# prompt: create a streamlit for svm for checking eligible for loan or not
import streamlit as st 
import joblib
import numpy as np

model = joblib.load(open('titanic_model.joblib', 'rb'))

st.markdown(
        """
        <style>
        /* Change the background color here */
        .stApp {
      
            background-image: url('https://i.pinimg.com/564x/59/ba/a5/59baa5f4e79e5e50ff42f7d1a4ba4d94.jpg'); /* Replace with your image URL */
            background-size: cover;
        }
        
        /* Change the color of the title here */
        h1 {
            color: #87ceeb ; 
            text-align: center; 
            font-family: Monaco;
            font-weight: bold;
        }

        p{
        color:#ffffff;
        font-weight: bold;
        
        }
        .stButton p {
            color: #002e63;
            font-weight: bold;
        }

        /* Change the color of number of slider here */
        .stSlider .st-cq {
            color: #ffffff; /* Change the color here */
            font-weight:bold;
        }

    
        </style>
        """,
        unsafe_allow_html=True,
    )

st.title('Titanic Survival Prediction')

# Input fields
pclass = st.selectbox("Passenger's class", [1, 2, 3])
sex = st.selectbox('Sex', ['male', 'female'])
age = st.slider('Age', min_value=0, max_value=100, value=30)
sibsp = st.slider('Number of siblings/spouses abroad', min_value=0, max_value=10, value=0)
parch = st.slider('Number of parents/children abroad', min_value=0, max_value=10, value=0)
fare = st.number_input('Fare', min_value=0.0, max_value=1000.0, value=50.0)
embarked = st.selectbox('Embarked from: ', ['Southampton', 'Cherbourg', 'Queenstown'])



if st.button('Predict'):
    sex = 1 if sex == 'male' else 0
    embarked_mapping = {'Cherbourg': 0, 'Queenstown': 1, 'Southampton': 2}
    embarked = embarked_mapping.get(embarked, 0)
    # Combine input data into a numpy array
    input_data = np.array([pclass, sex, age, sibsp, parch, fare, embarked]).reshape(1, -1)
    
    prediction = model.predict(input_data)
    
    # Display prediction
    if prediction[0] == 0:
        st.write('Not Survived')
    else:
        st.write('Survived')