Update app.py

app.py

@@ -2,7 +2,9 @@ import io
 import os
 import tempfile
 from pathlib import Path
+from typing import Optional
 
+# ---------- Paths & caches ----------
 BASE_DIR = Path(__file__).resolve().parent
 CACHE_ROOT = BASE_DIR / ".cache"
 NUMBA_CACHE_DIR = CACHE_ROOT / "numba"
@@ -12,14 +14,15 @@ for cache_dir in (NUMBA_CACHE_DIR, MPL_CACHE_DIR):
 os.environ.setdefault("NUMBA_CACHE_DIR", str(NUMBA_CACHE_DIR))
 os.environ.setdefault("MPLCONFIGDIR", str(MPL_CACHE_DIR))
 
+# ---------- Core deps ----------
 import joblib
 import numpy as np
 import pandas as pd
 import streamlit as st
 
-if not st.runtime.exists():.
+# If launched as `python app.py`, re-exec under `streamlit run ...`
+if not st.runtime.exists():
     from streamlit.web import cli as stcli
-
     import sys
 
     port = os.environ.get("PORT", "7860")
@@ -37,39 +40,30 @@ if not st.runtime.exists():.
     ]
     sys.exit(stcli.main())
 
+# ---------- Audio & plotting ----------
 import librosa
 import librosa.display
 import matplotlib.pyplot as plt
 
+# ---------- Local modules ----------
 from features import extract_features
 from devices import describe_label
 
-
+# ---------- Music key estimation helpers ----------
 NOTE_NAMES = [
-    "C",
-    "C#",
-    "D",
-    "D#",
-    "E",
-    "F",
-    "F#",
-    "G",
-    "G#",
-    "A",
-    "A#",
-    "B",
+    "C", "C#", "D", "D#", "E", "F",
+    "F#", "G", "G#", "A", "A#", "B",
 ]
 MAJOR_PROFILE = np.array([6.35, 2.23, 3.48, 2.33, 4.38, 4.09, 2.52, 5.19, 2.39, 3.66, 2.29, 2.88])
 MINOR_PROFILE = np.array([6.33, 2.68, 3.52, 5.38, 2.60, 3.53, 2.54, 4.75, 3.98, 2.69, 3.34, 3.17])
+
 UPLOAD_DIR = BASE_DIR / "uploads"
 MODEL_PATH = BASE_DIR / "models" / "model.pkl"
 ENCODER_PATH = BASE_DIR / "models" / "label_encoder.pkl"
-UPLOAD_DIR.mkdir(exist_ok=True)
-
+UPLOAD_DIR.mkdir(parents=True, exist_ok=True)
 
-def estimate_scale(y: np.ndarray, sr: int) -> str | None:
+def estimate_scale(y: np.ndarray, sr: int) -> Optional[str]:
     """Return a rough musical scale (e.g., 'C major') or None if unclear."""
-
     if y.size == 0:
         return None
     chroma = librosa.feature.chroma_cqt(y=y, sr=sr)
@@ -98,6 +92,7 @@ def estimate_scale(y: np.ndarray, sr: int) -> str | None:
         return f"{NOTE_NAMES[best_major]} major"
     return f"{NOTE_NAMES[best_minor]} minor"
 
+# ---------- UI ----------
 st.set_page_config(page_title="Mic-ID (MVP)", layout="centered")
 st.title("Mic-ID (MVP)")
 st.caption("Upload ~5s audio - guess the recording device")
@@ -105,15 +100,14 @@ st.caption("Upload ~5s audio - guess the recording device")
 with st.expander("Training data & devices", expanded=False):
     st.markdown(
         """
-        - **TAU Urban Acoustic Scenes 2019 Mobile**: 295 parallel scenes where the same moment was captured on three devices – Zoom F8 (device A, clips ending in `-a`), Samsung Galaxy S7 (device B, `-b`), and iPhone SE (device C, `-c`). We only keep folders containing a full `-a/-b/-c` triplet, so each mic has 295 clips.
-        - **Local additions**: 4 laptop and 4 iPhone recordings collected with `utils.py` to anchor the classifier on in-house gear.
-        - **Features & model**: log-mel + MFCC statistics flow into a histogram-based gradient boosting classifier tuned for this small balanced set.
+- **TAU Urban Acoustic Scenes 2019 Mobile**: 295 parallel scenes where the same moment was captured on three devices – Zoom F8 (device A, clips ending in `-a`), Samsung Galaxy S7 (device B, `-b`), and iPhone SE (device C, `-c`). We only keep folders containing a full `-a/-b/-c` triplet, so each mic has 295 clips.
+- **Local additions**: 4 laptop and 4 iPhone recordings collected with `utils.py` to anchor the classifier on in-house gear.
+- **Features & model**: log-mel + MFCC statistics flow into a histogram-based gradient boosting classifier tuned for this small balanced set.
 
-        Want more coverage? Record new clips under `data/<device>/` or export outtakes with `scripts/export_outtakes.py` before retraining via `python train.py`.
+Want more coverage? Record new clips under `data/<device>/` or export outtakes with `scripts/export_outtakes.py` before retraining via `python train.py`.
         """
     )
 
-
 @st.cache_resource
 def load_model():
     try:
@@ -124,7 +118,6 @@ def load_model():
         st.warning(f"Could not load trained artefacts: {exc}")
         return None, None
 
-
 clf, le = load_model()
 topk = None
 if clf and le is not None:
@@ -139,7 +132,7 @@ if clf and le is not None:
     )
     st.caption("The slider above only changes how many ranked predictions you see.")
 
-file = st.file_uploader("Upload WAV/MP3/M4A", type=["wav","mp3","m4a"])
+file = st.file_uploader("Upload WAV/MP3/M4A", type=["wav", "mp3", "m4a"])
 
 if file and clf and le is not None:
     data = file.read()
@@ -148,6 +141,8 @@ if file and clf and le is not None:
     saved_path = UPLOAD_DIR / renamed_name
     saved_path.write_bytes(data)
     st.caption(f"Saved a copy as `{saved_path}`.")
+
+    # Robust librosa load: in-memory first, fall back to temp file for odd formats
     try:
         y, sr = librosa.load(io.BytesIO(data), sr=16000, mono=True)
     except Exception:
@@ -156,34 +151,50 @@ if file and clf and le is not None:
             tmp.write(data)
             tmp.flush()
             y, sr = librosa.load(tmp.name, sr=16000, mono=True)
+
     raw_y = y.copy()
     rms = np.sqrt(np.mean(raw_y**2)) + 1e-8
     scale = estimate_scale(raw_y, sr)
-    y = raw_y * (0.05 / rms)  # simple RMS norm
+
+    # Simple RMS normalization to a modest level
+    y = raw_y * (0.05 / rms)
+
+    # Features -> classifier
     feats = extract_features(y, 16000).reshape(1, -1)
     proba = clf.predict_proba(feats)[0]
     idx = np.argsort(proba)[::-1]
+
     st.subheader("Prediction")
     if scale:
         st.write(f"Estimated scale: **{scale}** (experimental)")
     else:
         st.write("Scale detection: the clip lacked clear musical content, so no scale estimate.")
     st.write(f"Input loudness (RMS): {20 * np.log10(rms + 1e-12):.1f} dBFS")
+
     limit = topk or 3
     for i in idx[:limit]:
         label = le.classes_[i]
         st.write(f"{describe_label(label)} — **{proba[i] * 100:.1f}%**")
+
+    # Probability bar chart
     friendly_index = [describe_label(label) for label in le.classes_]
     st.bar_chart(pd.Series(proba, index=friendly_index))
 
+    # ---------- Visual explanation ----------
     with st.expander("How the model listens", expanded=False):
         st.markdown(
-            "We tidy the audio (level it, pull out key frequencies) and let the classifier score that summary. These charts show the raw waveform and the energy heatmap the model uses to decide." 
+            "We tidy the audio (level it, pull out key frequencies) and let the classifier score that summary. "
+            "These charts show the raw waveform and the energy heatmap the model uses to decide."
         )
 
         duration = raw_y.size / sr if raw_y.size else 0
-        times = np.linspace(0.0, duration, num=raw_y.size, endpoint=False) if raw_y.size else np.array([])
+        times = (
+            np.linspace(0.0, duration, num=raw_y.size, endpoint=False)
+            if raw_y.size
+            else np.array([])
+        )
 
+        # Waveform
         fig_wave, ax_wave = plt.subplots(figsize=(6, 2))
         if raw_y.size:
             ax_wave.plot(times, raw_y, linewidth=0.8, color="#1f77b4")
@@ -195,15 +206,19 @@ if file and clf and le is not None:
         st.pyplot(fig_wave, use_container_width=True)
         plt.close(fig_wave)
 
+        # Log-mel spectrogram
         mel = librosa.feature.melspectrogram(y=raw_y, sr=sr, n_fft=2048, hop_length=512, n_mels=64)
         mel_db = librosa.power_to_db(mel, ref=np.max) if mel.size else mel
         fig_spec, ax_spec = plt.subplots(figsize=(6, 3))
         if mel.size:
-            img = librosa.display.specshow(mel_db, sr=sr, hop_length=512, x_axis="time", y_axis="mel", ax=ax_spec)
+            img = librosa.display.specshow(
+                mel_db, sr=sr, hop_length=512, x_axis="time", y_axis="mel", ax=ax_spec
+            )
             cbar = fig_spec.colorbar(img, ax=ax_spec, format="%+2.f dB")
             cbar.set_label("Energy (dB)")
         ax_spec.set_title("Log-mel spectrogram (what the model summarises)")
         st.pyplot(fig_spec, use_container_width=True)
         plt.close(fig_spec)
+
 elif file and not clf:
     st.warning("No trained model found. Run `python train.py` first.")