Upload app.py

app.py

@@ -1,13 +1,50 @@
 import gradio as gr
+import librosa
+from tensorflow.keras.models import load_model
+import numpy as np
+
+# load model
+model = load_model("BBBN_model.hdf5")
+
+# basic variables for mel spectrogram
+target_sr = 22050
+frame_size = 2048
+frame_shift_len = 1024
+n_mels = 128
+
+genre_classes = {
+    0: "Blues",
+    1: "Classical",
+    2: "Country",
+    3: "Disco",
+    4: "Hiphop",
+    5: "Jazz",
+    6: "Metal",
+    7: "Pop",
+    8: "Reggae",
+    9: "Rock"
+}
+def get_melspec_feature(X, target_sr, frame_size, frame_shift_len, n_mels):
+    melspec_feature = []
+    for audio in X:
+        audio_melspec = librosa.feature.melspectrogram(y=audio, sr=target_sr, n_fft=frame_size, hop_length=frame_shift_len)
+        audio_melspec = librosa.power_to_db(audio_melspec)
+        audio_melspec = audio_melspec.T
+        melspec_feature.append(audio_melspec)
+    return np.array(melspec_feature, dtype=np.float32)
+
+
+def predict_genre(audio):
+    melspec = get_melspec_feature(audio, target_sr, frame_size, frame_shift_len, n_mels)
+    prediction = model.predict(melspec)[0]
+    return {genre_classes[i]: float(prediction[i]) for i in range(5)}
+
 
-def greet(name):
-    return "Hello " + name + "!!"
-ds = ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"]
 iface = gr.Interface(
-    greet, 
+    predict_genre, 
     inputs=gr.inputs.Audio(),
-    outputs=['textarea', 'textarea'],
-    allow_flagging="never",
+    outputs=gr.outputs.Label(num_top_classes=5),
+    title="Music Genre Classifier",
     live=True
 )
 iface.launch()