import streamlit as st
import pandas as pd
#import joblib
import matplotlib.pyplot as plt
import time
import os 

###Load the numerical imputer
num_imputer_filepath = "numerical_imputer.joblib"
#num_imputer = joblib.load(num_imputer_filepath)

## Load the scaler
scaler_filepath = "scaler.joblib"
#scaler = joblib.load(scaler_filepath)

# # Load the Random Forest model
model_filepath = "rf_model.joblib"
#model = joblib.load(model_filepath)

# Define a function to preprocess the input data
def preprocess_input_data(input_data):
    input_data_df = pd.DataFrame(input_data, columns=['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age', 'Insurance'])
    num_columns = input_data_df.select_dtypes(include='number').columns

    input_data_imputed_num = num_imputer.transform(input_data_df[num_columns])
    input_scaled_df = pd.DataFrame(scaler.transform(input_data_imputed_num), columns=num_columns)

    return input_scaled_df

# Define a function to make the sepsis prediction
def predict_sepsis(input_data):
    input_scaled_df = preprocess_input_data(input_data)
    prediction = model.predict(input_scaled_df)[0]
    probabilities = model.predict_proba(input_scaled_df)[0]
    sepsis_status = "Positive" if prediction == 1 else "Negative"

    output_df = pd.DataFrame(input_data, columns=['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age', 'Insurance'])
    output_df['Prediction'] = sepsis_status
    output_df['Negative Probability'] = probabilities[0]
    output_df['Positive Probability'] = probabilities[1]

    return output_df, probabilities

# Create a Streamlit app
def main():
    st.title('Sepsis Prediction Application')


    # How to use
    st.sidebar.title('How to Use')
    st.sidebar.markdown('Follow these steps to predict sepsis:')
    
  
    st.sidebar.title('Parameters')

    # Input parameter explanations

    # Plasma Glucose - Slider
    st.sidebar.markdown('**PRG:** Plasma Glucose')
    
    PRG = st.sidebar.slider('PRG', min_value=0.0, max_value=200.0, value=100.0)

    # Blood Work Result 1 - Text Input
    st.sidebar.markdown('**PL:** Blood Work Result 1')
    PL = st.sidebar.number_input('PL', value=0.0)

    # Blood Pressure Measured - Slider
    st.sidebar.markdown('**PR:** Blood Pressure Measured')
    PR = st.sidebar.slider('PR', min_value=60, max_value=180, value=120)

    # Blood Work Result 2 - Text Input
    st.sidebar.markdown('**SK:** Blood Work Result 2')
    SK = st.sidebar.number_input('SK', value=0.0)

    # Blood Work Result 3 - Slider
    st.sidebar.markdown('**TS:** Blood Work Result 3')
    TS = st.sidebar.slider('TS', min_value=0.0, max_value=100.0, value=50.0)

    # BMI - Text Input
    st.sidebar.markdown('**M11:** BMI')
    M11 = st.sidebar.number_input('M11', value=0.0)

    # Blood Work Result 4 - Slider
    st.sidebar.markdown('**BD2:** Blood Work Result 4')
    BD2 = st.sidebar.slider('BD2', min_value=0.0, max_value=10.0, value=5.0)

    # Age of the Patient - Slider
    st.sidebar.markdown('**Age:** What is the Age of the Patient: ')
    Age = st.sidebar.slider('Age', min_value=0, max_value=120, value=30)

    # Insurance - Radio Buttons
    st.sidebar.markdown('**Insurance:** Does you have Insurance?')
    insurance_options = {0: 'NO', 1: 'YES'}
    Insurance = st.sidebar.radio('Insurance', list(insurance_options.keys()), format_func=lambda x: insurance_options[x])

    input_data = [[PRG, PL, PR, SK, TS, M11, BD2, Age, Insurance]]

    if st.sidebar.button('Predict'):
        with st.spinner("Wait a moment..."):
            # Simulate a long-running process
            progress_bar = st.progress(0)
            for i in range(100):
                time.sleep(0.1)
                progress_bar.progress(i + 1)

            output_df, probabilities = predict_sepsis(input_data)

            st.subheader('Result')
            st.write(output_df)

            # Plot the probabilities as a pie chart 
            fig, ax = plt.subplots()
            colors = ['#FF0000', '#008000']  
            ax.pie(probabilities, labels=['Negative', 'Positive'], autopct='%1.1f%%', startangle=90, colors=colors)
            ax.set_title('Sepsis Prediction Probabilities')
            st.pyplot(fig)
            st.snow()