Update main.py

main.py

@@ -67,6 +67,17 @@ def receipt_radar_prompt(raw_text:str)->str:
         travel_type(bus,train,airplane,taxi,bike,rickshaw classify in these categories only strictly),travel_company_name , departure_destination , arrival_destination , arrival_city(if you are not able to find the arrival city add the arrival destination into this field strictly. ), departure_date,arrival_date .If the arrival and departure dates are the same from receipt text given to you analyse it properly to check that, then only use the same date in both the fields .if you don't find any field mark it as null.
     """
     hotel_data_points = """ hotel_type(hotel_stay , dine_in , dine_in + stay(use both keyword strictly)), hotel_brand_name , hotel_location , hotel_checkin_date , hotel_checkout_date. if you don't find any field mark it as null """
+    coupon_data_points = """
+        {
+      "brand_name": "Extract the brand_name from where the coupon is sent",
+      "validity": {
+        "start_date": "Date the coupon is valid from, if provided.other wise mark it as null",
+        "expiration_date": "Date the coupon expires. Leave blank if not mentioned."
+      },
+      "coupon_code": "Extract the unique code found in the email. If unavailable, leave it blank.",
+      "description": "Provide the discount details (amount/percentage), minimum purchase, eligible products, retailer, any terms or restrictions, usage limit (single-use/multi-use), and where the coupon applies (in-store/online)."
+      }
+    """
     system_prompt = f"""Extract information from the following receipt OCR text and return a JSON object with these exact keys: brand, total_cost, location, purchase_category, brand_category, Date, currency, filename, payment_method, metadata.
     Rules:
     1. For total_cost, use the highest monetary value in the text.
@@ -75,11 +86,12 @@ def receipt_radar_prompt(raw_text:str)->str:
     4. metadata: For insurance receipts extract the data points given in the JSON and return the JSON with structure: \n """ + insurance_response_structure + """
     5.metadata : For travel receipts(flight ,bus,train) extract these data points as a JSON object exactly""" + travel_response_structure + """
     6. metadata : For hotel receipts extract these data points as a JSON object exactly""" + hotel_data_points + f"""
+    7. metadata : For coupon receipts extract these data points as a JSON object exactly""" + coupon_data_points + f"""
     For non-insurance and non-travel , non-hotel receipts, return metadata as null.
-    4. Use currency codes (e.g., USD, EUR) instead of symbols.
-    5. Generate filename as 'PURCHASE_TYPE_BRAND_DATE' (e.g., 'clothing_gucci_20230715').
-    6. If a value is not found, return null.
-    7. If all values are null, return null.
+    8. Use currency codes (e.g., USD, EUR) instead of symbols.
+    9. Generate filename as 'PURCHASE_TYPE_BRAND_DATE' (e.g., 'clothing_gucci_20230715').
+    10. If a value is not found, return null.
+    11. If all values are null, return null.
     Ensure the strictly that output is a valid JSON object containing strictly the above keys, without any explanations.
     Here's the OCR text below analyse it and convert into json using keys provided in first line and using the rules provided in rules section:
     Generate a JSON response in the following format without using the ```json block. Ensure the output is properly formatted as plain text JSON.