Upload app.py

app.py

@@ -1,22 +1,21 @@
 # app.py — DiagStudio AI (Streamlit frontend)
-# Pairs with the FastAPI backend you built (composite image + JSON mode).
-# Configure the backend URL via one of:
-#   CARDIAG_AI_API  (preferred)  |  AI_LIGHTBOX_API  |  LUXFIT_API
-# Optional bearer auth passthrough:
+# Pairs with the FastAPI backend (composite image + JSON mode).
+# Backend URL via one of:
+#   CARDIAG_AI_API  |  AI_LIGHTBOX_API  |  LUXFIT_API
+# Optional bearer token passthrough:
 #   CARDIAG_AI_TOKEN | AI_LIGHTBOX_TOKEN | LUXFIT_TOKEN
 #
-# Run (example):
+# Run:
 #   CARDIAG_AI_API="https://<your-private-backend>" streamlit run app.py --server.port 7860
 
-import os, io, json, time, base64, requests, hashlib
+import os, json, time, hashlib, requests, io, base64
+import streamlit as st
 
-# Avoid /.streamlit permission errors on containers/HF
+# ---- container-safe defaults to avoid '/.streamlit' permission errors
 os.environ.setdefault("HOME", "/tmp")
 os.environ.setdefault("STREAMLIT_BROWSER_GATHER_USAGE_STATS", "false")
 os.environ.setdefault("STREAMLIT_SERVER_HEADLESS", "true")
 
-import streamlit as st
-
 # ================= Theme (no extra files) =================
 try:
     st._config.set_option("theme.base", "dark")
@@ -38,12 +37,13 @@ API_BASE = (
 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 {}
 
+# ================= Page =================
 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 diagnosis with visuals and next steps.")
 
 # ================= Helpers =================
-@st.cache_data(show_spinner=False, ttl=30)
+@st.cache_data(show_spinner=False, ttl=20)
 def _health(url: str, headers: dict) -> bool:
     try:
         r = requests.get(f"{url}/health", headers=headers, timeout=10)
@@ -70,7 +70,7 @@ def _post_analyze(audio_file, rpm_file, sr_target, n_fft, hop, env_band, run_llm
         "env_band": env_band,
         "run_llm": "true" if run_llm else "false",
         "extra_text": notes or "",
-        "nonce": str(time.time())  # cache-bypass hint; backend already sets no-store
+        "nonce": str(time.time())  # cache-bypass hint (backend already sets no-store)
     }
     r = requests.post(f"{API_BASE}/analyze", headers=HEADERS, files=files, data=data, timeout=240)
     r.raise_for_status()
@@ -111,14 +111,17 @@ with st.sidebar:
         hop = st.select_slider("Hop length", options=[128, 256, 512, 1024], value=512)
         env_band = st.text_input("Impact band (Hz low,high or 'auto')", value="auto")
 
+    # Allow manual URL override if env not set
     if not API_BASE:
-        st.error("Backend URL is not set. Define CARDIAG_AI_API (or AI_LIGHTBOX_API / LUXFIT_API).")
+        API_BASE = st.text_input("Backend URL", value="", placeholder="https://<your-private-backend>").strip().rstrip("/")
     ok = _health(API_BASE, HEADERS) if API_BASE else False
-    if not ok and API_BASE:
-        st.warning("Could not reach the backend. Check your private Space URL.")
+    if not API_BASE:
+        st.error("Backend URL missing. Set CARDIAG_AI_API env or enter it above.")
+    elif not ok:
+        st.warning("Backend not reachable. Check URL or token.")
     analyze_btn = st.button("Analyze now", type="primary", disabled=(audio_up is None or not ok))
 
-# ================= Reset state on file change (prevents stale results) =================
+# ================= Reset state on file change =================
 if "last_audio_id" not in st.session_state:
     st.session_state["last_audio_id"] = None
 if audio_up is not None:
@@ -150,11 +153,9 @@ if res:
 
     params = res.get("params", {})
     llm = res.get("llm_report") or {}
+    imgs = res.get("images", {}) if isinstance(res.get("images", {}), dict) else {}
 
-    model_name = params.get("vision_model", "gpt-4o")
-    st.caption(f"AI model: **{model_name}**")
-
-    # Input uniqueness proof + quick features
+    st.caption(f"AI model: **{params.get('vision_model','gpt-4o')}**")
     if params.get("audio_id"):
         st.caption(f"Audio ID: **{params['audio_id']}**")
     if params.get("features"):
@@ -168,7 +169,6 @@ if res:
         except Exception:
             st.caption(f"Features — {feat}")
 
-    # Show meta proving Vision read the image
     if isinstance(llm, dict) and llm.get("meta"):
         meta = llm["meta"]
         st.caption(f"Vision meta — seen_images: {meta.get('seen_images')}, views: {meta.get('views')}")
@@ -208,13 +208,10 @@ if res:
         "Signal envelope",
         "Order view",
     ])
-    imgs = res.get("images", {}) if isinstance(res.get("images", {}), dict) else {}
 
     with tabs[0]:
-        if imgs.get("composite"):
-            st.image(imgs["composite"], caption="Composite diagnostic figure")
-        else:
-            st.caption("Composite image is not available.")
+        if imgs.get("composite"): st.image(imgs["composite"], caption="Composite diagnostic figure")
+        else: st.caption("Composite image is not available.")
 
     with tabs[1]:
         if imgs.get("stft"): st.image(imgs["stft"], caption="Energy distribution across time.")
@@ -251,4 +248,4 @@ if res:
         except Exception as e:
             st.error(f"ZIP build failed: {e}")
 else:
-    st.info("Upload an engine recording, optionally add an RPM CSV, then click Analyze.")
+    st.info("Upload an engine recording, optionally add an RPM CSV, then click Analyze.")