Create app.py

app.py

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+import streamlit as st
+import pickle
+import pandas as pd
+import numpy as np
+
+# 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)"
+}
+
+# Page Title
+st.title("Sepsis Prediction App")
+
+# Sidebar with Data Fields
+st.sidebar.title("Input Data")
+input_data = {}
+for field, description in data_fields.items():
+    input_data[field] = st.sidebar.number_input(description, value=0.0)
+
+# 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]]
+
+# Predict Button
+if st.sidebar.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}")
+
+# About Section
+st.sidebar.title("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."
+)
+
+# Welcome Message
+st.write(
+    "Welcome to the Sepsis Prediction App! Enter the medical data in the sidebar, "
+    "click 'Predict Sepsis', and get the prediction result."
+)