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app.py

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+import streamlit as st
+import numpy as np
+import onnxruntime as rt
+import soundfile as sf
+import sounddevice as sd
+from scipy.io.wavfile import write
+
+# Load the ONNX model
+model_path = 'model.onnx'  # Replace with the actual path to your model
+model_inference = rt.InferenceSession(model_path)
+
+# Function to preprocess audio data
+def preprocess_audio(audio_data):
+    mu = np.nanmean(audio_data)
+    std = np.nanstd(audio_data)
+    audio_data = (audio_data - mu) / std
+    audio_data = np.pad(audio_data, (0, 22050 - len(audio_data)), 'constant').reshape(1, -1, 1).astype(np.float32)
+    return audio_data
+
+# Function to preprocess clinical data
+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):
+    sex_Female = 1 if sex == 'Female' else 0
+    sex_Male = 1 if sex == 'Male' else 0
+    tb_prior_No = 1 if tb_prior == 'No' else 0
+    tb_prior_Not_sure = 1 if tb_prior == 'Not sure' else 0
+    tb_prior_Yes = 1 if tb_prior == 'Yes' else 0
+    tb_prior_Pul_No = 1 if tb_prior_Pul == 'No' else 0
+    tb_prior_Pul_Yes = 1 if tb_prior_Pul == 'Yes' else 0
+    tb_prior_Extrapul_No = 1 if tb_prior_Extrapul == 'No' else 0
+    tb_prior_Extrapul_Yes = 1 if tb_prior_Extrapul == 'Yes' else 0
+    tb_prior_Unknown_No = 1 if tb_prior_Unknown == 'No' else 0
+    tb_prior_Unknown_Yes = 1 if tb_prior_Unknown == 'Yes' else 0
+    hemoptysis_No = 1 if hemoptysis == 'No' else 0
+    hemoptysis_Yes = 1 if hemoptysis == 'Yes' else 0
+    weight_loss_No = 1 if weight_loss == 'No' else 0
+    weight_loss_Yes = 1 if weight_loss == 'Yes' else 0
+    smoke_lweek_No = 1 if smoke_lweek == 'No' else 0
+    smoke_lweek_Yes = 1 if smoke_lweek == 'Yes' else 0
+    fever_No = 1 if fever == 'No' else 0
+    fever_Yes = 1 if fever == 'Yes' else 0
+    night_sweats_No = 1 if night_sweats == 'No' else 0
+    night_sweats_Yes = 1 if night_sweats == 'Yes' else 0
+
+    clinical_data = [age, height, weight, reported_cough_dur, heart_rate, temperature,
+                     sex_Female, sex_Male, tb_prior_No, tb_prior_Not_sure, tb_prior_Yes,
+                     tb_prior_Pul_No, tb_prior_Pul_Yes, tb_prior_Extrapul_No, tb_prior_Extrapul_Yes,
+                     tb_prior_Unknown_No, tb_prior_Unknown_Yes, hemoptysis_No, hemoptysis_Yes,
+                     weight_loss_No, weight_loss_Yes, smoke_lweek_No, smoke_lweek_Yes,
+                     fever_No, fever_Yes, night_sweats_No, night_sweats_Yes]
+    clinical_data = np.array(clinical_data).reshape(1, -1).astype(np.float32)
+    return clinical_data
+
+# Main function to run the app
+def main():
+    st.title('TB Cough Sound Analysis')
+
+    # Create tabs
+    tabs = ["Record Cough Sound", "Upload Cough Sound"]
+    choice = st.sidebar.selectbox("Choose Option", tabs)
+
+    if choice == "Record Cough Sound":
+        st.write("**Record Cough Sound**")
+        st.write("Press the button below to start recording:")
+        start_recording = st.button("Start Recording")
+
+        if start_recording:
+            duration = 5  # Set the default recording duration to 5 seconds
+            st.write("Recording started...")
+            sample_rate = 22050
+            audio_data = sd.rec(int(duration * sample_rate), samplerate=sample_rate, channels=1)
+            sd.wait()
+            write("audio_file.wav", sample_rate, audio_data)
+            st.success("Recording saved as 'audio_file.wav'.")
+            st.info("Please proceed to the next step.")
+
+    elif choice == "Upload Cough Sound":
+        st.write("**Upload Cough Sound**")
+        uploaded_file = st.file_uploader("Upload Cough Sound (WAV file)", type=["wav"])
+        if uploaded_file is not None:
+            with open("audio_file.wav", "wb") as f:
+                f.write(uploaded_file.getvalue())
+            st.success("File uploaded successfully.")
+            st.info("Please proceed to the next step.")
+
+    st.write('**Step 2: Enter Clinical Information**')
+    age = st.slider('Age', 1, 100, 30)
+    height = st.slider('Height (cm)', 100, 300, 170)
+    weight = st.slider('Weight (kg)', 20, 200, 70)
+    reported_cough_dur = st.slider('Reported Cough Duration (days)', 1, 100, 10)
+    heart_rate = st.slider('Heart Rate (bpm)', 50, 200, 80)
+    temperature = st.slider('Body Temperature (°C)', 35.0, 40.0, 37.0)
+    sex = st.radio('Sex', ('Male', 'Female'))
+    tb_prior = st.radio('TB Prior', ('No', 'Not sure', 'Yes'))
+    tb_prior_Pul = st.radio('TB Prior Pul', ('No', 'Yes'))
+    tb_prior_Extrapul = st.radio('TB Prior Extrapul', ('No', 'Yes'))
+    tb_prior_Unknown = st.radio('TB Prior Unknown', ('No', 'Yes'))
+    hemoptysis = st.radio('Hemoptysis', ('No', 'Yes'))
+    weight_loss = st.radio('Weight Loss', ('No', 'Yes'))
+    smoke_lweek = st.radio('Smoke Lweek', ('No', 'Yes'))
+    fever = st.radio('Fever', ('No', 'Yes'))
+    night_sweats = st.radio('Night Sweats', ('No', 'Yes'))
+
+    if st.button('Predict'):
+        if choice == "Record Cough Sound":
+            audio_file_path = "audio_file.wav"
+        elif choice == "Upload Cough Sound":
+            audio_file_path = "audio_file.wav"
+
+        raw_values, rate = sf.read(audio_file_path)
+        audio_data = preprocess_audio(raw_values)
+        clinical_data = 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)
+        input_name = model_inference.get_inputs()[0].name
+        input_name2 = model_inference.get_inputs()[1].name
+        label_name = model_inference.get_outputs()[0].name
+        onnx_pred = model_inference.run([label_name], {input_name: audio_data, input_name2: clinical_data})
+        result = onnx_pred[0]
+
+        st.write(f"**Prediction:** {result[0][0]}")
+        if result[0][0] >= 0.5:
+            st.write("Tuberculosis (TB) is found based on the audio data and clinical information.")
+        else:
+            st.write("No Tuberculosis (TB) is found based on the audio data and clinical information.")
+
+if __name__ == "__main__":
+    main()

model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:076ae7c17d22fa1ac1be93f26c12ae5c14962f6c97743433329ced2d36c22030
+size 1852490

requirements.txt

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+streamlit
+numpy
+onnxruntime
+soundfile
+sounddevice
+scipy