predict_genre function

app.py

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+import gradio as gr
+import joblib
+import numpy as np
+
+# Load your saved joblib model, scaler, label_encoder, feature order
+d = joblib.load("genre_classifier.joblib")
+clf = d["model"]
+scaler = d["scaler"]
+le = d["label_encoder"]
+feature_order = d["features"]  # Should match what your web UI sends
+
+def predict_genre(
+    danceability, energy, key, loudness, mode, speechiness, acousticness,
+    instrumentalness, liveness, valence, tempo, time_signature
+):
+    # Pack input as expected by model
+    X = np.array([[
+        danceability, energy, key, loudness, mode, speechiness, acousticness,
+        instrumentalness, liveness, valence, tempo, time_signature
+    ]])
+    X_scaled = scaler.transform(X)
+    pred = clf.predict(X_scaled)
+    label = le.inverse_transform(pred)[0]
+    return label
+
+# For API: single call with all features
+iface = gr.Interface(
+    fn=predict_genre,
+    inputs=[
+        gr.Number(label=f) for f in feature_order
+    ],
+    outputs=gr.Text(label="Predicted Genre"),
+    title="Billboard Genre Classifier",
+    description="Predicts the genre from audio features. For API usage, send a POST to /run/predict.",
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch()

genre_classifier.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cc0e0fd86f4b93ca401d555e2e366528886bcb7ac76d1e4065ddfb9fc7bd4f89
+size 61085604

requirements.txt

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+scikit-learn
+joblib
+gradio