Upd debugs

app.py

@@ -279,20 +279,22 @@ async def chat_endpoint(req: Request):
     query = query_raw.strip() if isinstance(query_raw, str) else ""
     lang    = body.get("lang", "EN")
     image_base64 = body.get("image_base64", None)
+    img_desc = body.get("img_desc", "Describe and investigate any clinical findings from this medical image.")
     # LLM Only
-    if not query and not image_base64:
+    if not image_base64:
         logger.info("[BOT] LLM scenario.")
     start = time.time()
     # If image is present → diagnose first
     image_diagnosis = ""
     # Img size safe processor
-    if image_base64 and len(image_base64.encode("utf-8")) > 5_000_000:
+    safe_load = len(image_base64.encode("utf-8"))
+    if image_base64 and safe_load > 5_000_000:
         return JSONResponse({"response": "⚠️ Image too large. Please upload smaller images (<5MB)."})
     # LLM+VLM
     if image_base64:
         logger.info("[BOT] VLM+LLM scenario.")
-        prompt = query or "Describe and investigate any clinical findings from this medical image."
-        image_diagnosis = process_medical_image(image_base64, prompt, lang)
+        logger.info(f"[VLM] Process medical image size: {safe_load}, desc: {img_desc}, {lang}.")
+        image_diagnosis = process_medical_image(image_base64, img_desc, lang)
     answer = chatbot.chat(user_id, query, lang, image_diagnosis)
     elapsed = time.time() - start
     # Final

vlm.py

@@ -7,7 +7,7 @@ from translation import translate_query
 HF_TOKEN = os.getenv("HF_TOKEN")
 client = InferenceClient(provider="auto", api_key=HF_TOKEN)
 
-logger = logging.getLogger("translation-agent")
+logger = logging.getLogger("vlm-agent")
 logging.basicConfig(level=logging.INFO, format="%(asctime)s — %(name)s — %(levelname)s — %(message)s", force=True) # Change INFO to DEBUG for full-ctx JSON loader
 
 def process_medical_image(base64_image: str, prompt: str = None, lang: str = "EN") -> str:
@@ -34,5 +34,5 @@ def process_medical_image(base64_image: str, prompt: str = None, lang: str = "EN
         logger.info(f"[VLM] MedGemma returned {result}")
         return result
     except Exception as e:
-        logger.error(f"⚠️ Error from image diagnosis model: {e}")
-        return f"⚠️ Error from image diagnosis model: {e}"
+        logger.error(f"[VLM] ⚠️ Error from image diagnosis model: {e}")
+        return f"[VLM] ⚠️ Error from image diagnosis model: {e}"