Update app.py

app.py

@@ -13,7 +13,7 @@ Notes:
  - This version includes a robust regex-based extractor that finds the outermost {...} block
    in the LLM output, extracts numeric values for the required keys, and always returns
    numeric defaults (no NaN) so frontends will not receive null/None for numeric fields.
- - This variant logs raw LLM output and the parsed JSON using Python logging.
+ - This variant logs raw VLM & LLM outputs and the parsed JSON using Python logging.
 """
 
 import io
@@ -276,7 +276,8 @@ def run_vlm_and_get_features(face_path: str, eye_path: str, prompt: Optional[str
        (parsed_features_dict_or_None, raw_text_response_str)
 
     We attempt to parse JSON as before, but always return the original raw text so it can be
-    forwarded verbatim to the LLM if desired.
+    forwarded verbatim to the LLM if desired. This function now logs the raw VLM output and
+    the parsed features for debugging.
     """
     prompt = prompt or DEFAULT_VLM_PROMPT
     if not os.path.exists(face_path) or not os.path.exists(eye_path):
@@ -312,6 +313,12 @@ def run_vlm_and_get_features(face_path: str, eye_path: str, prompt: Optional[str
     if not text_out:
         text_out = json.dumps(out)
 
+    # Log raw VLM output for debugging/auditing
+    try:
+        logger.info("VLM raw output:\n%s", text_out)
+    except Exception:
+        logger.info("VLM raw output (could not pretty print)")
+
     # Try to parse JSON but remember raw text always
     parsed_features = None
     try:
@@ -333,6 +340,15 @@ def run_vlm_and_get_features(face_path: str, eye_path: str, prompt: Optional[str
         except Exception:
             parsed_features = None
 
+    # Log parsed features if available
+    if parsed_features is not None:
+        try:
+            logger.info("VLM parsed features:\n%s", json.dumps(parsed_features, indent=2, ensure_ascii=False))
+        except Exception:
+            logger.info("VLM parsed features (raw): %s", str(parsed_features))
+    else:
+        logger.info("VLM parsed features: None (raw output kept)")
+
     return parsed_features, text_out
 
 # -----------------------
@@ -735,6 +751,10 @@ async def get_vitals_from_upload(
         # Run VLM (off the event loop)
         vlm_features, vlm_raw = await asyncio.to_thread(run_vlm_and_get_features, face_path, eye_path)
 
+        # Log VLM outputs (already logged inside run_vlm..., but log again with context)
+        logger.info("get_vitals_from_upload - VLM raw (snippet): %s", (vlm_raw[:100] + "...") if vlm_raw else "None")
+        logger.info("get_vitals_from_upload - VLM parsed features: %s", vlm_features if vlm_features is not None else "None")
+
         # Prefer sending raw vlm text to LLM (same behavior as process_screening)
         llm_input = vlm_raw if vlm_raw else (vlm_features if vlm_features else "{}")
 
@@ -770,6 +790,10 @@ async def get_vitals_for_screening(screening_id: str):
         # Run VLM off the event loop
         vlm_features, vlm_raw = await asyncio.to_thread(run_vlm_and_get_features, face_path, eye_path)
 
+        # Log VLM outputs
+        logger.info("get_vitals_for_screening(%s) - VLM raw (snippet): %s", screening_id, (vlm_raw[:100] + "...") if vlm_raw else "None")
+        logger.info("get_vitals_for_screening(%s) - VLM parsed features: %s", screening_id, vlm_features if vlm_features is not None else "None")
+
         llm_input = vlm_raw if vlm_raw else (vlm_features if vlm_features else "{}")
         structured_risk = await asyncio.to_thread(run_llm_on_vlm, llm_input)
 
@@ -901,6 +925,10 @@ async def process_screening(screening_id: str):
             vlm_features = None
             vlm_raw = None
 
+        # Log VLM outputs in pipeline context
+        logger.info("process_screening(%s) - VLM raw (snippet): %s", screening_id, (vlm_raw[:100] + "...") if vlm_raw else "None")
+        logger.info("process_screening(%s) - VLM parsed features: %s", screening_id, vlm_features if vlm_features is not None else "None")
+
         # --------------------------
         # RUN LLM on vlm_raw (preferred) or vlm_features -> structured risk JSON
         # --------------------------