File size: 5,358 Bytes
fe18b00 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 |
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)
for i in range(100):
time.sleep(0.1)
progress_bar.progress(i + 1)
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()
|