Upload 7 files

app.py

@@ -0,0 +1,75 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+import ast
+
+# Set page title and favicon
+st.set_page_config(page_title='Heart Failure Prediction', page_icon=':heart:')
+
+# Load models and data
+with open('model_scaler.pkl', 'rb') as file_1:
+  model_scaler = joblib.load(file_1)
+
+with open('rf_best_model.pkl', 'rb') as file_2:
+  rf_random_best = joblib.load(file_2)
+
+with open('list_num_col.txt', 'r') as file_3:
+  num_col = file_3.read()
+
+with open('list_cat_col.txt', 'r') as file_4:
+  cat_col = file_4.read()
+
+with open('boxcox.pkl', 'rb') as file_5:
+  box = joblib.load(file_5)
+
+num_col = ast.literal_eval(num_col)
+cat_col = ast.literal_eval(cat_col)
+
+# Set page header
+st.title('Heart Failure Prediction')
+
+# Add page description
+st.markdown('This web app predicts the likelihood of heart failure based on several factors. Fill in the form below and click the "Predict" button to get your results.')
+
+# Add form inputs
+time = st.slider('Follow-up period (days)', min_value=1, max_value=365, step=1)
+age = st.slider('Age', 30, 120, step=1)
+ejection_fraction = st.number_input('Ejection fraction', min_value=1, max_value=100, step=1)
+serum_creatinine = st.number_input('Serum creatinine', min_value=0.0, max_value=10.0, step=0.01)
+serum_sodium = st.number_input('Serum sodium', min_value=100, max_value=150, step=1)
+anaemia = st.radio('Anemia', ('Yes', 'No'))
+diabetes = st.radio('Diabetes', ('Yes', 'No'))
+smoking = st.radio('Smoking', ('Yes', 'No'))
+
+# Add prediction button
+if st.button('Predict'):
+    # Process form inputs
+    data_inf = pd.DataFrame({'time': time,
+                             'ejection_fraction': ejection_fraction,
+                             'age': age,
+                             'serum_creatinine': serum_creatinine,
+                             'serum_sodium': serum_sodium,
+                             'anaemia': 1 if anaemia == 'Yes' else 0,
+                             'diabetes': 1 if diabetes == 'Yes' else 0,
+                             'smoking': 1 if smoking == 'Yes' else 0
+                            }, index=[0])
+
+    data_inf_t = box.transform(data_inf)
+    data_inf_num = data_inf_t[num_col]
+    data_inf_cat = data_inf_t[cat_col]
+
+    data_inf_num_scaled = data_inf_num.copy()
+    data_inf_num_scaled[num_col] = model_scaler.transform(data_inf_num[num_col])
+    data_inf_cat_encoded = data_inf_cat.copy()
+
+    data_inf_final = np.concatenate([data_inf_num_scaled, data_inf_cat_encoded], axis=1)
+
+    result = rf_random_best.predict(data_inf_final)[0]
+
+    # Display prediction result
+    st.markdown('## Prediction result')
+    if result == 1:
+        st.markdown(':heart: **High risk of heart failure!** :heart:')
+    else:
+        st.markdown(':thumbsup: **Low risk of heart failure.** :thumbsup:')

boxcox.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:28ee56a50a1c2e09651cb183c69b021af913aba505784c6d89e57a19ea8ad14a
+size 474

list_cat_col.txt

@@ -0,0 +1 @@
+['anaemia', 'diabetes', 'smoking']

list_num_col.txt

@@ -0,0 +1 @@
+['age', 'ejection_fraction', 'serum_creatinine', 'serum_sodium', 'time']

model_scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9dc460a36f578c03d24eaaab347fc54ad89ba41ff63175f6b4316d5b5b709748
+size 723

requirements.txt

@@ -0,0 +1,5 @@
+streamlit
+joblib
+scikit-learn
+pandas
+matplotlib

rf_best_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:595d9f36c1f14f41bffac6bd0e4c9fe807df305f43fd218b964020aaf6babb36
+size 400462