# **BC-EnDeCoder**
BC-EnDeCoder is a Python library that provides a secure way to encode and decode data for use with Large Language Models (LLM). The library allows you to protect sensitive information by passing a fake dummy value, which is then encoded and decoded to and from its original form after receiving a response from the LLM.
## Features
- **Secure Encoding and Decoding:** Protect your sensitive data by encoding it with a fake dummy value and decoding it back to the original form after interacting with an LLM.
- **Easy Integration:** Simple and easy-to-use functions for encoding and decoding data, making it convenient to integrate into your projects.
- **Customizable Encoding Parameters:** Fine-tune the encoding process with customizable parameters to suit your specific use case.
## Installation
To install BC-EnDeCoder, you can use the following pip command:
```bash
pip install bc-en-de-coder
```
## How it Works
BC-EnDeCoder facilitates a secure interaction with LLMs through a three-step process:
- **Encoding with a Dummy Value:** Sensitive data is encoded using a fake value, providing an added layer of security during transmission to an LLM.
- **Interaction with LLM:** The encoded data is then passed to the LLM for analysis or processing.
- **Decoding the Response:** Upon receiving the LLM's response, BC-EnDeCoder decodes it, revealing the original information without compromising its security.
### Encoding and Decoding values in string
Encode and decode values in string using the `encode_str()` and `decode_str()` methods.
```python
from bc_endecoder.replace import BaseCoder
bc = BaseCoder()
text = '''
This is a dummy text with value 200,100,150,250.
We need to protect these values.
'''
encoded_text,encodings = bc.encode_str(text) #encode_str takes 1 paramter which is the text and returns the encoded text and encoding
print("Encoded Text : \n",encoded_text)
## encoded_text can be passed to GPT and after getting back the response it will be decoded using decode_str() method
original_text = bc.decode_str(encoded_text,encodings) #decode_str takes 2 parameters which are the encoded_text and encoding and returns the original text
print("\nOriginal Text : \n",original_text)
```
Output
```python
Encoded Text :
This is a dummy text with value 4858416350,7636580946,0858875814,8301435677.
We need to protect these values.
Original Text :
This is a dummy text with value 200,100,150,250.
We need to protect these values.
```
### Encoding and Decoding values in Dataframe
Encode and decode values in Dataframe using the `encode_df()` and `decode_df()` methods.
```python
from bc_endecoder.replace import BaseCoder
import pandas as pd
import numpy as np
bc = BaseCoder()
data = {
'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],
'Age': [25, 30, 22, 35, 28],
'City': ['New York', 'San Francisco', 'Los Angeles', 'Chicago', 'Miami'],
'Salary': [60000, 80000, 55000, 90000, 70000]
}
df = pd.DataFrame(data)
encoded_df,encoding = bc.encode_df(df) #encode_df takes 1 paramter which is Dataframe and returns the encoded dataframe and encodings
print("Encoded_df : \n",encoded_df)
## encoded_df can be passed to GPT and after getting back the response it will be decoded using decode_df() method
original_df = bc.decode_df(encoded_df,encoding) #decode_str takes 2 parameters which are the encoded_df and encoding and returns the original df
print("\nOriginal_df : \n",original_df)
```
Output
```python
Encoded_df :
Name Age City Salary
0 Alice 8623770624 New York 0197705789
1 Bob 5223314994 San Francisco 9743912420
2 Charlie 1795473060 Los Angeles 8982145407
3 David 6439787181 Chicago 6618233087
4 Eva 4699492207 Miami 6680877680
Original_df :
Name Age City Salary
0 Alice 25 New York 60000
1 Bob 30 San Francisco 80000
2 Charlie 22 Los Angeles 55000
3 David 35 Chicago 90000
4 Eva 28 Miami 70000
```
### Encoding and Decoding values with a ratio in Dataframe, Json or String
Encode and decode values with a ratio in Dataframe, Json or String using the `encode_df_ratio()` and `decode_df_ratio()` methods.
```python
from bc_endecoder.replace import BaseCoder
import pandas as pd
import numpy as np
bc = BaseCoder()
json_data = {
"key1": 10,
"key2": 20,
"key3": "Hello",
"key4": 3.14,
"key5": [1, 2, 3],
"key6": {"nested_key": "nested_value"},
"key8": "2022-01-01",
"key9": None,
"key10": {"sub_key1": 5, "sub_key2": "world"},
"key11": [4.5, 6.7, 8.9],
"key12": False,
"key13": "42",
"key14": ["apple", "banana", "cherry"],
"key15": {"nested_key2": [1, 2, 3]},
"key16": 7.77,
"key17": "test",
"key18": {"sub_key3": "value3", "sub_key4": 10},
"key19": [True, False],
"key20": 12345
}
ratio = 56 #this is the ratio for which we want to encode the data, it can be any number except 0 and 1
encoded_data = bc.encode_in_ratio(json_data,ratio) #encode_in_ratio takes 2 paramter which is Data and the ratio number, and returns the encoded data
print("Encoded data : \n", encoded_data)
## encoded_data can be passed to GPT and after getting back the response it will be decoded using decode_df() method
original_data = bc.decode_in_ratio(encoded_data,ratio) #decode_str takes 2 parameters which are the encoded_data and encoding and returns the original json
print("Original data : \n",original_data)
```
Output
```python
Encoded data :
{'key1': 560, 'key2': 1120, 'key3': 'Hello', 'key4': 175.84, 'key5': [56, 112, 168], 'key6': {'nested_key': 'nested_value'}, 'key8': '2022-01-01', 'key9': None, 'key10': {'sub_key1': 280, 'sub_key2': 'world'}, 'key11': [252.0, 375.2, 498.40000000000003], 'key12': 0, 'key13': '42', 'key14': ['apple', 'banana', 'cherry'], 'key15': {'nested_key2': [56, 112, 168]}, 'key16': 435.12, 'key17': 'test', 'key18': {'sub_key3': 'value3', 'sub_key4': 560}, 'key19': [56, 0], 'key20': 691320}
Original data :
{'key1': 10.0, 'key2': 20.0, 'key3': 'Hello', 'key4': 3.14, 'key5': [1.0, 2.0, 3.0], 'key6': {'nested_key': 'nested_value'}, 'key8': '2022-01-01', 'key9': None, 'key10': {'sub_key1': 5.0, 'sub_key2': 'world'}, 'key11': [4.5, 6.7, 8.9], 'key12': 0.0, 'key13': '42', 'key14': ['apple', 'banana', 'cherry'], 'key15': {'nested_key2': [1.0, 2.0, 3.0]}, 'key16': 7.7700000000000005, 'key17': 'test', 'key18': {'sub_key3': 'value3', 'sub_key4': 10.0}, 'key19': [1.0, 0.0], 'key20': 12345.0}
```
### Encoding and Decoding values and passing it to OPENAI
Pass your data to OPENAI without leaking your sensitive data.
```python
from bc_endecoder.replace import BaseCoder
from bc_endecoder.extract import extract_pdf
from openai import OpenAI
from dotenv import load_dotenv
import os
load_dotenv()
def gpt_call(data):
client = OpenAI(api_key= os.getenv("OPENAI_API_KEY"))
response1 = client.chat.completions.create(
messages=[{"role": "system", "content": f"""You are a Convert the given {data} to csv format"""},
{"role": "user", "content": '''Just convert the given text into csv format, and return the output'''}],
model="gpt-4",
temperature=0
)
output=response1.choices[0].message.content
return output
bc = BaseCoder()
data = extract_pdf("Dummy.pdf") # Extracting PDF data using the function from bc_endecoder package
print('PDF data : \n',data)
encoded_data, encodings = bc.encode_str(data) # Encoding the data and get the encoded data with their encodings
print('\nEncoded Data from Package : \n',encoded_data)
gpt_response = gpt_call(encoded_data) # Calling GPT-4 API to get the response from the encoded data
print('\nGPT Response :\n'+gpt_response)
decoded_data = bc.decode_str(gpt_response, encodings) # Decoding the encoded data and get your original data
print('\nDecoding the Encoded Values : \n',decoded_data)
```
Download the [Dummy.pdf](https://github.com/Distructor2404/BC-en-de-coder/blob/4452b436b3bf21bee1847236c357c317825077da/Dummy.pdf?raw=true) file used in the above code
Output
```python
PDF data :
Blenheim Chalcot Mumbai Andheri 87656 Phone number - 9878787878 Date : 23-02-2024 Invoice Statement HSBC bank Mumbai Andheri 787656 Account Holder: Abhishek Kumar Singh Account Number: 438743894378 Statement Period: 23-01-2024 to 15-01-2024 ---------------------------------------------------------------------------------------------------------- | Date | Description | Withdrawals | Deposits | Balance | |------------|------------------|------------------|--------------|-------------| | 2023-01-01 | Opening Balance | - | $10,000 | $10,000 | | 2023-01-05 | Payment received | - | $5,000 | $15,000 | | 2023-01-10 | Grocery Shopping | $200 | - | $14,800 | | 2023-01-15 | Salary Deposit | - | $8,000 | $22,800 | | 2023-01-25 | Utility Bill | $100 | - | $22,700 | | 2023-01-31 | Monthly Fee | $10 | - | $22,690 | ---------------------------------------------------------------------------------------------------------- Ending Balance: $22,690 Thank you for choosing HSBC Bank. If you have any questions, please contact our customer support at 8927348737.
Encoded Data from Package :
Blenheim Chalcot Mumbai Andheri 401363147298 Phone number - 906449033591 Date : 338480365517-280971266531-390031187131 Invoice Statement HSBC bank Mumbai Andheri 466271837735 Account Holder: Abhishek Kumar Singh Account Number: 300534140052 Statement Period: 338480365517-170754211816-390031187131 to 939324337053-170754211816-390031187131 ---------------------------------------------------------------------------------------------------------- | Date | Description | Withdrawals | Deposits | Balance | |------------|------------------|------------------|--------------|-------------| | 120522004913-170754211816-170754211816 | Opening Balance | - | $237668946781,294663348315 | $237668946781,294663348315 | | 120522004913-170754211816-027682990558 | Payment received | - | $877646736189,294663348315 | $939324337053,294663348315 | | 120522004913-170754211816-237668946781 | Grocery Shopping | $905935621694 | - | $255360822746,100094946280 | | 120522004913-170754211816-939324337053 | Salary Deposit | - | $511972984598,294663348315 | $804636746266,100094946280 | | 120522004913-170754211816-755648715445 | Utility Bill | $068679160933 | - | $804636746266,517699474565 | | 120522004913-170754211816-243591450716 | Monthly Fee | $237668946781 | - | $804636746266,559182488649 | ---------------------------------------------------------------------------------------------------------- Ending Balance: $804636746266,559182488649 Thank you for choosing HSBC Bank. If you have any questions, please contact our customer support at 550092238315.
GPT Response :
"Date","Description","Withdrawals","Deposits","Balance"
"120522004913-170754211816-170754211816","Opening Balance","-","$237668946781,294663348315","$237668946781,294663348315"
"120522004913-170754211816-027682990558","Payment received","-","$877646736189,294663348315","$939324337053,294663348315"
"120522004913-170754211816-237668946781","Grocery Shopping","$905935621694","-","$255360822746,100094946280"
"120522004913-170754211816-939324337053","Salary Deposit","-","$511972984598,294663348315","$804636746266,100094946280"
"120522004913-170754211816-755648715445","Utility Bill","$068679160933","-","$804636746266,517699474565"
"120522004913-170754211816-243591450716","Monthly Fee","$237668946781","-","$804636746266,559182488649"
Decoding the Encoded Values :
"Date","Description","Withdrawals","Deposits","Balance"
"2023-01-01","Opening Balance","-","$10,000","$10,000"
"2023-01-05","Payment received","-","$5,000","$15,000"
"2023-01-10","Grocery Shopping","$200","-","$14,800"
"2023-01-15","Salary Deposit","-","$8,000","$22,800"
"2023-01-25","Utility Bill","$100","-","$22,700"
"2023-01-31","Monthly Fee","$10","-","$22,690"
```
Download the above response by clicking [here](https://github.com/Distructor2404/BC-en-de-coder/blob/4452b436b3bf21bee1847236c357c317825077da/Response.txt?raw=true)
Raw data
{
"_id": null,
"home_page": "https://github.com/Distructor2404/bc-en-de-coder",
"name": "bc-en-de-coder",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "python,Data secure,Encoder,Decoder,pdf reader",
"author": "Abhishek Kumar Singh",
"author_email": "<abhishek123kumar123singh@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/14/9e/ccd50bae9bd1c5520dc1d92ed3f8d2dcbc8f71aac4cb4fa64273468a7c15/bc-en-de-coder-0.0.20.tar.gz",
"platform": null,
"description": "\n\n# **BC-EnDeCoder**\n\nBC-EnDeCoder is a Python library that provides a secure way to encode and decode data for use with Large Language Models (LLM). The library allows you to protect sensitive information by passing a fake dummy value, which is then encoded and decoded to and from its original form after receiving a response from the LLM.\n\n## Features\n\n- **Secure Encoding and Decoding:** Protect your sensitive data by encoding it with a fake dummy value and decoding it back to the original form after interacting with an LLM.\n\n- **Easy Integration:** Simple and easy-to-use functions for encoding and decoding data, making it convenient to integrate into your projects.\n\n- **Customizable Encoding Parameters:** Fine-tune the encoding process with customizable parameters to suit your specific use case.\n\n## Installation\n\nTo install BC-EnDeCoder, you can use the following pip command:\n\n```bash \npip install bc-en-de-coder \n```\n\n\n## How it Works\n\nBC-EnDeCoder facilitates a secure interaction with LLMs through a three-step process:\n\n- **Encoding with a Dummy Value:** Sensitive data is encoded using a fake value, providing an added layer of security during transmission to an LLM.\n\n- **Interaction with LLM:** The encoded data is then passed to the LLM for analysis or processing.\n\n- **Decoding the Response:** Upon receiving the LLM's response, BC-EnDeCoder decodes it, revealing the original information without compromising its security.\n\n\n\n\n### Encoding and Decoding values in string\n\nEncode and decode values in string using the `encode_str()` and `decode_str()` methods. \n\n```python\nfrom bc_endecoder.replace import BaseCoder\n\nbc = BaseCoder()\n\ntext = '''\n This is a dummy text with value 200,100,150,250.\n We need to protect these values.\n '''\n\nencoded_text,encodings = bc.encode_str(text) #encode_str takes 1 paramter which is the text and returns the encoded text and encoding\nprint(\"Encoded Text : \\n\",encoded_text)\n\n## encoded_text can be passed to GPT and after getting back the response it will be decoded using decode_str() method\n\noriginal_text = bc.decode_str(encoded_text,encodings) #decode_str takes 2 parameters which are the encoded_text and encoding and returns the original text\nprint(\"\\nOriginal Text : \\n\",original_text)\n```\n\nOutput\n```python\nEncoded Text : \n \n This is a dummy text with value 4858416350,7636580946,0858875814,8301435677.\n We need to protect these values.\n \n\nOriginal Text : \n \n This is a dummy text with value 200,100,150,250.\n We need to protect these values.\n```\n\n\n\n\n### Encoding and Decoding values in Dataframe\n\nEncode and decode values in Dataframe using the `encode_df()` and `decode_df()` methods.\n\n```python\nfrom bc_endecoder.replace import BaseCoder\nimport pandas as pd\nimport numpy as np\n\nbc = BaseCoder()\n\ndata = {\n 'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'],\n 'Age': [25, 30, 22, 35, 28],\n 'City': ['New York', 'San Francisco', 'Los Angeles', 'Chicago', 'Miami'],\n 'Salary': [60000, 80000, 55000, 90000, 70000]\n}\ndf = pd.DataFrame(data)\n\n\nencoded_df,encoding = bc.encode_df(df) #encode_df takes 1 paramter which is Dataframe and returns the encoded dataframe and encodings\nprint(\"Encoded_df : \\n\",encoded_df)\n\n## encoded_df can be passed to GPT and after getting back the response it will be decoded using decode_df() method\n\noriginal_df = bc.decode_df(encoded_df,encoding) #decode_str takes 2 parameters which are the encoded_df and encoding and returns the original df\nprint(\"\\nOriginal_df : \\n\",original_df)\n```\nOutput\n```python\nEncoded_df : \n Name Age City Salary\n 0 Alice 8623770624 New York 0197705789\n 1 Bob 5223314994 San Francisco 9743912420\n 2 Charlie 1795473060 Los Angeles 8982145407\n 3 David 6439787181 Chicago 6618233087\n 4 Eva 4699492207 Miami 6680877680\n\nOriginal_df : \n Name Age City Salary\n 0 Alice 25 New York 60000\n 1 Bob 30 San Francisco 80000\n 2 Charlie 22 Los Angeles 55000\n 3 David 35 Chicago 90000\n 4 Eva 28 Miami 70000\n```\n\n\n\n\n### Encoding and Decoding values with a ratio in Dataframe, Json or String\n\nEncode and decode values with a ratio in Dataframe, Json or String using the `encode_df_ratio()` and `decode_df_ratio()` methods.\n\n```python\nfrom bc_endecoder.replace import BaseCoder\nimport pandas as pd\nimport numpy as np\n\nbc = BaseCoder()\n\njson_data = {\n \"key1\": 10,\n \"key2\": 20,\n \"key3\": \"Hello\",\n \"key4\": 3.14,\n \"key5\": [1, 2, 3],\n \"key6\": {\"nested_key\": \"nested_value\"},\n \"key8\": \"2022-01-01\",\n \"key9\": None,\n \"key10\": {\"sub_key1\": 5, \"sub_key2\": \"world\"},\n \"key11\": [4.5, 6.7, 8.9],\n \"key12\": False,\n \"key13\": \"42\",\n \"key14\": [\"apple\", \"banana\", \"cherry\"],\n \"key15\": {\"nested_key2\": [1, 2, 3]},\n \"key16\": 7.77,\n \"key17\": \"test\",\n \"key18\": {\"sub_key3\": \"value3\", \"sub_key4\": 10},\n \"key19\": [True, False],\n \"key20\": 12345\n}\n\nratio = 56 #this is the ratio for which we want to encode the data, it can be any number except 0 and 1\n\nencoded_data = bc.encode_in_ratio(json_data,ratio) #encode_in_ratio takes 2 paramter which is Data and the ratio number, and returns the encoded data\nprint(\"Encoded data : \\n\", encoded_data)\n\n## encoded_data can be passed to GPT and after getting back the response it will be decoded using decode_df() method\n\noriginal_data = bc.decode_in_ratio(encoded_data,ratio) #decode_str takes 2 parameters which are the encoded_data and encoding and returns the original json\nprint(\"Original data : \\n\",original_data)\n```\n\nOutput\n```python\nEncoded data : \n {'key1': 560, 'key2': 1120, 'key3': 'Hello', 'key4': 175.84, 'key5': [56, 112, 168], 'key6': {'nested_key': 'nested_value'}, 'key8': '2022-01-01', 'key9': None, 'key10': {'sub_key1': 280, 'sub_key2': 'world'}, 'key11': [252.0, 375.2, 498.40000000000003], 'key12': 0, 'key13': '42', 'key14': ['apple', 'banana', 'cherry'], 'key15': {'nested_key2': [56, 112, 168]}, 'key16': 435.12, 'key17': 'test', 'key18': {'sub_key3': 'value3', 'sub_key4': 560}, 'key19': [56, 0], 'key20': 691320}\n\nOriginal data : \n {'key1': 10.0, 'key2': 20.0, 'key3': 'Hello', 'key4': 3.14, 'key5': [1.0, 2.0, 3.0], 'key6': {'nested_key': 'nested_value'}, 'key8': '2022-01-01', 'key9': None, 'key10': {'sub_key1': 5.0, 'sub_key2': 'world'}, 'key11': [4.5, 6.7, 8.9], 'key12': 0.0, 'key13': '42', 'key14': ['apple', 'banana', 'cherry'], 'key15': {'nested_key2': [1.0, 2.0, 3.0]}, 'key16': 7.7700000000000005, 'key17': 'test', 'key18': {'sub_key3': 'value3', 'sub_key4': 10.0}, 'key19': [1.0, 0.0], 'key20': 12345.0}\n```\n\n\n\n\n### Encoding and Decoding values and passing it to OPENAI \n\nPass your data to OPENAI without leaking your sensitive data.\n\n```python\nfrom bc_endecoder.replace import BaseCoder\nfrom bc_endecoder.extract import extract_pdf\nfrom openai import OpenAI\nfrom dotenv import load_dotenv\nimport os\n\nload_dotenv()\n\ndef gpt_call(data):\n client = OpenAI(api_key= os.getenv(\"OPENAI_API_KEY\")) \n response1 = client.chat.completions.create(\n messages=[{\"role\": \"system\", \"content\": f\"\"\"You are a Convert the given {data} to csv format\"\"\"},\n {\"role\": \"user\", \"content\": '''Just convert the given text into csv format, and return the output'''}],\n model=\"gpt-4\",\n temperature=0\n )\n output=response1.choices[0].message.content\n return output\n\nbc = BaseCoder()\n\ndata = extract_pdf(\"Dummy.pdf\") # Extracting PDF data using the function from bc_endecoder package\nprint('PDF data : \\n',data)\n\nencoded_data, encodings = bc.encode_str(data) # Encoding the data and get the encoded data with their encodings\nprint('\\nEncoded Data from Package : \\n',encoded_data)\n\ngpt_response = gpt_call(encoded_data) # Calling GPT-4 API to get the response from the encoded data\nprint('\\nGPT Response :\\n'+gpt_response)\n\ndecoded_data = bc.decode_str(gpt_response, encodings) # Decoding the encoded data and get your original data\nprint('\\nDecoding the Encoded Values : \\n',decoded_data)\n\n```\nDownload the [Dummy.pdf](https://github.com/Distructor2404/BC-en-de-coder/blob/4452b436b3bf21bee1847236c357c317825077da/Dummy.pdf?raw=true) file used in the above code\n\n\nOutput\n```python\nPDF data : \n Blenheim Chalcot Mumbai Andheri 87656 Phone number - 9878787878 Date : 23-02-2024 Invoice Statement HSBC bank Mumbai Andheri 787656 Account Holder: Abhishek Kumar Singh Account Number: 438743894378 Statement Period: 23-01-2024 to 15-01-2024 ---------------------------------------------------------------------------------------------------------- | Date | Description | Withdrawals | Deposits | Balance | |------------|------------------|------------------|--------------|-------------| | 2023-01-01 | Opening Balance | - | $10,000 | $10,000 | | 2023-01-05 | Payment received | - | $5,000 | $15,000 | | 2023-01-10 | Grocery Shopping | $200 | - | $14,800 | | 2023-01-15 | Salary Deposit | - | $8,000 | $22,800 | | 2023-01-25 | Utility Bill | $100 | - | $22,700 | | 2023-01-31 | Monthly Fee | $10 | - | $22,690 | ---------------------------------------------------------------------------------------------------------- Ending Balance: $22,690 Thank you for choosing HSBC Bank. If you have any questions, please contact our customer support at 8927348737. \n\nEncoded Data from Package : \n Blenheim Chalcot Mumbai Andheri 401363147298 Phone number - 906449033591 Date : 338480365517-280971266531-390031187131 Invoice Statement HSBC bank Mumbai Andheri 466271837735 Account Holder: Abhishek Kumar Singh Account Number: 300534140052 Statement Period: 338480365517-170754211816-390031187131 to 939324337053-170754211816-390031187131 ---------------------------------------------------------------------------------------------------------- | Date | Description | Withdrawals | Deposits | Balance | |------------|------------------|------------------|--------------|-------------| | 120522004913-170754211816-170754211816 | Opening Balance | - | $237668946781,294663348315 | $237668946781,294663348315 | | 120522004913-170754211816-027682990558 | Payment received | - | $877646736189,294663348315 | $939324337053,294663348315 | | 120522004913-170754211816-237668946781 | Grocery Shopping | $905935621694 | - | $255360822746,100094946280 | | 120522004913-170754211816-939324337053 | Salary Deposit | - | $511972984598,294663348315 | $804636746266,100094946280 | | 120522004913-170754211816-755648715445 | Utility Bill | $068679160933 | - | $804636746266,517699474565 | | 120522004913-170754211816-243591450716 | Monthly Fee | $237668946781 | - | $804636746266,559182488649 | ---------------------------------------------------------------------------------------------------------- Ending Balance: $804636746266,559182488649 Thank you for choosing HSBC Bank. If you have any questions, please contact our customer support at 550092238315. \n\nGPT Response :\n\"Date\",\"Description\",\"Withdrawals\",\"Deposits\",\"Balance\"\n\"120522004913-170754211816-170754211816\",\"Opening Balance\",\"-\",\"$237668946781,294663348315\",\"$237668946781,294663348315\"\n\"120522004913-170754211816-027682990558\",\"Payment received\",\"-\",\"$877646736189,294663348315\",\"$939324337053,294663348315\"\n\"120522004913-170754211816-237668946781\",\"Grocery Shopping\",\"$905935621694\",\"-\",\"$255360822746,100094946280\"\n\"120522004913-170754211816-939324337053\",\"Salary Deposit\",\"-\",\"$511972984598,294663348315\",\"$804636746266,100094946280\"\n\"120522004913-170754211816-755648715445\",\"Utility Bill\",\"$068679160933\",\"-\",\"$804636746266,517699474565\"\n\"120522004913-170754211816-243591450716\",\"Monthly Fee\",\"$237668946781\",\"-\",\"$804636746266,559182488649\"\n\nDecoding the Encoded Values : \n \"Date\",\"Description\",\"Withdrawals\",\"Deposits\",\"Balance\"\n\"2023-01-01\",\"Opening Balance\",\"-\",\"$10,000\",\"$10,000\"\n\"2023-01-05\",\"Payment received\",\"-\",\"$5,000\",\"$15,000\"\n\"2023-01-10\",\"Grocery Shopping\",\"$200\",\"-\",\"$14,800\"\n\"2023-01-15\",\"Salary Deposit\",\"-\",\"$8,000\",\"$22,800\"\n\"2023-01-25\",\"Utility Bill\",\"$100\",\"-\",\"$22,700\"\n\"2023-01-31\",\"Monthly Fee\",\"$10\",\"-\",\"$22,690\"\n```\nDownload the above response by clicking [here](https://github.com/Distructor2404/BC-en-de-coder/blob/4452b436b3bf21bee1847236c357c317825077da/Response.txt?raw=true) \n\n\n\n",
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