Update app.py

app.py

@@ -101,10 +101,10 @@ business_card_sheet_headers = ['이름', '회사명', '소속 및 직위', '주
 sms_sheet_headers = ['날짜', '계좌', '입금', '출금', '잔액', '문자']
 contact_sheet_headers = ['이름',	'전화번호1',	'전화번호2',	'전화번호3',	'이메일주소1',	'이메일주소2',	'이메일주소3',	'그룹']
 
-receipts_ss = 0
-business_cards_ss = 344204272
-contacts_ss = 1943060316
-sms_ss = 799147214
+receipts_ss = 1395967745
+business_cards_ss = 1733555840
+contacts_ss = 1729750548
+sms_ss = 1891574758
 
 kst = pytz.timezone('Asia/Seoul')
 
@@ -170,37 +170,7 @@ async def request_gpt4o_completion(image_id: str, user_credit: int):
         # Step 1: Download the image file from Google Drive
         file_content, mime_type = await download_file_from_drive(image_id)
 
-        # Step 2: Convert the image to base64
-        base64_image = base64.b64encode(file_content).decode('utf-8')
-
-
-
-        # client = vision.ImageAnnotatorClient()
-
-    
-        # image = vision.Image(content=file_content)
-    
-        # response = client.text_detection(image=image)
-        # texts = response.text_annotations
-        # print("Texts:")
-    
-        # for text in texts:
-        #     print(f'\n"{text.description}"')
-    
-        #     vertices = [
-        #         f"({vertex.x},{vertex.y})" for vertex in text.bounding_poly.vertices
-        #     ]
-    
-        #     print("bounds: {}".format(",".join(vertices)))
-    
-        # if response.error.message:
-        #     raise Exception(
-        #         "{}\nFor more info on error messages, check: "
-        #         "https://cloud.google.com/apis/design/errors".format(response.error.message)
-        #     )
-
         # Step 3: Get text data from the image using Google Cloud Vision
-        logging.info("Initiating text detection with Google Cloud Vision")
         client = vision.ImageAnnotatorClient()
         image = vision.Image(content=file_content)
         response = client.text_detection(image=image)
@@ -215,12 +185,10 @@ async def request_gpt4o_completion(image_id: str, user_credit: int):
 
         # Extract all detected text
         extracted_text = texts[0].description if texts else ""
-        logging.info(f"Text extracted from image. Length: {len(extracted_text)} characters")
-
 
         # Step 4: Prepare the prompt for Gemini
         prompt = f'''
-        Image content: {extracted_text[:500]}...  # Truncated for logging purposes
+        Image content: {extracted_text}
 
         Please analyze the above text extracted from an image and perform the following tasks:
         1. Identify if the image contains receipts or business cards.
@@ -235,19 +203,17 @@ async def request_gpt4o_completion(image_id: str, user_credit: int):
         logging.info("Prompt prepared for Gemini model: " + prompt)
 
         # Generate content using the Gemini model
-        logging.info("Sending request to Gemini model")
         response = model.generate_content(
             contents=[
                 prompt
             ]
         )
-        logging.info("Received response from Gemini model")
 
         # Check if the response has text content
         if response.text:
             logging.info("Parsing Gemini model response")
             json_response = json.loads(response.text)
-            logging.info(f"JSON response parsed. Keys: {json_response.keys()}")
+            logging.info(f"JSON response parsed: {response.text}")
             return json.dumps(json_response)
         else:
             logging.error("Gemini model did not return a text response")