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+# ---------- Public HF Space: Streamlit frontend ----------
+FROM python:3.11-slim
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    ca-certificates \
+ && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy frontend app
+COPY app.py /app/app.py
+
+# Expose default Spaces port
+EXPOSE 7860
+
+# Streamlit must bind to 0.0.0.0 and the provided port
+# Use ${PORT} if set by the platform, otherwise fall back to 7860.
+CMD ["sh", "-c", "streamlit run app.py --server.address=0.0.0.0 --server.port=${PORT:-7860}"]

app.py

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+# app.py — DiagStudio AI (Streamlit frontend for public HF Space)
+# - Talks to your private FastAPI backend:
+#     POST {API_BASE}/analyze
+#     POST {API_BASE}/analyze_zip
+# - Friendly UI for non-technical users; no DSP jargon on the surface.
+
+import os
+import io
+import json
+import time
+import requests
+import streamlit as st
+
+# ================= Theme (no extra files) =================
+try:
+    st._config.set_option("theme.base", "dark")
+    st._config.set_option("theme.primaryColor", "#ff7a1a")
+    st._config.set_option("theme.backgroundColor", "#0e0f12")
+    st._config.set_option("theme.secondaryBackgroundColor", "#171a1f")
+    st._config.set_option("theme.textColor", "#e6e8ea")
+except Exception:
+    pass
+
+# ================= Env & API =================
+def _env(k): 
+    return (os.getenv(k) or "").strip().strip("'\"")
+
+API_BASE = (
+    _env("CARDIAG_AI_API") 
+    or _env("AI_LIGHTBOX_API")
+    or _env("LUXFIT_API")
+).rstrip("/") if (
+    _env("CARDIAG_AI_API") or _env("AI_LIGHTBOX_API") or _env("LUXFIT_API")
+) else ""
+
+HF_TOKEN = _env("CARDIAG_AI_TOKEN") or _env("AI_LIGHTBOX_TOKEN") or _env("LUXFIT_TOKEN")
+HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
+
+# ================= App Header =================
+st.set_page_config(page_title="DiagStudio AI", layout="wide")
+st.title("DiagStudio AI")
+st.caption("Upload a short engine recording and get a clear, visual, and textual diagnosis report.")
+
+# Simple validation for API base
+if not API_BASE:
+    st.error("Backend URL is not set. Define CARDIAG_AI_API (or AI_LIGHTBOX_API / LUXFIT_API).")
+    st.stop()
+
+# Health check
+@st.cache_data(show_spinner=False, ttl=30)
+def check_health():
+    try:
+        r = requests.get(f"{API_BASE}/health", headers=HEADERS, timeout=10)
+        return r.status_code == 200
+    except Exception:
+        return False
+
+ok = check_health()
+if not ok:
+    st.warning("Could not reach the backend. Please verify your private Space is running and accessible.")
+
+# ================= Helpers =================
+def _nice_conf(p):
+    try:
+        v = float(p)
+        return min(max(v, 0.0), 1.0)
+    except Exception:
+        return 0.0
+
+def _post_analyze(audio_file, rpm_file, sr_target, n_fft, hop, env_band, run_llm, notes):
+    files = {}
+    if audio_file is not None:
+        files["audio"] = (audio_file.name, audio_file.getvalue(), audio_file.type or "application/octet-stream")
+    if rpm_file is not None:
+        files["rpm_csv"] = (rpm_file.name, rpm_file.getvalue(), "text/csv")
+
+    data = {
+        "sr_target": str(sr_target),
+        "n_fft": str(n_fft),
+        "hop": str(hop),
+        "env_band": env_band,
+        "run_llm": "true" if run_llm else "false",
+        "extra_text": notes or "",
+    }
+
+    # Multipart form-data via requests.files -> backend expects UploadFile/File. :contentReference[oaicite:1]{index=1}
+    r = requests.post(f"{API_BASE}/analyze", headers=HEADERS, files=files, data=data, timeout=120)
+    r.raise_for_status()
+    return r.json()
+
+def _post_analyze_zip(audio_file, rpm_file, sr_target, n_fft, hop, env_band, run_llm, notes):
+    files = {}
+    if audio_file is not None:
+        files["audio"] = (audio_file.name, audio_file.getvalue(), audio_file.type or "application/octet-stream")
+    if rpm_file is not None:
+        files["rpm_csv"] = (rpm_file.name, rpm_file.getvalue(), "text/csv")
+
+    data = {
+        "sr_target": str(sr_target),
+        "n_fft": str(n_fft),
+        "hop": str(hop),
+        "env_band": env_band,
+        "run_llm": "true" if run_llm else "false",
+        "extra_text": notes or "",
+    }
+
+    r = requests.post(f"{API_BASE}/analyze_zip", headers=HEADERS, files=files, data=data, timeout=180, stream=True)
+    r.raise_for_status()
+    return r.content  # bytes for st.download_button. :contentReference[oaicite:2]{index=2}
+
+def _dataurl_to_bytes(data_url: str) -> bytes:
+    # "data:image/png;base64,...."
+    b64 = data_url.split(",", 1)[1]
+    return base64_to_bytes(b64)
+
+def base64_to_bytes(b64: str) -> bytes:
+    import base64
+    return base64.b64decode(b64)
+
+# ================= Sidebar =================
+with st.sidebar:
+    st.header("Upload")
+    audio_up = st.file_uploader("Engine recording (WAV/MP3/M4A)", type=["wav", "mp3", "m4a"])  # st.file_uploader returns UploadedFile. :contentReference[oaicite:3]{index=3}
+    rpm_up = st.file_uploader("Optional RPM CSV (time_sec,rpm)", type=["csv"])
+
+    st.header("Options")
+    run_llm = st.toggle("Explain with AI", value=True, help="Adds an easy-to-read diagnosis and next steps.")
+    user_notes = st.text_area("Context (optional)", placeholder="Vehicle, engine type, driving condition, symptoms…")
+
+    with st.expander("Advanced (defaults are fine)"):
+        sr_target = st.select_slider("Sample rate (Hz)", options=[16000, 22050, 32000, 44100, 48000], value=22050)
+        n_fft = st.select_slider("Analysis window (n_fft)", options=[1024, 2048, 4096], value=2048)
+        hop = st.select_slider("Hop length", options=[128, 256, 512, 1024], value=512)
+        env_band = st.text_input("Impact band (Hz low,high)", value="1000,8000")
+
+    disabled = audio_up is None or not ok
+    analyze_btn = st.button("Analyze now", type="primary", disabled=disabled)
+
+# ================= Main UI =================
+status_ph = st.empty()
+
+if analyze_btn and audio_up is not None:
+    status_ph.info("Analyzing… This usually takes a few seconds.")
+    t0 = time.time()
+    try:
+        data = _post_analyze(
+            audio_file=audio_up, rpm_file=rpm_up,
+            sr_target=sr_target, n_fft=n_fft, hop=hop,
+            env_band=env_band, run_llm=run_llm, notes=user_notes
+        )
+        elapsed = time.time() - t0
+        status_ph.success(f"Done in {elapsed:.1f}s")
+        st.session_state["last_result"] = data
+    except requests.HTTPError as e:
+        status_ph.error(f"Backend error: {e.response.status_code} — {e.response.text[:300]}")
+    except Exception as e:
+        status_ph.error(f"Request failed: {e}")
+
+# Show results if present
+res = st.session_state.get("last_result")
+if res:
+    # -------- Summary cards (user-friendly text) --------
+    st.subheader("Result")
+    col1, col2 = st.columns([1,1])
+
+    # AI diagnosis (JSON). We tolerate either dict or string.
+    llm = res.get("llm_report") or {}
+    if isinstance(llm, str):
+        try:
+            llm = json.loads(llm)
+        except Exception:
+            llm = {"raw": llm}
+
+    diag_list = llm.get("diagnosis", [])
+    notes = llm.get("notes", "")
+    next_tests = llm.get("next_tests", "")
+
+    with col1:
+        st.markdown("**Likely issues**")
+        if diag_list:
+            for item in diag_list:
+                label = str(item.get("label", "Issue"))
+                conf = _nice_conf(item.get("likelihood", 0))
+                evidence = str(item.get("evidence", ""))
+                st.text(label)
+                st.progress(conf)
+                if evidence:
+                    st.caption(evidence)
+        else:
+            st.info("No specific faults detected with high confidence.")
+
+    with col2:
+        if notes:
+            st.markdown("**Mechanic-friendly notes**")
+            st.write(notes)
+        if next_tests:
+            st.markdown("**Suggested next checks**")
+            st.write(next_tests)
+
+    # -------- Visuals in tabs (friendly labels, no DSP jargon) --------
+    st.subheader("Visual checks")
+    tabs = st.tabs([
+        "Energy over time",
+        "Perceptual view",
+        "Impact pattern",
+        "Signal envelope",
+        "Detail view",
+        "Order view"
+    ])  # st.tabs API. :contentReference[oaicite:4]{index=4}
+
+    imgs = res.get("images", {})
+
+    with tabs[0]:
+        if "stft" in imgs:
+            st.image(imgs["stft"], caption="Energy distribution across time.")
+
+    with tabs[1]:
+        if "mel" in imgs:
+            st.image(imgs["mel"], caption="Perceptually-weighted overview.")
+
+    with tabs[2]:
+        if "envelope_spectrum" in imgs:
+            st.image(imgs["envelope_spectrum"], caption="Repetitive impact signature.")
+
+    with tabs[3]:
+        if "envelope_trace" in imgs:
+            st.image(imgs["envelope_trace"], caption="Overall intensity trace.")
+
+    with tabs[4]:
+        if "cwt" in imgs:
+            st.image(imgs["cwt"], caption="Fine details and short events.")
+
+    with tabs[5]:
+        if "order_map" in imgs:
+            st.image(imgs["order_map"], caption="Patterns that track engine speed.")
+        else:
+            st.caption("Order view requires an RPM CSV (time_sec,rpm).")
+
+    st.divider()
+
+    # -------- Download ZIP --------
+    st.subheader("Download")
+    st.caption("Get all visuals and the diagnosis as a single ZIP.")
+    if st.button("Prepare ZIP"):
+        try:
+            zbytes = _post_analyze_zip(
+                audio_file=audio_up, rpm_file=rpm_up,
+                sr_target=sr_target, n_fft=n_fft, hop=hop,
+                env_band=env_band, run_llm=run_llm, notes=user_notes
+            )
+            st.download_button(
+                "Download results (ZIP)",
+                data=zbytes,
+                file_name="diagstudio_results.zip",
+                mime="application/zip"
+            )  # st.download_button for binary files. :contentReference[oaicite:5]{index=5}
+        except Exception as e:
+            st.error(f"ZIP build failed: {e}")
+
+else:
+    st.info("Upload an engine recording, optionally add RPM CSV, then click Analyze.")

requirements.txt

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+streamlit>=1.36
+requests>=2.32