Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoFeatureExtractor, AutoModel
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+
+# Load HeAR model and feature extractor
+MODEL_ID = "google/hear"
+feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_ID)
+model = AutoModel.from_pretrained(MODEL_ID)
+
+# Dummy classifier (replace with your trained classifier)
+# For demonstration, we simulate a trained classifier with random weights
+# In real use, train a classifier on HeAR embeddings using your labeled dataset
+clf = LogisticRegression()
+clf.classes_ = np.array(["Normal", "Abnormal"])
+clf.coef_ = np.random.randn(1, 768)  # HeAR outputs 768-dim embeddings
+clf.intercept_ = np.random.randn(1)
+
+def extract_embedding(audio):
+    # audio: tuple (sr, np.array)
+    if audio is None:
+        return None
+    sr, y = audio
+    # HeAR expects 2-second clips at 16kHz; pad/truncate as needed
+    target_sr = 16000
+    if sr != target_sr:
+        import librosa
+        y = librosa.resample(y, orig_sr=sr, target_sr=target_sr)
+    y = y[:target_sr*2] if len(y) > target_sr*2 else np.pad(y, (0, max(0, target_sr*2-len(y))))
+    inputs = feature_extractor(y, sampling_rate=target_sr, return_tensors="pt")
+    with torch.no_grad():
+        emb = model(**inputs).last_hidden_state.mean(dim=1).cpu().numpy()
+    return emb
+
+def predict(audio):
+    emb = extract_embedding(audio)
+    if emb is None:
+        return "Please upload a heart or lung sound file."
+    # Predict with the dummy classifier
+    pred = clf.predict(emb)[0]
+    prob = clf.predict_proba(emb)[0]
+    return f"Prediction: **{pred}**\n\nConfidence: {max(prob):.2%}"
+
+description = """
+# Heart & Lung Sound Classifier (Demo)
+Upload a heart or lung sound (WAV, MP3, etc.).  
+This demo uses the [HeAR model](https://huggingface.co/google/hear) for health acoustic embeddings and a simple classifier for normal/abnormal prediction.  
+**Note:** For best results, use 2-second clips. For real diagnosis, a classifier trained on labeled heart/lung sound data should be used.
+"""
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Audio(sources=["upload", "microphone"], type="numpy", label="Upload Heart/Lung Sound"),
+    outputs=gr.Markdown(),
+    title="Heart & Lung Sound Classifier",
+    description=description,
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch()