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import streamlit as st |
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import numpy as np |
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import onnxruntime as rt |
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import soundfile as sf |
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model_path = 'model.onnx' |
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model_inference = rt.InferenceSession(model_path) |
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def preprocess_audio(audio_file): |
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raw_values, rate = sf.read(audio_file) |
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mu = np.nanmean(raw_values) |
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std = np.nanstd(raw_values) |
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audio_data = (raw_values - mu) / std |
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audio_data = np.pad(audio_data, (0, 22050 - len(audio_data)), 'constant').reshape(1, -1, 1).astype(np.float32) |
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return audio_data |
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def preprocess_clinical_data(age, height, weight, reported_cough_dur, heart_rate, temperature, sex, tb_prior, tb_prior_Pul, tb_prior_Extrapul, tb_prior_Unknown, hemoptysis, weight_loss, smoke_lweek, fever, night_sweats): |
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sex_Female = 1 if sex == 'Female' else 0 |
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sex_Male = 1 if sex == 'Male' else 0 |
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tb_prior_No = 1 if tb_prior == 'No' else 0 |
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tb_prior_Not_sure = 1 if tb_prior == 'Not sure' else 0 |
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tb_prior_Yes = 1 if tb_prior == 'Yes' else 0 |
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tb_prior_Pul_No = 1 if tb_prior_Pul == 'No' else 0 |
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tb_prior_Pul_Yes = 1 if tb_prior_Pul == 'Yes' else 0 |
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tb_prior_Extrapul_No = 1 if tb_prior_Extrapul == 'No' else 0 |
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tb_prior_Extrapul_Yes = 1 if tb_prior_Extrapul == 'Yes' else 0 |
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tb_prior_Unknown_No = 1 if tb_prior_Unknown == 'No' else 0 |
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tb_prior_Unknown_Yes = 1 if tb_prior_Unknown == 'Yes' else 0 |
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hemoptysis_No = 1 if hemoptysis == 'No' else 0 |
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hemoptysis_Yes = 1 if hemoptysis == 'Yes' else 0 |
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weight_loss_No = 1 if weight_loss == 'No' else 0 |
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weight_loss_Yes = 1 if weight_loss == 'Yes' else 0 |
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smoke_lweek_No = 1 if smoke_lweek == 'No' else 0 |
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smoke_lweek_Yes = 1 if smoke_lweek == 'Yes' else 0 |
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fever_No = 1 if fever == 'No' else 0 |
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fever_Yes = 1 if fever == 'Yes' else 0 |
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night_sweats_No = 1 if night_sweats == 'No' else 0 |
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night_sweats_Yes = 1 if night_sweats == 'Yes' else 0 |
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clinical_data = [age, height, weight, reported_cough_dur, heart_rate, temperature, |
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sex_Female, sex_Male, tb_prior_No, tb_prior_Not_sure, tb_prior_Yes, |
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tb_prior_Pul_No, tb_prior_Pul_Yes, tb_prior_Extrapul_No, tb_prior_Extrapul_Yes, |
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tb_prior_Unknown_No, tb_prior_Unknown_Yes, hemoptysis_No, hemoptysis_Yes, |
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weight_loss_No, weight_loss_Yes, smoke_lweek_No, smoke_lweek_Yes, |
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fever_No, fever_Yes, night_sweats_No, night_sweats_Yes] |
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clinical_data = np.array(clinical_data).reshape(1, -1).astype(np.float32) |
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return clinical_data |
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def main(): |
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st.title('TB Cough Sound Analysis') |
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st.write('**Step 1: Upload Cough Sound Recording**') |
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audio_file = st.file_uploader("Upload a cough sound recording", type=['wav', 'mp3']) |
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if audio_file is not None: |
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st.audio(audio_file) |
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st.write('**Step 2: Enter Clinical Data**') |
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age = st.slider('Age', min_value=0, max_value=100, value=30, step=1) |
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height = st.slider('Height (in cm)', min_value=0.0, max_value=300.0, value=170.0, step=0.1) |
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weight = st.slider('Weight (in kg)', min_value=0.0, max_value=200.0, value=70.0, step=0.1) |
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reported_cough_dur = st.slider('Reported Cough Duration (in days)', min_value=0, max_value=100, value=10, step=1) |
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heart_rate = st.slider('Heart Rate (beats per minute)', min_value=0, max_value=200, value=80, step=1) |
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temperature = st.slider('Body Temperature (in °C)', min_value=0.0, max_value=40.0, value=37.0, step=0.1) |
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sex = st.radio('Sex', ['Male', 'Female']) |
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tb_prior = st.selectbox('TB Prior', ['No', 'Not sure', 'Yes'], index=0) |
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tb_prior_Pul = st.selectbox('TB Prior - Pulmonary', ['No', 'Yes'], index=0) |
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tb_prior_Extrapul = st.selectbox('TB Prior - Extrapulmonary', ['No', 'Yes'], index=0) |
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tb_prior_Unknown = st.selectbox('TB Prior - Unknown', ['No', 'Yes'], index=0) |
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hemoptysis = st.selectbox('Hemoptysis', ['No', 'Yes'], index=0) |
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weight_loss = st.selectbox('Weight Loss', ['No', 'Yes'], index=0) |
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smoke_lweek = st.selectbox('Smoking Last Week', ['No', 'Yes'], index=0) |
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fever = st.selectbox('Fever', ['No', 'Yes'], index=0) |
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night_sweats = st.selectbox('Night Sweats', ['No', 'Yes'], index=0) |
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if st.button('Predict'): |
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audio_data = preprocess_audio(audio_file) |
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clinical_data = preprocess_clinical_data(age, height, weight, reported_cough_dur, heart_rate, temperature, |
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sex, tb_prior, tb_prior_Pul, tb_prior_Extrapul, tb_prior_Unknown, hemoptysis, weight_loss, smoke_lweek, fever, |
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night_sweats) |
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input_name = model_inference.get_inputs()[0].name |
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input_name2 = model_inference.get_inputs()[1].name |
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label_name = model_inference.get_outputs()[0].name |
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onnx_pred = model_inference.run([label_name], {input_name: audio_data, input_name2: clinical_data}) |
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result = onnx_pred[0] |
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if result >= 0.5: |
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prediction_text = "Tuberculosis is found based on the audio data and clinical information." |
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probability_label = "Positive Probability" |
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else: |
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prediction_text = "No Tuberculosis is found based on the audio data and clinical information." |
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probability_label = "Negative Probability" |
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st.markdown(f"<h2 style='text-align:center;'>Prediction:</h2>", unsafe_allow_html=True) |
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st.markdown(f"<h3 style='text-align:center; font-weight:bold;'>{prediction_text}</h3>", unsafe_allow_html=True) |
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st.markdown(f"<h4 style='text-align:center;'>{probability_label}: {result}</h4>", unsafe_allow_html=True) |
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if __name__ == '__main__': |
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main() |
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