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import gradio as gr
import joblib
import numpy as np
import os
import re
import subprocess
NAME_TO_PC = {
"C":0,"C#":1,"Db":1,"D":2,"D#":3,"Eb":3,"E":4,"F":5,"F#":6,"Gb":6,
"G":7,"G#":8,"Ab":8,"A":9,"A#":10,"Bb":10,"B":11
}
NOTE_TOKEN_RE = re.compile(r"[A-Ga-g](?:#|b)?")
def notes_to_vector(notes_str: str):
tokens = NOTE_TOKEN_RE.findall(notes_str)
pcs = [NAME_TO_PC.get(t.upper(), None) for t in tokens]
pcs = [p for p in pcs if p is not None]
vec = np.zeros(12)
for p in pcs:
vec[p] = 1
return vec
MODEL_PATH = "chord_classifier.pkl"
def load_model():
if not os.path.exists(MODEL_PATH):
print("⚠️ chord_classifier.pkl not found. Training model...")
subprocess.run(["python", "train_chord_model.py"], check=True)
return joblib.load(MODEL_PATH)
clf = load_model()
def chord_bot(message: str, history: list[tuple[str,str]]):
vec = notes_to_vector(message)
if np.sum(vec) < 2:
return "⚠️ Please enter at least 2 distinct notes (e.g., C E G)"
label = clf.predict([vec])[0]
return f"🎵 Identified chord: **{label}**"
chatbot = gr.ChatInterface(
fn=chord_bot,
title="🎶 ML Chord Bot",
description="Enter 2+ notes (e.g., C E G or Db F Ab C). Powered by a trained RandomForest classifier."
)
if __name__ == "__main__":
chatbot.launch()
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