feat: :man_dancing: YOLO

app.py

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+import torchaudio
+import torch
+import gradio as gr
+import keras
+import pandas as pd
+
+from transformers import pipeline
+
+
+def get_murmur_from_recordings(audio):
+    pipe = pipeline("audio-classification",
+                    model="cogniveon/eeem069_heart_murmur_classification")
+
+    sampling_rate, data = audio
+    waveform = torch.tensor(data).float()
+
+    # Resample the audio to 16 kHz (if necessary)
+    if sampling_rate != 16000:
+        resampler = torchaudio.transforms.Resample(sampling_rate, 16000)
+        waveform = resampler(waveform)
+
+    results = pipe(waveform.numpy())
+
+    sorted_results = sorted(results, key=lambda x: x['score'], reverse=True)
+    label_scores = {item['label']: item['score'] for item in sorted_results}
+    return label_scores
+
+
+def get_patient_outcome(age, sex, height, weight, is_pregnant, murmur):
+    model = keras.models.load_model('patient_outcome_classifier.keras')
+
+    is_pregnant = 1 if is_pregnant else 0
+    sex2int = {'Male': 0, 'Female': 1}
+    sex = sex2int[sex]
+
+    age2int = {'Neonate': 0, 'Infant': 1, 'Child': 2, 'Adolescent': 3}
+    age = age2int[age]
+
+    murmur = 0 if murmur == 'Absent' else (1 if murmur == 'Present' else 2)
+
+    data = pd.DataFrame({
+        'Age': float(age),
+        'Sex': float(sex),
+        'Height': float(height),
+        'Weight': float(weight),
+        'Pregnancy status': float(is_pregnant),
+        'Murmur': float(murmur),
+    }, index=[0])
+
+    output = model.predict(data)[0]
+    # 0 - Normal, 1 - Abnormal
+    results = {
+        'Normal': output[0],
+        'Abnormal': output[1]
+    }
+
+    return results
+
+
+def predict(audio, age, sex, height, weight, is_pregnant):
+    assert audio is not None, 'Audio cannot be None'
+
+    murmur_scores = get_murmur_from_recordings(audio)
+    murmur = "Unknown"
+    if murmur_scores['Present'] > 0.70:
+        murmur = "Present"
+    if murmur_scores['Absent'] > 0.80:
+        murmur = "Absent"
+
+    outcome = get_patient_outcome(
+        age, sex, height, weight, is_pregnant, murmur)
+
+    return outcome
+
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        "audio",
+        gr.Radio(label="Age", choices=[
+                 "Neonate", "Infant", "Child", "Adolescent"],
+                 value="Adolescent"),
+        gr.Radio(label="Sex", choices=["Male", "Female"], value="Male"),
+        gr.Number(label="Height", value="98.0"),
+        gr.Number(label="Weight", value="38.1"),
+        gr.Checkbox(label="Pregnant", value=False)
+    ],
+    outputs="label"
+)
+demo.launch()

requirements.txt

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+pandas
+torch
+torchaudio
+keras