Improved NLP Logic#2

nlp_service.py

@@ -2,7 +2,7 @@
 import json
 from model_setup import zero_shot, ner # Assuming model_setup.py exists and is correct
 from utils import parse_entities      # Assuming utils.py exists and is correct
-from config import CATEGORY_KEYWORDS  # Import categories from config
+from config import CATEGORY_KEYWORDS, QUERY_KEYWORDS  # Import categories and query keywords from config
 
 def analyze_text(text: str) -> dict:
     """
@@ -38,31 +38,41 @@ def analyze_text(text: str) -> dict:
             "error": str(e)
         }
 
-    # Step 2: Check if intent requires fallback (e.g., Gemini route)
-    if intent == "query":
-        print(f"NLP Service: Intent classified as '{intent}'. Fallback route triggered.")
-        # Placeholder for potential future Gemini integration
+    # Step 2: Check for Query Keywords or Query Intent
+    text_lower = text.lower()
+    is_query_keyword_present = any(kw in text_lower for kw in QUERY_KEYWORDS)
+
+    if intent == "query" or is_query_keyword_present:
+        if is_query_keyword_present and intent != "query":
+            print(f"NLP Service: Query keyword detected, overriding initial intent '{intent}'. Fallback triggered.")
+        else:
+            print(f"NLP Service: Intent classified as '{intent}' or query keyword found. Fallback route triggered.")
+        
+        # Return fallback status
         return {
-            "status": "fallback_required", # Use a specific status
-            "message": "Intent requires further processing (e.g., query engine - not implemented).",
+            "status": "fallback_required",
+            "message": "Intent requires further processing (query detected).",
             "original_text": text,
-            "classified_intent": intent
+            "classified_intent": "query" # Standardize to query if fallback is triggered
         }
 
     # Step 3: Entity extraction (for non-fallback intents)
     try:
         entities = ner(text)
         print(f"NLP Service: NER entities: {entities}")
-        amount, currency, item = parse_entities(entities)
+        # --- FIX: Pass the original 'text' as 'full_text' ---
+        amount, currency, item = parse_entities(entities, full_text=text) 
         print(f"NLP Service: Parsed entities: Amount={amount}, Currency={currency}, Item={item}")
     except Exception as e:
         print(f"NLP Service: Error during entity extraction: {e}")
         # Decide if you want to return an error or proceed with partial data
         amount, currency, item = None, None, None # Default to None on error
+        # Optionally, log the traceback for debugging
+        import traceback
+        traceback.print_exc()
 
     # Step 4: Category matching using config.py
     category = "Misc" # Default
-    text_lower = text.lower()
     item_lower = item.lower() if item else ""
 
     # Check intent first for Income/Investment categories