Create main_app.py

main_app.py

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+import os, io, pathlib, urllib.request
+import numpy as np
+import streamlit as st
+from PIL import Image
+from matplotlib import cm
+
+st.write("### ✅ Voice Guard Streamlit — env-only v4 (no st.secrets)")
+
+# ---- import Detector from app/ or src/ ----
+Detector, _last_err = None, None
+for mod in ["app.inference_wav2vec", "app.inference",
+            "src.inference_wav2vec", "src.inference"]:
+    try:
+        Detector = __import__(mod, fromlist=["Detector"]).Detector
+        break
+    except Exception as e:
+        _last_err = e
+if Detector is None:
+    st.error(f"Could not import Detector from app/ or src/. Last error: {_last_err}")
+    st.stop()
+
+# ---- ENV config only ----
+def cfg(name: str, default: str = "") -> str:
+    v = os.getenv(name)
+    return v if v not in (None, "") else default
+
+def ensure_weights() -> str:
+    wp  = cfg("MODEL_WEIGHTS_PATH", "app/models/weights/wav2vec2_classifier.pth")
+    url = cfg("MODEL_WEIGHTS_URL", "")
+    dest = pathlib.Path(wp)
+    if not dest.exists() and url:
+        dest.parent.mkdir(parents=True, exist_ok=True)
+        with st.spinner(f"Downloading model weights to {dest} …"):
+            urllib.request.urlretrieve(url, str(dest))
+            st.toast("Weights downloaded", icon="✅")
+    if not dest.exists() and not url:
+        st.warning(
+            f"Model weights not found at '{wp}'. "
+            "Upload the .pth there OR set MODEL_WEIGHTS_URL in Settings → Variables & secrets."
+        )
+    return str(dest)
+
+@st.cache_resource(show_spinner=True)
+def load_detector():
+    return Detector(weights_path=ensure_weights())
+
+det = load_detector()
+
+# ---- helpers ----
+def cam_to_png_bytes(cam: np.ndarray) -> bytes:
+    cam = np.asarray(cam, dtype=np.float32)
+    cam = np.nan_to_num(cam, nan=0.0); cam = np.clip(cam, 0.0, 1.0)
+    rgb = (cm.magma(cam)[..., :3] * 255).astype(np.uint8)
+    buf = io.BytesIO(); Image.fromarray(rgb).save(buf, "PNG")
+    return buf.getvalue()
+
+def analyze(wav_bytes: bytes, source_hint: str):
+    proba = det.predict_proba(wav_bytes, source_hint=source_hint)
+    exp   = det.explain(wav_bytes, source_hint=source_hint)
+    return proba, exp
+
+# ---- UI ----
+st.set_page_config(page_title="Voice Guard", page_icon="🛡️", layout="wide")
+st.title("🛡️ Voice Guard — Human vs AI Speech")
+
+left, right = st.columns([1,2], gap="large")
+with left:
+    st.subheader("Input")
+    tab_rec, tab_up = st.tabs(["🎙️ Microphone", "📁 Upload"])
+    wav_bytes, source_hint = None, None
+
+    with tab_rec:
+        st.caption("Record ~3–7 s. If mic fails, use Upload.")
+        try:
+            from audio_recorder_streamlit import audio_recorder
+            audio = audio_recorder(text="Record",
+                                   recording_color="#ff6a00",
+                                   neutral_color="#2b2b2b",
+                                   icon_size="2x")
+            if audio:
+                wav_bytes, source_hint = audio, "microphone"
+                st.audio(wav_bytes, format="audio/wav")
+        except Exception:
+            st.info("Recorder not available—use Upload tab.")
+
+    with tab_up:
+        f = st.file_uploader("Upload wav/mp3/m4a/aac", type=["wav","mp3","m4a","aac"])
+        if f:
+            wav_bytes, source_hint = f.read(), "upload"
+            st.audio(wav_bytes)
+
+    st.markdown("---")
+    run = st.button("🔍 Analyze", type="primary", use_container_width=True,
+                    disabled=wav_bytes is None)
+
+with right:
+    st.subheader("Results")
+    if run and wav_bytes:
+        try:
+            with st.spinner("Analyzing…"):
+                proba, exp = analyze(wav_bytes, source_hint or "auto")
+            ph = float(proba.get("human",0.0)); pa = float(proba.get("ai",0.0))
+            label = (proba.get("label","human") or "human").upper()
+            thr = float(proba.get("threshold",0.5))
+            rule = proba.get("decision","threshold")
+            thr_src = proba.get("threshold_source","—")
+            rscore = proba.get("replay_score", None)
+
+            c1,c2,c3 = st.columns(3)
+            with c1: st.metric("Human", f"{ph*100:.1f}%")
+            with c2: st.metric("AI", f"{pa*100:.1f}%")
+            with c3:
+                color = "#22c55e" if label=="HUMAN" else "#fb7185"
+                st.markdown(f"**Final Label:** <span style='color:{color}'>{label}</span>", unsafe_allow_html=True)
+                st.caption(f"thr({thr_src})={thr:.2f} • rule={rule} • replay={'—' if rscore is None else f'{float(rscore):.2f}'}")
+
+            st.markdown("##### Explanation Heatmap")
+            cam = np.asarray(exp.get("cam"), dtype=np.float32)
+            st.image(cam_to_png_bytes(cam), caption="Spectrogram importance", use_column_width=True)
+
+            with st.expander("Raw JSON (debug)"):
+                st.json({"proba": proba, "explain": {"cam_shape": list(cam.shape)}})
+        except Exception as e:
+            st.error(f"Analyze failed: {e}")
+
+st.caption("Upload 3–7s clips for the most reliable experience across browsers.")