Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import pickle
+from sklearn.preprocessing import StandardScaler
+from sklearn.linear_model import LogisticRegression
+
+# Load the trained model and scaler
+model = pickle.load(open('heart_failure_prediction.pkl', 'rb'))  # Load the logistic regression model
+scaler = pickle.load(open('scaler.pkl', 'rb'))  # Load the scaler
+
+#streamlit page
+st.title('Heart Failure Prediction')
+st.header('Enter the following details to predict heart failure:')
+age = st.number_input('Age', min_value=0, max_value=120, value=25)
+anaemia = st.selectbox('Anaemia', [0, 1])
+creatinine_phosphokinase = st.number_input('Creatinine Phosphokinase', min_value=0, max_value=10000, value=100)
+diabetes = st.selectbox('Diabetes', [0, 1])
+ejection_fraction = st.number_input('Ejection Fraction', min_value=0, max_value=100, value=50)
+high_blood_pressure = st.selectbox('High Blood Pressure', [0, 1])
+platelets = st.number_input('Platelets', min_value=0, max_value=1000000, value=10000)
+serum_creatinine = st.number_input('Serum Creatinine', min_value=0.0, max_value=10.0, value=1.0)
+serum_sodium = st.number_input('Serum Sodium', min_value=0, max_value=200, value=100)
+sex = st.selectbox('Sex', ('Male', 'Female'))
+
+# Map 'Male' to 1 and 'Female' to 0
+if sex == 'Male':
+    sex_value = 1
+else:
+    sex_value = 0
+
+smoking = st.selectbox('Smoking', [0, 1])
+time = st.number_input('Time', min_value=0, max_value=1000, value=100)
+
+input_data = np.array([[age, anaemia, creatinine_phosphokinase, diabetes, ejection_fraction, high_blood_pressure, platelets, serum_creatinine, serum_sodium, sex_value, smoking, time]])
+
+if st.button('Predict'):
+  scaled_input = scaler.transform(input_data)
+  prediction = model.predict(scaled_input)
+  if prediction[0] == 0:
+    st.write('The person is not at risk of heart failure.')
+  else:
+    st.write('The person is at risk of heart failure.')
+