
    Assortment = st.radio("Assortment", ["basic", "extra", "extended"])
    

    # Encode StateHoliday as 1 for 'Yes' and 0 for 'No' --------------------------------------
    StateHoliday = st.radio("State Holiday", ["Yes", "No"])
    StateHoliday = 1 if StateHoliday == "Yes" else 0

    SchoolHoliday = st.radio("School Holiday", ["Yes", "No"])
    SchoolHoliday = 1 if SchoolHoliday == "Yes" else 0

    Promo = st.radio("Promotion", ["store is participating", "store is not participating"])
    Promo = 1 if Promo == "store is participating" else 0
    # ----------------------------------------------------------------------------------------
    

    Store = st.slider("Store", 1, 1115)
    Customers = st.slider("Customers", 0, 7388)
    CompetitionDistance = st.slider("Competition Distance", 20, 75860)
    CompetitionOpenSinceMonth = st.slider("Competition Open Since Month", 1, 12)
    CompetitionOpenSinceYear = st.slider("Competition Open Since Year", 1998, 2015)
    # ----------------------------------------------------------------------------------------

    # Store user inputs
    input_data = pd.DataFrame({
        'PromoInterval': [PromoInterval],
        'StoreType': [StoreType],
        'Assortment': [Assortment],
        'StateHoliday': [StateHoliday],
        'Store': [Store],
        'Customers': [Customers],
        'Promo': [Promo],
        'SchoolHoliday': [SchoolHoliday],
        'CompetitionDistance': [CompetitionDistance],
        'CompetitionOpenSinceMonth': [CompetitionOpenSinceMonth],
        'CompetitionOpenSinceYear': [CompetitionOpenSinceYear]
    })

    # Display input data
    st.subheader('Input Data:')
    st.write(input_data)

    # Predict sales
    # if st.button('Predict Sales'):
    #     prediction = predict_sales(input_data)
    #     st.write('Predicted Sales:', prediction)

    if st.button('Predict Sales'):
        prediction = predict_sales(input_data)[0]
        formatted_prediction = "{:.2f}".format(prediction)  # Format prediction to display two decimal points
        st.write('Predicted Sales:', formatted_prediction)


if __name__ == '__main__':
    main()



# Record at index 795018:
# PromoInterval                Jan,Apr,Jul,Oct
# StoreType                         Small Shop
# Assortment                             basic
# StateHoliday                               0
# Store                                    650
# Customers                                636
# Promo                                      1
# SchoolHoliday                              0
# CompetitionDistance                     1420
# CompetitionOpenSinceMonth                 10
# CompetitionOpenSinceYear                2012
# Sales                                   6322
# Name: 795018, dtype: object