Add application file

app.py

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+# Hugging Face Spaces – Phone Vibration Sound Classifier
+# -------------------------------------------------
+# Features:
+# - Record 2x 60-second audio samples (Class A and Class B)
+# - Sliding window segmentation (20 ms)
+# - MFCC feature extraction
+# - Train / fine‑tune a classifier
+# - Record a 3rd sample for testing and predict class
+#
+# Requirements (automatically handled by Spaces):
+#   gradio, numpy, librosa, scikit-learn, soundfile
+#
+# Space type: Gradio
+
+import gradio as gr
+import numpy as np
+import librosa
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import Pipeline
+import tempfile
+import os
+
+SAMPLE_RATE = 16000
+WINDOW_MS = 20
+WINDOW_SAMPLES = int(SAMPLE_RATE * WINDOW_MS / 1000)
+N_MFCC = 13
+
+# Global model storage
+model_pipeline = None
+class_names = ["Class A", "Class B"]
+
+
+def audio_to_windows(y: np.ndarray, sr: int):
+    hop = WINDOW_SAMPLES
+    windows = []
+    for start in range(0, len(y) - WINDOW_SAMPLES, hop):
+        windows.append(y[start:start + WINDOW_SAMPLES])
+    return windows
+
+
+def extract_features(y: np.ndarray, sr: int):
+    windows = audio_to_windows(y, sr)
+    feats = []
+    for w in windows:
+        mfcc = librosa.feature.mfcc(y=w, sr=sr, n_mfcc=N_MFCC)
+        mfcc_mean = mfcc.mean(axis=1)
+        feats.append(mfcc_mean)
+    return np.array(feats)
+
+
+def load_audio(file):
+    y, sr = librosa.load(file, sr=SAMPLE_RATE, mono=True)
+    return y, sr
+
+
+def train_model(audio_a, audio_b):
+    global model_pipeline
+
+    if audio_a is None or audio_b is None:
+        return "Please record both Class A and Class B samples."
+
+    y_a, sr_a = load_audio(audio_a)
+    y_b, sr_b = load_audio(audio_b)
+
+    X_a = extract_features(y_a, sr_a)
+    X_b = extract_features(y_b, sr_b)
+
+    y_labels = np.concatenate([
+        np.zeros(len(X_a)),
+        np.ones(len(X_b))
+    ])
+
+    X = np.vstack([X_a, X_b])
+
+    model_pipeline = Pipeline([
+        ("scaler", StandardScaler()),
+        ("clf", LogisticRegression(max_iter=200))
+    ])
+
+    model_pipeline.fit(X, y_labels)
+
+    return f"Model trained successfully. Windows used: {len(X)}"
+
+
+def predict(audio_test):
+    global model_pipeline
+
+    if model_pipeline is None:
+        return "Model not trained yet."
+
+    if audio_test is None:
+        return "Please record a test sample."
+
+    y, sr = load_audio(audio_test)
+    X_test = extract_features(y, sr)
+
+    probs = model_pipeline.predict_proba(X_test)
+    avg_prob = probs.mean(axis=0)
+
+    predicted_class = int(np.argmax(avg_prob))
+
+    return (
+        f"Prediction: {class_names[predicted_class]} (Confidence: {avg_prob[predicted_class]*100:.1f}%)"
+    )
+
+
+with gr.Blocks(title="Phone Vibration Sound Classifier") as demo:
+    gr.Markdown("# Phone Vibration Sound Classifier")
+    gr.Markdown("Record two 1‑minute samples for two vibration sources, train the model, then test a third recording.")
+
+    with gr.Row():
+        audio_a = gr.Audio(sources=["microphone"], type="filepath", label="Record Class A (60 seconds)")
+        audio_b = gr.Audio(sources=["microphone"], type="filepath", label="Record Class B (60 seconds)")
+
+    train_btn = gr.Button("Train / Fine‑tune Model")
+    train_status = gr.Textbox(label="Training Status")
+
+    train_btn.click(train_model, inputs=[audio_a, audio_b], outputs=train_status)
+
+    gr.Markdown("---")
+
+    audio_test = gr.Audio(sources=["microphone"], type="filepath", label="Record Test Sample (up to 60 seconds)")
+    predict_btn = gr.Button("Predict Class")
+    prediction_output = gr.Textbox(label="Prediction Result")
+
+    predict_btn.click(predict, inputs=audio_test, outputs=prediction_output)
+
+
+demo.launch(share=True,server_port=7861)

requirements.txt

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+gradio
+numpy
+librosa
+scikit-learn
+soundfile