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import streamlit as st
import pandas as pd
import joblib
import matplotlib.pyplot as plt
import time
# Load the pre-trained numerical imputer, scaler, and model using joblib
num_imputer = joblib.load('numerical_imputer.joblib')
scaler = joblib.load('scaler.joblib')
model = joblib.load('Final_model.joblib')
# 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 App')
st.image("Strealit_.jpg")
# How to use
st.sidebar.title('How to Use')
st.sidebar.markdown('1. Adjust the input parameters on the left sidebar.')
st.sidebar.markdown('2. Click the "Predict" button to initiate the prediction.')
st.sidebar.markdown('3. The app will simulate a prediction process with a progress bar.')
st.sidebar.markdown('4. Once the prediction is complete, the results will be displayed below.')
st.sidebar.title('Input Parameters')
# Input parameter explanations
st.sidebar.markdown('**PRG:** Plasma Glucose')
PRG = st.sidebar.number_input('PRG', value=0.0)
st.sidebar.markdown('**PL:** Blood Work Result 1')
PL = st.sidebar.number_input('PL', value=0.0)
st.sidebar.markdown('**PR:** Blood Pressure Measured')
PR = st.sidebar.number_input('PR', value=0.0)
st.sidebar.markdown('**SK:** Blood Work Result 2')
SK = st.sidebar.number_input('SK', value=0.0)
st.sidebar.markdown('**TS:** Blood Work Result 3')
TS = st.sidebar.number_input('TS', value=0.0)
st.sidebar.markdown('**M11:** BMI')
M11 = st.sidebar.number_input('M11', value=0.0)
st.sidebar.markdown('**BD2:** Blood Work Result 4')
BD2 = st.sidebar.number_input('BD2', value=0.0)
st.sidebar.markdown('**Age:** What is the Age of the Patient: ')
Age = st.sidebar.number_input('Age', value=0.0)
st.sidebar.markdown('**Insurance:** Does the patient 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("Predicting..."):
# Simulate a long-running process
progress_bar = st.progress(0)
step = 20 # A big step will reduce the execution time
for i in range(0, 100, step):
time.sleep(0.1)
progress_bar.progress(i + step)
output_df, probabilities = predict_sepsis(input_data)
st.subheader('Prediction Result')
st.write(output_df)
# Plot the probabilities
fig, ax = plt.subplots()
ax.bar(['Negative', 'Positive'], probabilities)
ax.set_xlabel('Sepsis Status')
ax.set_ylabel('Probability')
ax.set_title('Sepsis Prediction Probabilities')
st.pyplot(fig)
# Print feature importance
if hasattr(model, 'coef_'):
feature_importances = model.coef_[0]
feature_names = ['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age', 'Insurance']
importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importances})
importance_df = importance_df.sort_values('Importance', ascending=False)
st.subheader('Feature Importance')
fig, ax = plt.subplots()
bars = ax.bar(importance_df['Feature'], importance_df['Importance'])
ax.set_xlabel('Feature')
ax.set_ylabel('Importance')
ax.set_title('Feature Importance')
ax.tick_params(axis='x', rotation=45)
# Add data labels to the bars
for bar in bars:
height = bar.get_height()
ax.annotate(f'{height:.2f}', xy=(bar.get_x() + bar.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom')
st.pyplot(fig)
#st.subheader('Feature Importance')
#st.write(importance_df)
else:
st.write('Feature importance is not available for this model.')
if __name__ == '__main__':
main()
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