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Cardiovascular_Health_Prediction_app
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import streamlit as st 
import joblib
import numpy as np
## Heading 
html_temp = """
    <div style="background-color:black;padding:10px">
    <h2 style="color:red;text-align:center;">Cardiovascular Health Prediction App </h2>
    </div>
    """
st.markdown(html_temp, unsafe_allow_html=True)

# st.markdown(html_temp, unsafe_allow_html=True)

# image
url="https://static.vecteezy.com/system/resources/previews/006/712/964/original/abstract-health-medical-science-healthcare-icon-digital-technology-doctor-concept-modern-innovation-treatment-medicine-on-hi-tech-future-blue-background-for-wallpaper-template-web-design-vector.jpg"
st.image(url, use_container_width=True)

st.markdown(f"""
    <style>
        /* Set the background image for the entire app */
        .stApp {{
            background-color:#fdf5df;
            background-size: 100px;
            background-repeat:no;
            background-attachment: auto;
            background-position:full;
        }}
    </style>
    """, unsafe_allow_html=True)

st.write("Enter the Patient  details:")

# model

health_model=joblib.load("health_model.joblib")

scaler_model=joblib.load('scaler_the data_model.joblib')

# Input variables

col1, col2 = st.columns(2)
with col1:
    
    ## Age
    age=st.slider("Slide the AGE of the Patient:",max_value=80,min_value=20)
    
with col2:
    
    ## gender
    option_gen=["Female "," Male"]
    gen=st.selectbox("Select the Gender",option_gen) 
    gender_value=option_gen.index(gen)
    
col1, col2 = st.columns(2)
with col1:
    ## chestpain
    chestpain=st.slider("Slide the chestpain Value:",min_value=0,max_value=3)
    
with col2:
    ## restingBP
    restingBP=st.slider("Slide the Resting  BP of the Patient:",min_value=90,max_value=200)

col1,col2=st.columns(2)
with col1:
    ## serumcholestrol
    serumcholestrol=st.slider("Slide The Serum cholestrol Of a Ptient :",min_value=0,max_value=600)
    
with col2:
    ## fastingbloodsugar
    fastingbloodsugar= st.radio("Is Patient blood is Fasting:", ["YES", "NO"])
    if fastingbloodsugar=="YES":
        fastingbloodsugar_value=1
    else:
       fastingbloodsugar_value=0
    
col1,col2=st.columns(2)

with col1:
   ## restingrelectro
    restingrelectro=st.slider("Slide The restingrelectro of a Patient :",min_value=0,max_value=2)
        
with col2:
    ## maxheartrate
    maxheartrate=st.slider("Slide The max heart rate  of the Patient :",min_value=70,max_value=200)

col1,col2=st.columns(2)
with col1:
    ## exerciseangia
    exerciseangia=st.slider("Slide the exerciseangia Value:",min_value=0,max_value=1)
    

with col2:
    ##oldpeak'
    oldpeak=st.slider("Silde the oldpeak :",max_value=6.2,min_value=0.0,value=0.1)
    
    
col1,col2=st.columns(2)
with col1:
    ## slope
    slope=st.slider("Silde the slope of the Patient :",max_value=3,min_value=0,value=1)
    
with col2:
    ## noofmajorvessels
    noofmajorvessels=st.slider("Silde the No of Major Vessels to  a Patient :",max_value=3,min_value=0,value=1)


if st.button("Submit"):
    
    # st.write("before scaleing:",[age,gender_value,chestpain,restingBP,serumcholestrol,fastingbloodsugar_value,restingrelectro,
    #                              maxheartrate,exerciseangia,oldpeak,slope,noofmajorvessels])
    # Scaler the values
    scaler_val = scaler_model.transform([[age,gender_value,chestpain,restingBP,serumcholestrol,fastingbloodsugar_value,restingrelectro,
                                 maxheartrate,exerciseangia,oldpeak,slope,noofmajorvessels]])
    
    # st.write("after scaleing:",scaler_val)

    try:
    
        prediction=health_model.predict(scaler_val)[0]
        
        # Define messages and colors
        review_status = {
            0: ("✅ The patient is Healthy", "#32CD32"),  # Green
            1: ("❌ The Patient has Heart Disease ", "#FF4500")   # Red-Orange
         }
    
        # Get message and color based on prediction
        message, color = review_status.get(prediction, ("❓ Unknown Prediction", "#808080"))
    
        # Display styled result
        st.markdown(f"""
            <div style="
                padding: 15px; 
                background-color: {color}; 
                border-radius: 10px; 
                text-align: center; 
                font-size: 18px; 
                font-weight: bold; 
                color: white;">
                {message}
            </div>
        """, unsafe_allow_html=True)
    
    except Exception as e:
        st.error(f"⚠️ Error in prediction: {e}")