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| import streamlit as st | |
| import joblib | |
| import numpy as np | |
| # load model | |
| loaded_model = joblib.load('model.pkl') | |
| def generate_prediction(input_array): | |
| ans = loaded_model.predict(input_array) | |
| return ans | |
| def main(): | |
| # Face Analysis Application # | |
| st.title("Online Food Order Prediction") | |
| activiteis = ["Home", "Prediction","About"] | |
| choice = st.sidebar.selectbox("Select Activity", activiteis) | |
| if choice == "Home": | |
| html_temp_home1 = """<div style="background-color:#6D7B8D;padding:10px"> | |
| <h3 style="color:yellow;text-align:center;"> Welcome to world of AI with Prince </h3> | |
| <h4 style="color:white;text-align:center;"> | |
| Online Food Order Prediction using Python.</h4> | |
| </div> | |
| </br>""" | |
| st.markdown(html_temp_home1, unsafe_allow_html=True) | |
| st.write(""" | |
| Online Food Order Prediction | |
| """) | |
| if choice == "Prediction": | |
| st.header("Online Food Order Prediction") | |
| # Define the input fields | |
| age = st.number_input("Age", min_value=0, max_value=120, value=30, step=1) | |
| income = st.number_input("Income", min_value=0, max_value=1000000, value=50000, step=1000) | |
| family_size = st.number_input("Family Size", min_value=1, max_value=10, value=4, step=1) | |
| pin = st.number_input("Pin", min_value=100000, max_value=999999, value=500000, step=1) | |
| gender = { "Male" :1,"Female" : 2} | |
| Gender_index = st.selectbox("Gender", options=list(gender.keys())) | |
| Gender = gender[Gender_index] | |
| Mirrage = {"Single" : 1, "Married": 2,"Not Revealed" : 3} | |
| Marital_index = st.selectbox("Marital Status", options=list(Mirrage.keys())) | |
| Marital_status = Mirrage[Marital_index] | |
| occupation_dict = {"Student" :1, "Employee" : 2, "Self Employeed" : 3, "House wife" : 4} | |
| occupation_index = st.selectbox("Marital Status", options=list(occupation_dict.keys())) | |
| occupation = occupation_dict[occupation_index] | |
| educational_level = {"Graduate": 1, "Post Graduate":2, "Ph.D":3, "School" :4, "Uneducated" :5} | |
| educational_index = st.selectbox("educational_level", options=list(educational_level.keys())) | |
| education = educational_level[educational_index] | |
| Review_dict = {"Positive": 1, "Negative": 0} | |
| Review_index = st.selectbox("Review", options=list(Review_dict.keys())) | |
| Review = Review_dict[Review_index] | |
| # Create a button to trigger the model | |
| if st.button("Predict"): | |
| # TODO: Replace with your model code | |
| prediction = generate_prediction(np.array([[age, income, family_size, pin, Gender, Review, Marital_status, occupation, education]])) | |
| # Show the prediction | |
| st.write("Prediction:", prediction[0]) | |
| elif choice == "About": | |
| st.subheader("About this app") | |
| html_temp_about1= """<div style="background-color:#6D7B8D;padding:10px"> | |
| <h4 style="color:white;text-align:center;"> | |
| Online Food Order Prediction with Machine Learning .</h4> | |
| </div> | |
| </br>""" | |
| st.markdown(html_temp_about1, unsafe_allow_html=True) | |
| html_temp4 = """ | |
| <div style="background-color:#98AFC7;padding:10px"> | |
| <h4 style="color:white;text-align:center;">Thanks for Visiting</h4> | |
| </div> | |
| <br></br> | |
| <br></br>""" | |
| st.markdown(html_temp4, unsafe_allow_html=True) | |
| else: | |
| pass | |
| if __name__ == "__main__": | |
| main() | |
| # import streamlit as st | |