update

.gitignore

@@ -1,5 +1,9 @@
-image/*
-saved/*
-image
-saved
-.DS_Store
+image/hkid.jpg
+saved/HKID.jpg
+.DS_Store
+bank_statement/*
+bank_statement
+data1.txt
+.env
+test.py
+dontTouchMe

README.md

@@ -1,13 +1,24 @@
----
-title: Similarity Check
-emoji: 👁
-colorFrom: purple
-colorTo: red
-sdk: streamlit
-sdk_version: 1.21.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Similarity_check
+
+This application serves as a similarity check of user's name during registration
+
+## Installation
+
+Use the package manager [pip](https://pip.pypa.io/en/stable/) to install packages.
+```
+pip install -r requirements.txt
+```
+
+## Usage
+
+Run web UI
+```
+streamlit run webapp.py
+```
+
+## Uploading Files
+
+jpg/jpeg format of the HKID and bank statement are required to run the application
+
+## Connecion to database
+The code related to connecting to database is done through API request, and it can be checked in transaction_api repository.

check_hkid_validity.py

@@ -0,0 +1,76 @@
+from cnocr import CnOcr
+
+def string_similarity(s1, s2): # Levenshtein distance algorithm
+
+    s1 = s1.replace(' ','')
+    s1 = s1.lower()
+    s2 = s2.replace(' ','')
+    s2 = s2.lower()
+
+    if s1 == s2:
+        return 100.0
+    
+    len1 = len(s1)
+    len2 = len(s2)
+    matrix = [[0] * (len2 + 1) for _ in range(len1 + 1)]
+
+    for i in range(len1 + 1):
+        matrix[i][0] = i
+    
+    for j in range(len2 + 1):
+        matrix[0][j] = j
+    
+    for i in range(1, len1 + 1):
+        for j in range(1, len2 + 1):
+            if s1[i - 1] == s2[j - 1]:
+                cost = 0
+            else:
+                cost = 1
+            matrix[i][j] = min(matrix[i - 1][j] + 1,          # deletion
+                               matrix[i][j - 1] + 1,          # insertion
+                               matrix[i - 1][j - 1] + cost)   # substitution
+    
+    similarity = (1 - matrix[len1][len2] / max(len1, len2)) * 100
+    return round(similarity, 1)
+
+def is_good_subsequence(s1, s2):
+    len_s2 = len(s2)
+    len_s1 = len(s1)
+
+    s1 = s1.lower()
+    s2 = s2.lower()
+    
+    if len_s2 > len_s1 + 10:
+        return False
+    
+    # Initialize variables for counting matches
+    match_count = 0
+    s1_index = 0
+    
+    # Iterate over each character in s2
+    for char in s2:
+        # Search for the character in s1 starting from the last matched index
+        while s1_index < len_s1:
+            if s1[s1_index] == char:
+                match_count += 1
+                s1_index += 1
+                break
+            s1_index += 1
+    
+    # Check if the match count is more than 70% of s2 length
+    return match_count >= (0.5 * len_s2)
+
+def check_hkid(path):
+    ocr = CnOcr(rec_model_name='en_PP-OCRv3')
+    # ocr = CnOcr(rec_model_name='densenet_lite_136-fc')
+    out = ocr.ocr(path)
+
+    for data in out:
+        text = data['text']
+
+        if string_similarity('HONGKONGPERMANENTIDENTITYCARD', text) > 60:
+            return True
+
+    return False
+
+# print(check_hkid('image/hkid.jpg'))

demo.py

@@ -13,6 +13,8 @@ import Visualization_utilities as vis
 # Load a sample picture and learn how to recognize it.
 
 def get_face_encoding(path):
+    print(f'path: {path}')
+    print('hello')
     HKID_cropped = imageSegmentation.auto_cropping(path)
     cv2.imwrite('saved/HKID.jpg', HKID_cropped)
     HKID_image = face_recognition.load_image_file("saved/HKID.jpg")
@@ -63,6 +65,8 @@ def process_frame(frame, process_this_frame, face_locations, faces, face_names,
 
     hkid_face_encoding = get_face_encoding("image")
 
+    print(f'encoding: {hkid_face_encoding}')
+
     known_face_encodings = [
         hkid_face_encoding
     ]

extract_pdf.py

@@ -43,7 +43,7 @@ def get_info_from_bank(img_path, file_name):
     out = ocr.ocr(img_path)
     # Data
     bank_data = {
-        "name_on_bs": "",
+        "nameStatement": "",
         "address": "",
         "bank": check_bank_name(file_name),
         "date": "",
@@ -67,8 +67,8 @@ def get_info_from_bank(img_path, file_name):
             pass
         elif ((positions[0][0] >= 147) and (positions[0][1] >= 265) and (positions[2][0] <= 400) and (positions[2][1] <= 295)):
             if (raw_detected_text != ''): # name
-                bank_data["name_on_bs"] += raw_detected_text
-                bank_data["name_on_bs"] = check_mr(bank_data["name_on_bs"])
+                bank_data["nameStatement"] += raw_detected_text
+                bank_data["nameStatement"] = check_mr(bank_data["nameStatement"])
         elif ((positions[0][0] >= 113) and (positions[0][1] >= 291) and (positions[2][0] <= 500) and (positions[2][1] <= 381)):
             if (raw_detected_text != ''): # position
                 bank_data["address"] += raw_detected_text
@@ -100,13 +100,13 @@ def get_info_from_bank(img_path, file_name):
 
 
     # print('------------From bank statement------------')
-    # print(f'Name: {bank_data["name_on_bs"]}')
+    # print(f'Name: {bank_data["nameStatement"]}')
     # print(f'Address: {bank_data["address"]}')
     # print(f'Bank: {bank_data["bank"]}')
     # print(f'Date: {bank_data["date"]}')
     # print(f'Asset: {asset_equa} = {bank_data["asset"]}')
     # print(f'Liabilities: {bank_data["liabilities"]}')
-    # post_data(bank_data["bank"], bank_data["name_on_bs"], bank_data["address"], bank_data["asset"], bank_data["liabilities"], bank_data["date"])
+    # post_data(bank_data["bank"], bank_data["nameStatement"], bank_data["address"], bank_data["asset"], bank_data["liabilities"], bank_data["date"])
     return bank_data
 
 ########## Posting data through API ############

extraction_data.py

@@ -2,6 +2,9 @@
 from cnocr import CnOcr
 from pdfquery import PDFQuery
 import openai
+import json
+from dotenv import load_dotenv
+import os
 
 def validate(text):
     invalid_list = [' ',',']
@@ -39,58 +42,40 @@ def check_mr(text):
     else:
         return text
     
-def get_info_from_bank(img_path, pdf_path):
+def get_info_from_bank(img_path):
     # Running the model
     ocr = CnOcr(rec_model_name='densenet_lite_136-gru')
     out = ocr.ocr(img_path)
 
-    # Data
-    bank_data = {
-        "name_on_bs": "",
-        "address": "",
-        "bank": "",
-        "date": "",
-        "asset": 0.0,
-        "liabilities": ""
-    }
-    
-    # {
-    #     "Customer Name": "MR CHIU CHUNG YIN",
-    #     "Address": "FLAT 13,8/F,OILOK HOUSE, YAU OI ESTATE, TUEN MUN NT",
-    #     "Bank Name": "HSBC",
-    #     "Statement Issue Date": "10 January 2023",
-    #     "Total Asset": "7,265.80",
-    #     "Total Liability": "7,265.80"
-    # }
-
-    openai.api_key = "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"
+    load_dotenv()
+    openai.api_key = os.environ.get("data-extraction-api")
     invalid_list = [' ',',']
     data_set_1 = []
 
-    pdf = PDFQuery(pdf_path)
-    pdf.load(0)
-    text_elements = pdf.pq('LTTextLineHorizontal').text()
-    text_elements = text_elements.replace("cid:", "")
-
     for item in out:
         if item['text'] not in invalid_list:
             data_set_1.append(item['text'])
 
     completion = openai.ChatCompletion.create(
         model = "gpt-3.5-turbo",
-        temperature = 0.2,
+        temperature = 0,
         messages = [
-            {"role": "system", "content": "You are an AI assistant for extracting data from bank statements. Uppercase and lowercase letters are the same. List results in a dictionary format."},
-            {"role": "user", "content": f"Extract data from the following 2 sets of text: {data_set_1} and {text_elements}. (1.) Data that locate in the front part of the text: customer full name, address in Hong Kong (including flat, floor, court/estate, region in Hong Kong), bank name, bank statement issue date (verly likely to be within 1-2 years), (2.) Data that mainly locate in the other part of the text: total asset (including investments and deposits) and total liability (often contains DR and includes credit card but might be zero) of the current month."},
-            # {"role": "assistant", "content": "Q: How do you make 7 even? A: Take away the s."},
-            # {"role": "user", "content": "Write one related to programmers."}
+            {"role": "system", "content": "You are an AI assistant for extracting data with following names(bank, nameStatement, address, totalAsset (only HKD and represent as one number), totalLiability, statementDate) from bank statements. Uppercase and lowercase letters are the same. Store the results in dictionary format"},
+            {"role": "user", "content": f"Extract data from the following 2 sets of text: {data_set_1}. (1.) Data that locate in the front part of the text: customer full name (it should be a Chinese name in English spelling and two to three words), address in Hong Kong (including flat, floor, court/estate, region in Hong Kong), bank name, bank statement issue date (verly likely to be within 1-2 years), (2.) Data that mainly locate in the other part of the text: total asset (including investments and deposits) and total liability (often contains DR and includes credit card but might be zero) of the current month."},
         ]
     )
-    bs_data = completion['choices'][0]['message']['content']
-    print(bs_data)
-    return bs_data
 
-# get_info_from_bank('hangseng_page-0001.jpg','hangseng.pdf')
-# get_info_from_bank('hsbc_one_account_page-0001.jpg','hsbc_one_account.pdf')
-# get_info_from_bank('boch_consolidated.jpg','boch_consolidated.pdf')
-get_info_from_bank('hsbc_one_account_page-10001.jpg','hsbc_one_account_page-10001.pdf')
+    # bs_data = completion['choices'][0]['message']
+    data = completion['choices'][0]['message']['content']
+    bs_data = json.loads(data)
+    # for data_item in bs_data:
+    #     if 'name' in data_item:
+    #         bs_data[''] = check_mr
+    # print(bs_data)
+    # new_name = check_mr(bs_data["nameStatement"])
+    bs_data["nameStatement"] = check_mr(bs_data["nameStatement"])
+    # bs_data["totalAsset"] = bs_data["totalAsset"].replace("HKD","")
+    # bs_data["totalLiability"] = bs_data["totalLiability"].replace("HKD","")
+    # bs_data["totalLiability"] = bs_data["totalLiability"].replace("DR","")
+    # print(bs_data)
+    return bs_data

imageSegmentation.py

@@ -22,34 +22,42 @@ detector = vision.FaceDetector.create_from_options(options)
 def crop(
     image,
     detection_result
-) -> np.ndarray :
-  annotated_image = image.copy()
-  height, width, _ = image.shape
+):
+  # annotated_image = image.copy()
+  # height, width, _ = image.shape
 
+  print(image.shape)
   # Here assume we only detect one face
   for detection in detection_result.detections:
     # Crop detected face
     bbox = detection.bounding_box
-    cropped_img = image[bbox.origin_y - 90: bbox.origin_y + bbox.height + 30, bbox.origin_x - 80:bbox.origin_x + bbox.width + 35]
-  
-  return cropped_img
+    print(f'bbox {bbox}')
+    cropped_img = image[bbox.origin_y: bbox.origin_y + bbox.height, bbox.origin_x:bbox.origin_x + bbox.width]
+    # cropped_img = image[bbox.origin_y - 90: bbox.origin_y + bbox.height + 30, bbox.origin_x - 80:bbox.origin_x + bbox.width + 35]
+    print(f'crop: {cropped_img}')
+    return cropped_img
 
 def auto_cropping(dir):
 
   files = os.listdir(dir) # list of files in directory
 
+  print(files)
   for file in files:
-
+      if file == "DONT_DELETE.txt":
+        continue
       file_dir = Path(dir + "/" + file)
       abs_path = file_dir.resolve()
 
       img = mp.Image.create_from_file(str(abs_path))
 
       detection_result = detector.detect(img)        
-      save_path = 'saved'
 
       image_copy = np.copy(img.numpy_view())
       annotated_image = crop(image_copy, detection_result)
+
+      print('hello')
+      print(annotated_image)
+
       rgb_annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
   
   return rgb_annotated_image
@@ -57,4 +65,6 @@ def auto_cropping(dir):
 # auto_cropping("image") # <----------- !!!!change address here!!!! ------------------> #
 
 # The current problem (6/2/2023) is that the model may recognize some cartoon face as human face,
-# my idea is to use another model to classify if the cropped image is real human face 
+# my idea is to use another model to classify if the cropped image is real human face 
+
+# print(auto_cropping("image"))

model0.py

@@ -0,0 +1,54 @@
+from cnocr import CnOcr
+import openai
+from dotenv import load_dotenv
+import os
+import json
+
+def model0(path):
+    ocr = CnOcr(rec_model_name='en_PP-OCRv3')
+    out = ocr.ocr(path)
+
+    print(out)
+
+    load_dotenv()
+    openai.api_key = os.environ.get("data-extraction-api")
+
+    invalid_list = [' ',',']
+    data_set_1 = []
+    for item in out:
+        if item['text'] not in invalid_list:
+            data_set_1.append(item['text'])
+
+    completion = openai.ChatCompletion.create(
+        model = "gpt-3.5-turbo",
+        temperature = 0,
+        messages = [
+            {"role": "system", "content": "You are an AI assistant for extracting data from HKID card with following information \
+                (name, HKID number, date of issue) from HKID card. Uppercase and lowercase letters are the same. Store the results in \
+                dictionary format"},
+            {"role": "user", "content": f"Extract data from the following set of text: {data_set_1}. \
+                You have three types of data to extract. \
+                1. id card holder full name (it noramlly is a chinese name, including surname and family \
+                name in English spelling, and it may be separate in different fields in the data set for surname and family name \
+                sometimes) \
+                2. issue date (should be a date with month and day, e.g. 19-97 is the required format, but 26-11-18 is not \
+                because date of issue of have 5 characters) Only choose valid format!!! \
+                3. HKID number (The standard format of HKID number is @123456(#) e.g. A123456(7) is a valid HKID number. \
+                (a) @ represents any one or two capital letters of the alphabet. \
+                (b) # is the check digit which has 11 possible values from 0 to 9 and A.) \
+                Remember to include the check digit with () \
+                Only reply a dictionary. No need to add other words or explanation. Use double quote for dictionary."},
+        ]
+    )
+
+    data = completion['choices'][0]['message']['content']
+
+    print(data)
+
+    id_data = json.loads(data)
+    
+    print(id_data)
+    return
+    # return [name, valid_hkid, hkid, issuedate]
+
+model0('dontTouchMe/IMG_4499.jpg')

request_json/sbt_request_generator.py

@@ -51,9 +51,8 @@ def generate_request(data):
 
 
 def split_data(data):
-    request_id = "request1234"
-    # token_id = generate_token_id(501)
-    token_id = "12344321"
+    # request_id = "request1234"
+    # token_id = "12344321"
 
     f = open('data1.txt', 'r')
     with open('data1.txt') as f:
@@ -62,47 +61,26 @@ def split_data(data):
 
     if "avg_score" not in data.keys():
         data["avg_score"] = "0"
+    elif "similarity_score" not in data.keys():
+        data["similarity_score"] = "0"
 
-    legal_doc_data = {
+    sbt_data = {
         "endpoint": "SBT",
-        "apiType": "store_legalDoc_verif",
-        "requestId": "request_id_id",
+        "apiType": "store_img_verif",
+        "requestId": "request_id_1234",
         "date": get_today_date(), # a string
-        "tokenID": token_id,# a string
         "docType": "HKID",
         "nameDoc": data["name_on_id"], # a string; lower case with space separate; e.g. san chi nan
         "docID": data["hkid"], # a string; with bracket (); e.g. G908833(1)
         "docValidity": data["validity"], # a string; "True" or "False"
         "dateOfIssue": data["issue_date"], # a string; month-year; e.g. 07-81
-        "matchingScore": str(data["avg_score"]) # a string; e.g. "0.957"
+        "matchingScore": str(data["similarity_score"]), # a string; e.g. "0.957"
+        "bank":str(data["bank"]), # 
+        "nameStatement":str(data["nameStatement"]), # 
+        "address":str(data["address"]), # 
+        "asset": str(data["totalAsset"]), # a string containing only numbers
+        "liability": str(data["totalLiability"]), # a string containing only numbers
+        "statementDate": str(data["statementDate"]), # a string
     }
 
-    bank_statement_data = {
-        "endpoint": "SBT",
-        "apiType": "store_statement_verif",
-        "requestId": "request_id_bs",
-        "date": get_today_date(), # a string
-        "tokenID": token_id, # a string
-        "bank":data["bank"], # 
-        "nameStatement":data["name_on_bs"], # 
-        "address":data["address"], # 
-        "asset": str(data["asset"]), # a string containing only numbers
-        "liability": data["liabilities"], # a string containing only numbers
-        "statementDate": data["date"], # a string
-    }
-
-    generate_request(legal_doc_data)
-    generate_request(bank_statement_data)
-
-
-    # demo structure of the data
-    # {"password2": "chingfuilau", "username": "Allenlau1111", "password1": "Allen02118173", "date": "2023-03-03 00:00:00",
-    #         "credentialId": "testing123","requestID": "test_statements", 
-    #         "userId": "7893456", 
-    #         "endpoint": "SBT",  
-    #         "apiType": "metadata", 
-    #         'tokenId':"500", 
-    #         "ipfsLink1": ".",
-    #         "ipfsLink2": "..",
-    #         "ipfsLink3": "...",
-    #         "membershipStatus": "1"}
+    generate_request(sbt_data)

sbt_request.txt

@@ -1,22 +1,34 @@
-endpoint: b'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'
+endpoint: b'NbBxyA/6/7XAzpk8HYqkN6D7k7yD8/c2e53wyblTHcVg0D1EgYnZodoTuvDpEMCkouCyk80xAhIynxYa0lsRfLsI4438OLHuad/gvZt29v1cYqxVYqs+udA/Hd2zbsr4zYKORBW+LJhj9u+8zefNEGCstMK215eeht0ZpxLlgQMQdApSkPsPJnYO3VLWmBiV7txwZGhkqtOvE+cfsUEsulSpdhpRvcj5IGMNrF/14gLKHWjxTKj45At2w1ToBzIwfPDPrF9QA4mw6kUs25UFoSOFOo5/OMbW2iSDpYFE4UHj74Z75hzYJWdWlDTKLZUDWxwOF19CGSbKNxZt5Fsr3W2k17W5D5CG0skkEl4lhCyepoyGNWJZCNJ65PRFcUYyT2JqWRQM7htIKgbs1lZlHl85go3Z9ULNf3CgJeIZGwo61a2jwDPB7lr9LmpVk6tz5HLUNaKpyGZaebiB2J/Fk7MyrYd/r5AB8qLgZXgp61YRFv7bFpZAKIRnMTZP1FH3ZLR8CE9sISWxsx5WipGmSI3FmTASg9Xf7PBdyx3+GChiP7bxmBCDhQ/BqOn45ULOIyHURQ+im4O9B8MEx0XlWhilK0Fz0LtXnT0PGG2GdTE/B9kaEd6g6u8oaT4+3WeyZTN3Jtae0yFQ9iKnwPoR45LtXejfvQgaqAjauUnFDBs='
 
-apiType: b'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'
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-liability: b'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'
+dateOfIssue: b'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'
 
-statementDate: b'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'
+matchingScore: b'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'
+
+bank: b'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'
+
+nameStatement: b'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'
+
+address: b'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'
+
+asset: b'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'
+
+liability: b'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'
+
+statementDate: b'F0hBPc79PlZ3TE+jEY42dYDYQ3dzb5JrpLYmJaFgZS7Bumb7NjuyRKPPwLdILtYHfoSK2ZBQXLmplLY9J2k5UzMFwgQj/Oc/9tOJA6uHqCI0FjAx62Cu3PnECokXvKA1Cm1rNsY1IDt212eC5ghiiMF3pdKBW7jElIBsuDjJavtCnTF52QyXs7eVhAA0ZxhI4t0aP9TKEZINiGteczj36Of+UDgej3QR9/uK7Ds+FJFEQhMKWzNUM22AuXGGvhceAKiK2heOez8znCEtp/vvTRvYicYmRbNweQGjtrhCEVMeQ4UTGlOEqploOHuZ0ykU0TIUmjIPzPLUZsaKj8a4HK+ecEIntUWwCDtD76DHcbJ+ZzLLFYSfiIPTBTS88Pul2HqgnqilRufui1A6GEcBHluHQpnPrFth/FMB4Xuia8AvIkA5TfuKAneoq2Lwo3kD4/OrixfboF1d0NKm/tGmKhiCIWmhW6D1MtZv86rlVzsFrSS5UxjIQyHTuIYprsNerF7C0Uqy6llmdKkti2M9IJKc9T8pqReKevYmWxkkaQ9xd/orqpG9bPHYn3bcgj60FQ5x4zaytfF2rdHr6sTWCTfTb/Iz+yWBzmXS4kdV/V5lTPuGfp4uFyPI72nhPgGtQ6OCLuWkEmgjBK6VcZALY0TefZ8fByfsVLRNKJgnvVQ='
 

similarity_check.py

@@ -2,10 +2,15 @@ from model1 import model1
 from model2 import model2
 import checkTool as ct
 import extract_pdf as pf
+import extraction_data as ed
 
 # get info from hkid card
 
 def string_similarity(s1, s2): # Levenshtein distance algorithm
+    s1 = s1.replace(' ', '')
+    s1 = s1.lower()
+    s2 = s2.replace(' ', '')
+    s2 = s2.lower()
     if s1 == s2:
         return 100.0
     
@@ -32,7 +37,7 @@ def string_similarity(s1, s2): # Levenshtein distance algorithm
     similarity = (1 - matrix[len1][len2] / max(len1, len2)) * 100
     return round(similarity, 1)
 
-def get_data(img1_path, img2_path, file_name):
+def get_data(img1_path, img2_path):
 
     # img_fp = 'IMG_4495.jpg'
 
@@ -58,8 +63,9 @@ def get_data(img1_path, img2_path, file_name):
     # name = name.replace(' ', '')
     # name = name.lower()
 
-    data = pf.get_info_from_bank(img2_path, file_name)
-    name = data["name_on_bs"]
+    # data = pf.get_info_from_bank(img2_path, file_name)
+    data = ed.get_info_from_bank(img2_path)
+    name = data["nameStatement"]
 
 
     ############# Similarity check ##############

test.py

@@ -1,3 +1,4 @@
 import streamlit
+import mediapipe
 
-print(streamlit.__version__)
+print(mediapipe.__version__)

test_ocr.py

@@ -0,0 +1,4 @@
+import easyocr
+reader = easyocr.Reader(['en'])
+result = reader.readtext('hangseng_page-0001.jpg', detail = 0)
+print(result)

webapp.py

@@ -11,12 +11,13 @@ import streamlit as st
 import requests
 import json
 import request_json.sbt_request_generator as sbt
+import pathlib
+import os
+import check_hkid_validity as chv
 
-global data
-data = {}
-    
 
 def main():
+
     # st.title("SBT Web Application")
     # today's date = get_today_date
 
@@ -30,22 +31,37 @@ def main():
         """
     st.markdown(html_temp, unsafe_allow_html=True)
     
+    if 'hkid_image_validity' not in st.session_state:
+        st.session_state.hkid_image_validity = False
+
+    if 'data' not in st.session_state:
+        st.session_state['data'] = {}
+
     st.header("I. Similarity Check")
-    image_file = st.file_uploader("Upload Image", type=['jpg', 'png', 'jpeg'], accept_multiple_files=True)
+    image_file = st.file_uploader("Upload Image", type=['jpg', 'png', 'jpeg', 'pdf'], accept_multiple_files=True)
     if len(image_file) == 1:
-        # print(image_file[0].name)
         image1 = Image.open(image_file[0])
         st.text("HKID card")
         st.image(image1)
+        image1.save('image/hkid.jpg', 'JPEG')
+        if chv.check_hkid('image/hkid.jpg'):
+            st.text("Valid HKID card.")
+            st.session_state.hkid_image_validity = True
+        else:
+            st.text("Invalid HKID card. Please upload again!")
+            st.session_state.hkid_image_validity = False
     elif len(image_file) == 2:
         image1 = Image.open(image_file[0])
         st.text("HKID card")
         st.image(image1)
         image2 = Image.open(image_file[1])
-        file_name = image_file[1].name
+        # image2 = image_file[1]
+        # image2.save('image/hkid.jpg', 'JPEG')
+        # file_name = image_file[1].name
         st.text("Bank statement")
         st.image(image2)
-       
+
+    print(f"the id is: {st.session_state.hkid_image_validity}")
     # if image_file2 is not None:
     #     image2 = Image.open(image_file)
     #     st.text("Bank statement")
@@ -60,15 +76,20 @@ def main():
     if st.button("Recognise"):
         with st.spinner('Wait for it...'):
             # global data
-            data = sc.get_data(image1, image2, file_name)
-  
-            with open('data1.txt', 'w') as f:
-                f.write(json.dumps(data))
+            data = sc.get_data(image1, image2)
+            # data = ed.get_info_from_bank('hsbc_one_account.pdf')
+            # with open('data1.txt', 'w') as f:
+            #     f.write(json.dumps(data))
             # data.update(sc.get_data(image1, image2, file_name))
-            print(f'data inside {data}')
+            # print(f'data inside {data}')
             # sbt.split_data(data)
+        if 'data' in st.session_state:
+            st.session_state['data'] = data
         st.success('Done!')
-        score = data["similarity_score"]
+        # if "similarity_score" not in data.keys():
+        #     data["similarity_score"] = "0"
+        score = int(st.session_state['data']['similarity_score'])
+        # score = int(data["similarity_score"])
         #print(score)
         st.text(f'score: {score}')
         if (score>85):
@@ -76,6 +97,8 @@ def main():
         else:
             st.text(f'unmatched')
         
+
+        data = st.session_state['data']
         st.header("IIa. HKID Data Extraction")
         st.text(f'Name: {data["name_on_id"]}') # name is without space
         st.text(f'HKID: {data["hkid"]} and validity: {data["validity"]}')
@@ -83,16 +106,21 @@ def main():
     
         st.header("IIb. Bank Statement Data Extraction")
         # st.write('------------From bank statement------------')
-        st.text(f'Name: {data["name_on_bs"]}')
+        st.text(f'Name: {data["nameStatement"]}')
         st.text(f'Address: {data["address"]}')
         st.text(f'Bank: {data["bank"]}')
-        st.text(f'Date: {data["date"]}')
-        st.text(f'Asset: {data["asset"]} hkd')
-        st.text(f'Liabilities: {data["liabilities"]} hkd')
+        st.text(f'Date: {data["statementDate"]}')
+        st.text(f'Asset: {data["totalAsset"]} hkd')
+        st.text(f'Liabilities: {data["totalLiability"]} hkd')
         # result_img= detect_faces(our_image)
         # st.image(result_img)
     # print(f'data outside 1 {data}')
+
+    if 'data' in st.session_state:
+        tempout = st.session_state['data']
+        print(f'hello: {tempout}')
     
+
     st.header("II. Facial Recognition")
     run = st.checkbox('Run')
 
@@ -127,18 +155,27 @@ def main():
         print(score)
         if len(score) > 20:
             avg_score =  sum(score) / len(score)
-            st.write(f'{avg_score}')
-            with open('data1.txt', 'w') as f:
-                data_raw = f.read()
-                data = json.loads(data_raw)
-                data['avg_score'] = str(avg_score)
-                f.write(json.dumps(data))
+            st.write(avg_score)
+            # st.write(f'{demo.convert_distance_to_percentage(avg_score, 0.45)}')
+            camera.release()
+            run = not run
+            st.session_state['data']['avg_score'] = str(avg_score)
+            # with open('data1.txt', 'r') as f:
+            #     if f is not None:
+            #         data_raw = f.read()
+            #         data = json.loads(data_raw)
+            #         data['avg_score'] = str(avg_score)
+            #     else:
+            #         data = {}
+
+            
+            # with open('data1.txt', 'w') as f:
+            #         f.write(json.dumps(data))
                 
 
         # update_text(f'{demo.convert_distance_to_percentage(score, 0.45)}')
     else:
         st.write('Stopped')
-
     
     # print(f'the data is {data}')
 
@@ -149,7 +186,7 @@ def main():
 
     # st.header("IIIb. Bank Statement Data Extraction")
     # # st.write('------------From bank statement------------')
-    # st.text(f'Name: {data["name_on_bs"]}')
+    # st.text(f'Name: {data["nameStatement"]}')
     # st.text(f'Address: {data["address"]}')
     # st.text(f'Bank: {data["bank"]}')
     # st.text(f'Date: {data["date"]}')
@@ -160,7 +197,7 @@ def main():
     if st.button("Confirm"):
         # print(f'data outside 3 {data}')
         with st.spinner('Sending data...'):
-            sbt.split_data(data)
+            sbt.split_data(st.session_state['data'])
         st.success('Done!')
 
 if __name__ == '__main__':