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| import pandas as pd | |
| import pickle | |
| import streamlit as st | |
| import numpy as np | |
| from huggingface_hub import hf_hub_download | |
| import shap | |
| import matplotlib.pyplot as plt | |
| model_path = hf_hub_download(repo_id=st.secrets["REPO_ID"], filename="model.pkl", token=st.secrets["HF_TOKEN"]) | |
| explainer_path = hf_hub_download(repo_id=st.secrets["REPO_ID"], filename="explainer.pkl", token=st.secrets["HF_TOKEN"]) | |
| # load model | |
| model = pickle.load(open(model_path, "rb")) | |
| def model_proba(x): | |
| return model.predict_proba(x)[:, 1] | |
| explainer = pickle.load(open(explainer_path, "rb")) | |
| def predict(input): | |
| col1c, col2c= st.columns([0.3, 0.7]) | |
| inputss = pd.Series(input) | |
| inputdf = pd.DataFrame(inputss) | |
| inputdf.rename(columns={0: 'value'}, inplace=True) | |
| with col1c: | |
| st.subheader('Input data') | |
| st.table(inputdf) | |
| prob = model.predict_proba([inputss]) | |
| st.header(f'CVD Risk: {prob[0][1]*100:.2f}%') | |
| with col2c: | |
| st.subheader('CVD Risk Explanation') | |
| shap_value = explainer(pd.DataFrame(inputdf).T) | |
| shap.decision_plot(shap_value.base_values, shap_value.values, feature_names=shap_value.feature_names) | |
| ax = plt.gca() | |
| ax.set_xlabel('<-- Feature input decreases risk | Feature input increases risk -->') | |
| ax.set_ylabel('Feature impact -->') | |
| st.pyplot(plt.gcf()) | |
| st.markdown(''' | |
| * The effect of each input feature's value on the model's result shown relates to THIS instance only. | |
| * The straight vertical line is the expected (mean) value of the model. | |
| * The plotted line shows the effect of each feature in deviating from the expected value. | |
| ''') | |
| st.title('Cardiovascular Disease Risk Prediction DEMO') | |
| st.markdown(''' | |
| This is a CVD risk prediction app for demonstration only, **therefore not for clinical use**. | |
| Output from a simple logistic regression model based on 1000 individuals in India. | |
| Data source: [Cardiovascular Disease Dataset](https://www.kaggle.com/datasets/jocelyndumlao/cardiovascular-disease-dataset/) | |
| The CVD risk model prediction is explained using [SHAP](https://shap.readthedocs.io/en/stable/) values. | |
| ''') | |
| col1, col2= st.columns(2) | |
| with col1: | |
| age = st.number_input('Age (years)', 0, 100) | |
| sex_options = ['Female', 'Male'] | |
| sex = st.radio('Sex', sex_options) | |
| chestpain_options = ['none', 'non-anginal pain', 'typical angina', 'atypical angina'] | |
| chestpain= st.radio('Chest pain type', chestpain_options) | |
| restingBP = st.number_input('Resting systolic blood pressure mm HG (94-200)', 0, 200) | |
| with col2: | |
| serumcholestrol = st.number_input('Serum Cholesterol in mg/dl (126-564)', 0, 300) | |
| fastingbloodsugar_options = ['LESS than 120 mg/dl', 'GREATER than or EQUAL 120 mg/dl'] | |
| fastingbloodsugar = st.radio('Fasting blood sugar', fastingbloodsugar_options) | |
| maxheartrate = st.number_input('Maximum heart rate achieved BPM (71-202)', 0, 300) | |
| exerciseangia_options = ['no', 'yes'] | |
| exerciseangia = st.radio('Exercise induced angina', exerciseangia_options) | |
| st.divider() | |
| input={ | |
| 'age': age, | |
| 'gender': sex_options.index(sex), | |
| 'chestpain': chestpain_options.index(chestpain), | |
| 'restingBP': restingBP, | |
| 'serumcholestrol': serumcholestrol, | |
| 'fastingbloodsugar': fastingbloodsugar_options.index(fastingbloodsugar), | |
| 'maxheartrate': maxheartrate, | |
| 'exerciseangia': exerciseangia_options.index(exerciseangia) | |
| } | |
| col1b, col2b = st.columns(2) | |
| with col1b: | |
| but1 = st.empty() | |
| with col2b: | |
| but2 = st.empty() | |
| st.divider() | |
| if but1.button('Predict Input', use_container_width=True): | |
| predict(input) | |
| if but2.button('Predict Random', use_container_width=True): | |
| predict({ | |
| 'age': np.random.randint(35, 90), | |
| 'gender': np.random.randint(0, 2), | |
| 'chestpain': np.random.randint(0, 4), | |
| 'restingBP': np.random.randint(80, 200), | |
| 'serumcholestrol': np.random.randint(100, 600), | |
| 'fastingbloodsugar': np.random.randint(0, 2), | |
| 'maxheartrate': np.random.randint(70, 220), | |
| 'exerciseangia': np.random.randint(0, 2) | |
| }) | |