Delete app.py

app.py

@@ -1,177 +0,0 @@
-import gradio as gr
-import torch
-import torch.nn as nn
-import numpy as np
-import librosa
-
-# ── Constants (must match your notebook exactly) ──────────────────────────────
-SR          = 22050
-DURATION    = 30
-N_SAMPLES   = SR * DURATION      # 661500
-N_MELS      = 128
-HOP_LENGTH  = 512
-N_FFT       = 2048
-N_CLASSES   = 10
-
-GENRES = sorted([
-    "blues", "classical", "country", "disco", "hiphop",
-    "jazz", "metal", "pop", "reggae", "rock"
-])
-
-DEVICE = torch.device("cpu")     # HF Spaces free tier = CPU only
-
-
-# ── Model Architecture (must be identical to your notebook Cell 64) ───────────
-class GenreCNN(nn.Module):
-    def __init__(self, n_classes=N_CLASSES):
-        super().__init__()
-
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(1, 32, 3, padding=1), nn.BatchNorm2d(32),
-            nn.ReLU(), nn.MaxPool2d(2, 2), nn.Dropout2d(0.25)
-        )
-        self.conv2 = nn.Sequential(
-            nn.Conv2d(32, 64, 3, padding=1), nn.BatchNorm2d(64),
-            nn.ReLU(), nn.MaxPool2d(2, 2), nn.Dropout2d(0.25)
-        )
-        self.conv3 = nn.Sequential(
-            nn.Conv2d(64, 128, 3, padding=1), nn.BatchNorm2d(128),
-            nn.ReLU(), nn.MaxPool2d(2, 2), nn.Dropout2d(0.25)
-        )
-        self.conv4 = nn.Sequential(
-            nn.Conv2d(128, 256, 3, padding=1), nn.BatchNorm2d(256),
-            nn.ReLU(), nn.MaxPool2d(2, 2), nn.Dropout2d(0.25)
-        )
-        self.pool       = nn.AdaptiveAvgPool2d((1, 1))
-        self.classifier = nn.Sequential(
-            nn.Flatten(),
-            nn.Linear(256, 128), nn.ReLU(), nn.Dropout(0.5),
-            nn.Linear(128, n_classes)
-        )
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.conv2(x)
-        x = self.conv3(x)
-        x = self.conv4(x)
-        x = self.pool(x)
-        return self.classifier(x)
-
-
-# ── Load model once at startup ────────────────────────────────────────────────
-model = GenreCNN().to(DEVICE)
-model.load_state_dict(
-    torch.load("best_model.pth", map_location=DEVICE)
-)
-model.eval()
-print("✓ Model loaded successfully")
-
-
-# ── Audio preprocessing helpers ───────────────────────────────────────────────
-def get_mel_spectrogram(y):
-    """Convert raw audio array → normalised (128, 512) mel spectrogram tensor."""
-    mel    = librosa.feature.melspectrogram(
-                y=y, sr=SR, n_mels=N_MELS,
-                hop_length=HOP_LENGTH, n_fft=N_FFT
-             )
-    mel_db = librosa.power_to_db(mel, ref=np.max)
-
-    # normalise to 0-1
-    mel_db = (mel_db - mel_db.min()) / (mel_db.max() - mel_db.min() + 1e-6)
-
-    # pad or crop to exactly 512 time frames
-    if mel_db.shape[1] >= 512:
-        mel_db = mel_db[:, :512]
-    else:
-        mel_db = np.pad(mel_db, ((0, 0), (0, 512 - mel_db.shape[1])))
-
-    return mel_db.astype(np.float32)   # (128, 512)
-
-
-def extract_3_crops(y):
-    """Return [start, center, end] crops of exactly N_SAMPLES each."""
-    total = len(y)
-
-    if total <= N_SAMPLES:
-        y = np.pad(y, (0, N_SAMPLES - total))
-        return [y, y, y]
-
-    start  = y[:N_SAMPLES]
-    mid_s  = (total - N_SAMPLES) // 2
-    center = y[mid_s : mid_s + N_SAMPLES]
-    end    = y[total - N_SAMPLES:]
-    return [start, center, end]
-
-
-# ── Main prediction function ──────────────────────────────────────────────────
-def predict_genre(audio_path):
-    """
-    Takes an audio file path from Gradio,
-    returns a dict of {genre: probability} for the label display.
-    """
-    if audio_path is None:
-        return {}
-
-    # 1. Load audio (librosa handles mp3, wav, flac, ogg, etc.)
-    try:
-        y, _ = librosa.load(audio_path, sr=SR, mono=True)
-    except Exception as e:
-        return {f"Error loading audio: {str(e)}": 1.0}
-
-    # 2. Extract 3 crops for test-time augmentation
-    crops = extract_3_crops(y)
-
-    # 3. Convert each crop to a mel spectrogram tensor
-    #    Shape per crop: (1, 1, 128, 512)  ← batch=1, channel=1, H, W
-    mel_tensors = [
-        torch.tensor(get_mel_spectrogram(crop)).unsqueeze(0).unsqueeze(0)
-        for crop in crops
-    ]
-
-    # 4. Run all 3 crops through the model, average probabilities (TTA)
-    probs = torch.zeros(1, N_CLASSES)
-
-    with torch.no_grad():
-        for mel in mel_tensors:
-            logits  = model(mel.to(DEVICE))
-            probs  += torch.softmax(logits, dim=1).cpu()
-
-    probs /= 3.0   # average over 3 crops
-
-    # 5. Build output dict for Gradio's Label component
-    prob_np = probs.squeeze().numpy()
-    return {GENRES[i]: float(prob_np[i]) for i in range(N_CLASSES)}
-
-
-# ── Gradio UI ──────────────���──────────────────────────────────────────────────
-demo = gr.Interface(
-    fn=predict_genre,
-
-    inputs=gr.Audio(
-        type="filepath",
-        label="Upload an audio file (wav, mp3, flac, ogg — any genre)"
-    ),
-
-    outputs=gr.Label(
-        num_top_classes=10,
-        label="Genre probabilities"
-    ),
-
-    title="🎵 Music Genre Classifier",
-    description=(
-        "Upload any audio clip and the model will predict its genre.\n\n"
-        "**Genres:** Blues · Classical · Country · Disco · Hip-hop · "
-        "Jazz · Metal · Pop · Reggae · Rock\n\n"
-        "**How it works:** The audio is converted to a mel spectrogram "
-        "(a 2D image of frequency vs time), then passed through a CNN trained "
-        "on 27,000 synthetic audio mashups. Three time-crop predictions are "
-        "averaged for more reliable results."
-    ),
-
-    examples=[],   # add example file paths here if you upload samples
-
-    allow_flagging="never",
-)
-
-if __name__ == "__main__":
-    demo.launch()