File size: 3,121 Bytes
a562d9d dd8cdb5 8a5bdef 318b7fb 8a5bdef dd8cdb5 a562d9d 318b7fb c64d12f a562d9d ed34a12 a562d9d 318b7fb 8a5bdef 0b12140 a562d9d 8a5bdef a562d9d dd8cdb5 |
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 |
import streamlit as st
import pickle
import pandas as pd
import numpy as np
# Page Title with Style
st.title("🩸 Sepsis Prediction App")
st.markdown("---")
# Welcome Message with Style
st.write(
"👋 Welcome to the Sepsis Prediction App! Enter the medical data in the input fields below, "
"click 'Predict Sepsis', and get the prediction result."
)
# About Section with Style
st.sidebar.title("ℹ️ About")
st.sidebar.info(
"This app predicts sepsis based on medical input data. "
"It uses a machine learning model trained on a dataset of sepsis cases."
)
# Load the model and key components
with open('model_and_key_components.pkl', 'rb') as file:
loaded_components = pickle.load(file)
loaded_model = loaded_components['model']
loaded_scaler = loaded_components['scaler']
# Data Fields
data_fields = {
"PRG": "Number of pregnancies (applicable only to females)",
"PL": "Plasma glucose concentration (mg/dL)",
"PR": "Diastolic blood pressure (mm Hg)",
"SK": "Triceps skinfold thickness (mm)",
"TS": "2-hour serum insulin (mu U/ml)",
"M11": "Body mass index (BMI) (weight in kg / {(height in m)}^2)",
"BD2": "Diabetes pedigree function (mu U/ml)",
"Age": "Age of the patient (years)"
}
# Organize input fields into two columns
col1, col2 = st.columns(2)
# Initialize input_data dictionary
input_data = {}
# Function to preprocess input data
def preprocess_input_data(input_data):
numerical_cols = ['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age']
input_data_scaled = loaded_scaler.transform([list(input_data.values())])
return pd.DataFrame(input_data_scaled, columns=numerical_cols)
# Function to make predictions
def make_predictions(input_data_scaled_df):
y_pred = loaded_model.predict(input_data_scaled_df)
sepsis_mapping = {0: 'Negative', 1: 'Positive'}
return sepsis_mapping[y_pred[0]]
# Input Data Fields in two columns
with col1:
input_data["PRG"] = st.number_input("Number of Pregnancies", value=0.0)
input_data["PL"] = st.number_input("Plasma Glucose Concentration (mg/dL)", value=0.0)
input_data["PR"] = st.number_input("Diastolic Blood Pressure (mm Hg)", value=0.0)
input_data["SK"] = st.number_input("Triceps Skinfold Thickness (mm)", value=0.0)
with col2:
input_data["TS"] = st.number_input("2-Hour Serum Insulin (mu U/ml)", value=0.0)
input_data["M11"] = st.number_input("Body Mass Index (BMI)", value=0.0)
input_data["BD2"] = st.number_input("Diabetes Pedigree Function (mu U/ml)", value=0.0)
input_data["Age"] = st.slider("Age of the patient (years)", 0, 100, 0)
# Predict Button with Style
if st.button("🔮 Predict Sepsis"):
try:
input_data_scaled_df = preprocess_input_data(input_data)
sepsis_status = make_predictions(input_data_scaled_df)
st.success(f"The predicted sepsis status is: {sepsis_status}")
except Exception as e:
st.error(f"An error occurred: {e}")
# Display Data Fields and Descriptions
st.sidebar.title("🔍 Data Fields")
for field, description in data_fields.items():
st.sidebar.text(f"{field}: {description}") |