117 lines
4.1 KiB
Python
117 lines
4.1 KiB
Python
import tempfile
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from pathlib import Path
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import joblib
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import streamlit as st
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from audio_utils import SUPPORTED_AUDIO_EXTENSIONS, convert_to_wav
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from features import LABEL_PD, LABEL_TPD, extract_features
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BASE_DIR = Path(__file__).resolve().parent
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MODEL_PATH = BASE_DIR / "models" / "svm_voice_confidence_model.joblib"
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CONFIDENCE_THRESHOLD = 0.75
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MARGIN_THRESHOLD = 0.25
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SUPPORTED_UPLOAD_TYPES = [extension.replace(".", "") for extension in sorted(SUPPORTED_AUDIO_EXTENSIONS)]
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LABEL_DESCRIPTION = {
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LABEL_PD: "Percaya Diri",
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LABEL_TPD: "Tidak Percaya Diri",
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}
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@st.cache_resource
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def load_model(model_mtime):
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"""
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model_mtime dipakai sebagai cache key.
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Jika model dilatih ulang dan file berubah, Streamlit otomatis memuat model baru.
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"""
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return joblib.load(MODEL_PATH)
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def save_uploaded_file(uploaded_file, suffix):
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with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as temp_audio:
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temp_audio.write(uploaded_file.getbuffer())
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return Path(temp_audio.name)
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def predict_uploaded_audio(uploaded_file):
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model = load_model(MODEL_PATH.stat().st_mtime)
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original_extension = Path(uploaded_file.name).suffix.lower()
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if original_extension not in SUPPORTED_AUDIO_EXTENSIONS:
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allowed = ", ".join(SUPPORTED_UPLOAD_TYPES).upper()
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raise ValueError(f"Format file tidak didukung. Gunakan salah satu: {allowed}")
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temp_input_path = save_uploaded_file(uploaded_file, original_extension)
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temp_wav_path = Path(tempfile.NamedTemporaryFile(delete=False, suffix=".wav").name)
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# Semua input disiapkan ulang menjadi WAV mono 22050 Hz sebelum ekstraksi fitur.
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try:
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convert_to_wav(temp_input_path, temp_wav_path)
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features = extract_features(temp_wav_path).reshape(1, -1)
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expected_features = model.named_steps["scaler"].n_features_in_
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if features.shape[1] != expected_features:
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raise ValueError(
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"Jumlah fitur audio tidak sesuai dengan model. "
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f"Audio menghasilkan {features.shape[1]} fitur, "
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f"sedangkan model mengharapkan {expected_features}. "
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"Jalankan ulang `python train_model.py`, lalu restart Streamlit."
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)
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predicted_label = model.predict(features)[0]
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probabilities = model.predict_proba(features)[0]
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class_probabilities = dict(zip(model.classes_, probabilities))
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confidence = class_probabilities[predicted_label]
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finally:
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temp_input_path.unlink(missing_ok=True)
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temp_wav_path.unlink(missing_ok=True)
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return predicted_label, confidence, class_probabilities
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st.set_page_config(
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page_title="Klasifikasi Percaya Diri dari Suara",
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layout="centered",
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)
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st.title("Klasifikasi Percaya Diri dari Suara")
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st.write("Upload file audio untuk memprediksi kelas PD atau TPD.")
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if not MODEL_PATH.exists():
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st.error("Model belum ditemukan. Jalankan `python train_model.py` terlebih dahulu.")
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st.stop()
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uploaded_file = st.file_uploader(
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"Pilih file audio",
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type=SUPPORTED_UPLOAD_TYPES,
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)
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if uploaded_file is not None:
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uploaded_extension = Path(uploaded_file.name).suffix.lower().replace(".", "")
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st.audio(uploaded_file, format=f"audio/{uploaded_extension}")
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if st.button("Prediksi"):
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with st.spinner("Mengekstraksi fitur dan memprediksi..."):
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try:
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label, confidence, probabilities = predict_uploaded_audio(uploaded_file)
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except Exception as error:
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st.error(f"Gagal memproses audio: {error}")
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st.stop()
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st.subheader("Hasil Prediksi")
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probability_pd = probabilities.get(LABEL_PD, 0.0)
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probability_tpd = probabilities.get(LABEL_TPD, 0.0)
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margin = abs(probability_pd - probability_tpd)
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if confidence >= CONFIDENCE_THRESHOLD and margin >= MARGIN_THRESHOLD:
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st.success(f"Hasil utama: {label} - {LABEL_DESCRIPTION[label]}")
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else:
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st.warning(
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"Model belum cukup yakin. Suara perlu direkam ulang atau data training perlu ditambah."
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)
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st.progress(float(probability_pd), text=f"PD: {probability_pd * 100:.2f}%")
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st.progress(float(probability_tpd), text=f"TPD: {probability_tpd * 100:.2f}%")
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