Update app.py

app.py

@@ -128,26 +128,36 @@ Extract expense details from the OCR text below:
 {full_text}
 \"\"\"
 
+### STRICT INFORMATION RULES:
+- Do NOT create or guess any information that does not exist in the extracted text.
+- If any field (date, time, payment_type, total_amount) is not clearly present in the text, set its value to "unknown".
+- Only infer the label category (Restaurant, Store, etc.) based on business name and item types.
+
 ### Labeling Rules:
 1. Detect the business/merchant name from the text (e.g., KFC, Starbucks, Ying Thai Kitchen).
 2. If items are food or restaurant-related → label must be: "<Business Name> Restaurant".
 3. If it's a store/retail → "<Business Name> Store".
 4. If unclear, infer the closest meaningful category.
+5. If business name is not found → label = "unknown".
 
 ### Notes Format:
 Always generate notes EXACTLY in this format:
 "Spent <total_amount> on <label> on <date>."
 
+- If <total_amount> is unknown → use "unknown"
+- If <label> is unknown → use "unknown"
+- If <date> is unknown → use "unknown"
+
 ### Required Output:
 Return structured JSON (via schema) with:
-- total_amount
-- label
-- date
-- time
-- payment_type
-- notes
-
-Fill **every** field with the best possible inference.
+- total_amount     (if missing → "unknown")
+- label            (if missing → "unknown")
+- date             (if missing → "unknown")
+- time             (if missing → "unknown")
+- payment_type     (if missing → "unknown")
+- notes            (follow the provided format)
+
+Fill **every** field ONLY with information found in the extracted text or "unknown".
 """
 
     try: