Upload app.py

app.py

@@ -1,11 +1,11 @@
-# app.py — DiagStudio AI (Streamlit frontend, fixed)
-# Root-cause fix: prevent Streamlit from writing to '/.streamlit'
-# by defining a writable HOME and disabling usage stats before import.
+# app.py — DiagStudio AI (Streamlit frontend, public HF Space)
+# Kök dizine yazma girişimlerini engellemek için HOME ve headless/statistics env'lerini set ediyoruz.
+# Streamlit konfig/env yöntemleri belgeli.  # :contentReference[oaicite:6]{index=6}
 
 import os
-# ----- MUST be set BEFORE importing streamlit -----
+# ---- set BEFORE importing streamlit ----
 os.environ.setdefault("HOME", "/tmp")
-os.environ.setdefault("STREAMLIT_BROWSER_GATHER_USAGE_STATS", "false")
+os.environ.setdefault("STREAMLIT_BROWSER_GATHER_USAGE_STATS", "false")  # :contentReference[oaicite:7]{index=7}
 os.environ.setdefault("STREAMLIT_SERVER_HEADLESS", "true")
 
 import io
@@ -15,7 +15,7 @@ import base64
 import requests
 import streamlit as st
 
-# ================= Theme (no extra files) =================
+# ================= Theme =================
 try:
     st._config.set_option("theme.base", "dark")
     st._config.set_option("theme.primaryColor", "#ff7a1a")
@@ -26,11 +26,11 @@ except Exception:
     pass
 
 # ================= Env & API =================
-def _env(k):
+def _env(k): 
     return (os.getenv(k) or "").strip().strip("'\"")
 
 API_BASE = (
-    _env("CARDIAG_AI_API")
+    _env("CARDIAG_AI_API") 
     or _env("AI_LIGHTBOX_API")
     or _env("LUXFIT_API")
 ).rstrip("/") if (
@@ -81,6 +81,7 @@ def _post_analyze(audio_file, rpm_file, sr_target, n_fft, hop, env_band, run_llm
         "extra_text": notes or "",
     }
 
+    # multipart/form-data upload akışı FastAPI UploadFile'a uygun  # :contentReference[oaicite:8]{index=8}
     r = requests.post(f"{API_BASE}/analyze", headers=HEADERS, files=files, data=data, timeout=180)
     r.raise_for_status()
     return r.json()
@@ -106,7 +107,6 @@ def _post_analyze_zip(audio_file, rpm_file, sr_target, n_fft, hop, env_band, run
     return r.content
 
 def dataurl_to_bytes(data_url: str) -> bytes:
-    # "data:image/png;base64,...."
     b64 = data_url.split(",", 1)[1]
     return base64.b64decode(b64)
 
@@ -156,15 +156,18 @@ if analyze_btn and audio_up is not None:
 res = st.session_state.get("last_result")
 if res:
     st.subheader("Result")
-    col1, col2 = st.columns([1,1])
 
+    # ---- Model & usage meta (proof that GPT-4o Vision ran) ----
     llm = res.get("llm_report") or {}
-    if isinstance(llm, str):
-        try:
-            llm = json.loads(llm)
-        except Exception:
-            llm = {"raw": llm}
-
+    model_name = llm.get("model", "unknown")
+    usage = llm.get("usage") or {}
+    with st.container():
+        st.caption(f"AI model: **{model_name}**")
+        if usage:
+            st.caption(f"Tokens — prompt: {usage.get('prompt_tokens')}, completion: {usage.get('completion_tokens')}")
+
+    # ---- Diagnosis summary ----
+    col1, col2 = st.columns([1,1])
     diag_list = llm.get("diagnosis", [])
     notes = llm.get("notes", "")
     next_tests = llm.get("next_tests", "")
@@ -174,10 +177,13 @@ if res:
         if diag_list:
             for item in diag_list:
                 label = str(item.get("label", "Issue"))
-                conf = _nice_conf(item.get("likelihood", 0))
+                conf = item.get("likelihood", 0)
                 evidence = str(item.get("evidence", ""))
                 st.text(label)
-                st.progress(conf)
+                try:
+                    st.progress(min(max(float(conf), 0.0), 1.0))
+                except Exception:
+                    st.progress(0.0)
                 if evidence:
                     st.caption(evidence)
         else:
@@ -191,6 +197,7 @@ if res:
             st.markdown("**Suggested next checks**")
             st.write(next_tests)
 
+    # ---- Visuals ----
     st.subheader("Visual checks")
     tabs = st.tabs([
         "Energy over time",
@@ -206,23 +213,18 @@ if res:
     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.")