Expense keyword updates

main.py

@@ -152,7 +152,11 @@ expense_keywords = [
     "paid", "bought", "purchased", "ordered", "spent", "payment",
     "recharged", "booked", "transaction", "debit", "renewed",
     "credit card", "cash", "amount", "transfer", "EMI", "wallet",
-    "petrol", "bill", "invoice"
+    "petrol", "bill", "invoice", "kharida", "kharcha", "kharch", "bill", "paisa", "khareed", "order", "le liya", "diya", "khud diya", "khud kharida",
+    "expense", "cost", "buy", "buying", "purchase", "purchased", "paid for", "paid to", "paid via", "paid using",
+    "expense", "expenses", "costs", "costing", "bills", "bought from", "ordered from", "paid at",
+    "paid online", "paid cash", "paid card", "paid wallet", "paid app", "paid through", "paid via",
+    "khariden", "kharidi"
 ]
 
 class TextInput(BaseModel):
@@ -716,6 +720,15 @@ async def analyze(input: TextInput):
 
     best_label = label_map.get(best_label, best_label)
 
+    if (
+        best_label == "task"
+        and (any(word in text.lower() for word in expense_keywords) or amounts)
+    ):
+        best_label = "expense"
+
+    if best_label == "purchase":
+        best_label = "expense"
+
     if "reported" in text or "announced" in text or "collapsed" in text:
         if best_label in ["task", "reminder", "event"]:
             best_label = "news"
@@ -736,12 +749,13 @@ async def analyze(input: TextInput):
     mood = estimate_mood(text)
     tags = generate_tags(best_label, text)
     language_detected = detect_language(text)
-    sentiment_score = get_sentiment_score(text)    
+    # sentiment_score = get_sentiment_score(text)    
     entities = await asyncio.to_thread(extract_entities, text)
     people = entities["people"] # Extracted people entities
     intent = infer_intent(best_label, text)
     urgency_score = get_urgency_score(text, parsed_dates)
     detected_stores = detect_store_category(text)
+    
     expense_category = ""
     if best_label == "expense" or best_label ==  "purchase":
         expense_category = predict_expense_category(text, detected_stores)
@@ -778,7 +792,7 @@ async def analyze(input: TextInput):
         "people": people,
         "mood": mood,
         "language": language_detected,
-        "sentiment_score": sentiment_score,
+        "sentiment_score": "",
         "tags": tags,
         "action_required": action_required,
         "entities": entities,
@@ -803,5 +817,4 @@ async def analyze(input: TextInput):
     result.pop("raw_json", None)
 
     # Return the result as JSON response
-    return ORJSONResponse(content=result)
-
+    return ORJSONResponse(content=result)