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import streamlit as st
import os
import tempfile
import time
from pathlib import Path
from modules.preprocessing import AudioPreprocessor
from modules.feature_extraction import FeatureExtractor
# from models.lightgbm import LightGBMModel
from models.xgboost import XGBoostModel
from modules.pipelines import ModelPipeline
import warnings
warnings.filterwarnings("ignore")
# Constants
MODEL_NAME = {
"XGBoost": XGBoostModel,
# "LightGBM": LightGBMModel,
}
# UI Layout
st.set_page_config(page_title="Audio Classification App", layout="centered")
st.title("🎧 Audio Classification")
st.markdown("Upload an `.mp3` or `.wav` file and select a model to get a prediction.")
# File Upload
uploaded_file = st.file_uploader("Upload your audio file", type=["wav", "mp3"])
# Model Selection
selected_model_name = st.selectbox("Select a model", list(MODEL_NAME.keys()))
# Process if file is uploaded
if uploaded_file is not None:
# Save uploaded file temporarily
with tempfile.TemporaryDirectory() as tmpdir:
audio_path = os.path.join(tmpdir, "input_audio.wav")
with open(audio_path, "wb") as f:
f.write(uploaded_file.read())
# Preprocess, extract features, predict
st.info("🔍 Processing audio...")
try:
# Initialize pipeline
preprocessor = AudioPreprocessor()
extractor = FeatureExtractor()
model = ModelPipeline(model=MODEL_NAME[selected_model_name])
model.load_model_from_registry(model_name=selected_model_name)
# Preprocess and predict
start_time = time.time()
y = preprocessor.preprocess(preprocessor.load_audio(audio_path, sr=16000))
if y is None:
st.error("Audio preprocessing failed.")
else:
x = extractor.extract(y, sr=16000, mode="traditional", n_mfcc=20)
pred = model.predict([x])[0]
elapsed = time.time() - start_time
# Display result
st.success(f"✅ Predicted Class: `{pred}`")
st.write(f"Inference time: `{elapsed:.4f}` seconds")
except Exception as e:
st.error(f"❌ An error occurred: {str(e)}")
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