Simple Data Toolkit - Tutorial - BitTorrent API - Python

October 10, 2024 at 6:05 AM UTC

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Simple Data Toolkit provides an API for retrieving transactions from BitTorrent.

To retrieve all transactions in Python, using Simple Data Toolkit, we can do the following:


from sdtk import com_sdtk_api_BitTorrentAPI



def printer(data, reader):

    print(reader.toArrayOfNativeMaps(None))



com_sdtk_api_BitTorrentAPI.transactionsAPI().retrieveData({}, printer)

We can also convert the transactions, to SDTK's internal event/calendar format with the BitTorrentFormat class like so:


from sdtk import com_sdtk_api_BitTorrentAPI,com_sdtk_calendar_BitTorrentFormat



def printerEvents(data, reader):

    etherScan = com_sdtk_calendar_BitTorrentFormat.instance()

    for event in reader.toArrayOfNativeMaps(None):

        ci = etherScan.read(event)

        print(ci)



com_sdtk_api_BitTorrentAPI.transactionsAPI().retrieveData({}, printerEvents)

We can also search by address, startTimestamp, and endTimestamp.

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Simple Data Toolkit - Tutorial - Ethereum via Etherscan - PythonSimple Data Toolkit provides an unofficial API for reading Ethereum transactions from the Etherscan API. (At the time of this writing, the release of this is pending for complete support, but it is coming soon) To retrieve all Ethereum transactions for a particular address we can do the following:

from sdtk import com_sdtk_api_EtherscanAPI

def printer(data, reader):
    print(reader.toArrayOfNativeMaps(None))

com_sdtk_api_EtherscanAPI.transactionsAPI().retrieveData({"address": "0xAddress"}, printer)

Replace 0xAddress with the address you wish to search on. Additionally, we can convert the transactions to our standard internal event interface like so:

from sdtk import com_sdtk_api_EtherscanAPI,com_sdtk_calendar_EtherscanFormat

def printerEvents(data, reader):
    etherScan = com_sdtk_calendar_EtherscanFormat.instance()
    for event in reader.toArrayOfNativeMaps(None):
        ci = etherScan.read(event)
        print(ci.start.toString())

com_sdtk_api_EtherscanAPI.transactionsAPI().retrieveData({"address": "0xAddress"}, printerEvents)

Replace 0xAddress with the address you wish to search on.Simple Data Toolkit - Tutorial - Git API - PythonSimple Data Toolkit provides an unofficial API for reading files, commits, branches, and repos from the Git API. (At the time of this writing, the release of this is pending for complete support, but it is coming soon) To retrieve all repos a user has using Simple Data Toolkit, we can do the following:

	from sdtk import com_sdtk_api_GitAPI

	def printer(data, reader):
	    print(reader.toArrayOfNativeMaps(None))

	com_sdtk_api_GitAPI.reposAPI().retrieveData({"owner": "Vis-LLC"}, printer)

To retrieve all branches a repo has using Simple Data Toolkit, we can do the following:

	from sdtk import com_sdtk_api_GitAPI

	def printer(data, reader):
	    print(reader.toArrayOfNativeMaps(None))

	com_sdtk_api_GitAPI.branchesAPI().retrieveData({"owner": "Vis-LLC", "repo": "Simple-Data-Toolkit"}, printer)

To retrieve all the files in a branch using Simple Data Toolkit, we can do the following:

	from sdtk import com_sdtk_api_GitAPI

	def printer(data, reader):
	    print(reader.toArrayOfNativeMaps(None))

	com_sdtk_api_GitAPI.filesAPI().retrieveData({"owner": "Vis-LLC", "repo": "Simple-Data-Toolkit", "branch": "main"}, printer)

To retrieve the data in a file using Simple Data Toolkit, we can do the following:

	from sdtk import com_sdtk_api_GitAPI

	def printerData(data, reader):
	    print(data)

	com_sdtk_api_GitAPI.retrieveAPI().retrieveData({"owner": "Vis-LLC", "repo": "Simple-Data-Toolkit-UI", "branch": "main", "path": "index.html"}, printerData)

We can also login using a personal access token (https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens)

	from sdtk import com_sdtk_api_GitAPI

	def printerData(data, reader):
	    print(data)

	com_sdtk_api_GitAPI.instance().setKey("Personal Access Token Here").retrieveAPI().retrieveData({"owner": "Vis-LLC", "repo": "Simple-Data-Toolkit-UI", "branch": "main", "path": "index.html"}, printerData)

Simple Data Toolkit - Tutorial - ACM Events - PythonSimple Data Toolkit provides an API for retrieving events from ACM (Association of Computing Machinery). (At the time of this writing, the release of this is pending for complete support, but it is coming soon) To retrieve all events in Python, using Simple Data Toolkit, we can do the following:

from sdtk import com_sdtk_api_ACMAPI

def printer(data, reader):
    print(reader.toArrayOfNativeMaps(None))
    
com_sdtk_api_ACMAPI.eventsAPI().retrieveData({}, printer)

We can also convert the transactions, to SDTK's internal event/calendar format with the ACMEventFormat class like so:

from sdtk import com_sdtk_api_ACMAPI,com_sdtk_calendar_ACMEventFormat

def printerEvents(data, reader):
    acm = com_sdtk_calendar_ACMEventFormat.instance()
    for event in reader.toArrayOfNativeMaps(None):
        ci = acm.read(event)
        print(ci)

com_sdtk_api_ACMAPI.eventsAPI().retrieveData({}, printerEvents)

Simple Data Toolkit - Playstation Trophies - PythonSimple Data Toolkit provides an API for retrieving trophies from PlayStation. (Release pending) You will need to get an NPSSO token first: Login via https://store.playstation.com And then access https://ca.account.sony.com/api/v1/ssocookie This will need to be set in an environment variable PSN_NPSSO Or you can set it in Python with com_sdtk_api_PSNAPI.setNpsso To retrieve all trophies for an account id in Python, using Simple Data Toolkit, we can do the following:

from sdtk import com_sdtk_api_PSNAPI

def printer(data, reader):
	print(reader.toArrayOfNativeMaps(None))
    
com_sdtk_api_PSNAPI.trophyTitlesAPI().retrieveData({"accountId": None}, printer)

To retrieve all trophies for a given title, you'll need it's communication id and use the following Python code:

from sdtk import com_sdtk_api_PSNAPI

def printer(data, reader):
	print(reader.toArrayOfNativeMaps(None))

com_sdtk_api_PSNAPI.trophiesForTitleAPI().retrieveData({"communicationId": "NPWR20188_00", "trophyGroupId": None}, printer)

To retrieve all trophies earned for a given title, you'll need it's communication id and use the following Python code:

from sdtk import com_sdtk_api_PSNAPI

def printer(data, reader):
	print(reader.toArrayOfNativeMaps(None))

com_sdtk_api_PSNAPI.trophiesEarnedForATitleAPI().retrieveData({"accountId": None, "communicationId": "NPWR20188_00", "trophyGroupId": None}, printer)

Simple Data Toolkit - Tutorial - Ortingo API - PythonAt the time of this writing, Ortingo does not have an official API. Fortunately, Simple Data Toolkit provides an unofficial API for reading posts (at the time of this writing, the release of this is pending for complete support, but it is coming soon) To retrieve all posts for a given user in Python, using Simple Data Toolkit, we can do the following:

from sdtk import com_sdtk_api_OrtingoAPI


def printer(data, reader):
    print(reader.toArrayOfNativeMaps(None))

com_sdtk_api_OrtingoAPI.postsAPI().retrieveData({"owner": "60CQ59FN46SVQFXJ"}, printer)

Let's suppose we want only a list of titles for a given user, we can do this instead:

from sdtk import com_sdtk_api_OrtingoAPI


def printer(data, reader):
    print(reader.filterColumnsOnly(["title"]).toArrayOfNativeMaps(None))

com_sdtk_api_OrtingoAPI.postsAPI().retrieveData({"owner": "60CQ59FN46SVQFXJ"}, printer)

We can also pull suggested content from Ortingo with the following, where the topics we are searching on are provided with the query parameter (in this case it's value is data):

from sdtk import com_sdtk_api_OrtingoAPI

def printerUrls(data, reader):
    print(reader.filterColumnsOnly(["url"]).toArrayOfNativeMaps(None))

com_sdtk_api_OrtingoAPI.suggestionsAPI().retrieveData({"query": "data"}, printerUrls)

And finally, we can also pull comments attached to a post in Ortingo with the following, where the user is myself and the post is a test post I created:

from sdtk import com_sdtk_api_OrtingoAPI

def printerComments(data, reader):
    print(reader.filterColumnsOnly(["commentDate", "post"]).toArrayOfNativeMaps(None))

com_sdtk_api_OrtingoAPI.commentsAPI().retrieveData({"owner": "60CQ59FN46SVQFXJ", "id": "test"}, printerComments)

The columns supported at the time of this writing are: - id - owner - title - subtitle - post - url For comments the following columns are supported: - id - owner - commentDate - replyTo - post Simple Data Toolkit - Tutorial - IEEE Events API - PythonSimple Data Toolkit provides an unofficial API for reading events from the IEEE API. (At the time of this writing, the release of this is pending for complete support, but it is coming soon) To retrieve all events in Python, using Simple Data Toolkit, we can do the following:

from sdtk import com_sdtk_api_IEEEAPI,com_sdtk_calendar_IEEEEventFormat

def printer(data, reader):
    ieee = com_sdtk_calendar_IEEEEventFormat.instance
    for event in reader.toArrayOfNativeMaps(None):
        ci = ieee.read(event)
        print(ci.summary)


com_sdtk_api_IEEEAPI.eventsAPI().retrieveData({"limit": "2"}, printer)

We can search using the following parameters: - limit - The limit to the number of events to return - start - The start datetime to search - end - The end datetime to search The columns supported at the time of this writing are: - created-at mapped to created - start-time mapped to start - end-time mapped to end - title mapped to summary - uid mapped to uid Simple Data Toolkit - Python - Loading Data and Text Through ChatGPTSimple Data Toolkit provides an API for passing data and text to ChatGPT and extracting related data or text. (At the time of this writing, the release of this is pending for complete support, but it is coming soon) Below is an example which uses an embedded data of users and orders, plus text loaded in from a file.

from sdtk import com_sdtk_api_ChatGPTAPI, com_sdtk_table_ArrayOfMapsReader

#Set

def callbackData(reader):
    print(reader.toArrayOfNativeMaps(None))

def callbackText(data):
    print(data)

users = [
    {"user_id": 1, "first_name": "Sally", "last_name": "Franklin"},
    {"user_id": 2, "first_name": "Lucas", "last_name": "Franklin"},
    {"user_id": 3, "first_name": "Joe", "last_name": "Romeo"},
    {"user_id": 4, "first_name": "Julie", "last_name": "Romeo"},
    {"user_id": 5, "first_name": "Lucia", "last_name": "Templeton"},
]

orders = [
    {"order_id": 1, "user_id": 3, "item_desc": "Book 1", "item_quantity": 2, "date": "2024-01-01"},
    {"order_id": 2, "user_id": 3, "item_desc": "Book 2", "item_quantity": 1, "date": "2024-02-01"},
    {"order_id": 3, "user_id": 3, "item_desc": "Book 3", "item_quantity": 3, "date": "2024-03-01"},
    {"order_id": 4, "user_id": 3, "item_desc": "Book 4", "item_quantity": 0, "date": "2024-04-01"},
    {"order_id": 5, "user_id": 3, "item_desc": "Book 5", "item_quantity": 4, "date": "2024-05-01"}
]

com_sdtk_api_ChatGPTAPI.queryAsReaderWithDataAPI().execute("What can we determine about the buying habits of all of our users?", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackData)
com_sdtk_api_ChatGPTAPI.queryWithDataAPI().execute("What can we determine about the buying habits of all of our users?  As a narrative.", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackText)

content = ""
with open('sample.html', 'r') as content_file:
    content = content_file.read()

com_sdtk_api_ChatGPTAPI.queryWithDataAPI().execute("We also have this document in HTML format on recent trends.\n" + content + "\n\nWhat can we determine about the buying habits of all of our users?  As a narrative.", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackText)

Below is an example which queries data from ChatGPT in a table format using a DataTableReader from SDTK.

from sdtk import com_sdtk_api_ChatGPTAPI

def callback(data, reader):
    print(reader.toArrayOfNativeMaps(None))

com_sdtk_api_ChatGPTAPI.queryAsReaderAPI().retrieveData({ "query": "List all cities in the USA with known population." }, callback)

Simple Data Toolkit - Python - Loading Data and Text Through ChatGPTSimple Data Toolkit provides an API for passing data and text to ChatGPT and extracting related data or text. (At the time of this writing, the release of this is pending for complete support, but it is coming soon) Below is an example which uses an embedded data of users and orders, plus text loaded in from a file.

from sdtk import com_sdtk_api_ChatGPTAPI, com_sdtk_table_ArrayOfMapsReader

#Set

def callbackData(reader):
    print(reader.toArrayOfNativeMaps(None))

def callbackText(data):
    print(data)

users = [
    {"user_id": 1, "first_name": "Sally", "last_name": "Franklin"},
    {"user_id": 2, "first_name": "Lucas", "last_name": "Franklin"},
    {"user_id": 3, "first_name": "Joe", "last_name": "Romeo"},
    {"user_id": 4, "first_name": "Julie", "last_name": "Romeo"},
    {"user_id": 5, "first_name": "Lucia", "last_name": "Templeton"},
]

orders = [
    {"order_id": 1, "user_id": 3, "item_desc": "Book 1", "item_quantity": 2, "date": "2024-01-01"},
    {"order_id": 2, "user_id": 3, "item_desc": "Book 2", "item_quantity": 1, "date": "2024-02-01"},
    {"order_id": 3, "user_id": 3, "item_desc": "Book 3", "item_quantity": 3, "date": "2024-03-01"},
    {"order_id": 4, "user_id": 3, "item_desc": "Book 4", "item_quantity": 0, "date": "2024-04-01"},
    {"order_id": 5, "user_id": 3, "item_desc": "Book 5", "item_quantity": 4, "date": "2024-05-01"}
]

com_sdtk_api_ChatGPTAPI.queryAsReaderWithDataAPI().execute("What can we determine about the buying habits of all of our users?", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackData)
com_sdtk_api_ChatGPTAPI.queryWithDataAPI().execute("What can we determine about the buying habits of all of our users?  As a narrative.", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackText)

content = ""
with open('sample.html', 'r') as content_file:
    content = content_file.read()

com_sdtk_api_ChatGPTAPI.queryWithDataAPI().execute("We also have this document in HTML format on recent trends.\n" + content + "\n\nWhat can we determine about the buying habits of all of our users?  As a narrative.", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackText)

Below is an example which queries data from ChatGPT in a table format using a DataTableReader from SDTK.

from sdtk import com_sdtk_api_ChatGPTAPI

def callback(data, reader):
    print(reader.toArrayOfNativeMaps(None))

com_sdtk_api_ChatGPTAPI.queryAsReaderAPI().retrieveData({ "query": "List all cities in the USA with known population." }, callback)

from sdtk import com_sdtk_api_ChatGPTAPI, com_sdtk_table_ArrayOfMapsReader

#Set

def callbackData(reader):
    print(reader.toArrayOfNativeMaps(None))

def callbackText(data):
    print(data)

users = [
    {"user_id": 1, "first_name": "Sally", "last_name": "Franklin"},
    {"user_id": 2, "first_name": "Lucas", "last_name": "Franklin"},
    {"user_id": 3, "first_name": "Joe", "last_name": "Romeo"},
    {"user_id": 4, "first_name": "Julie", "last_name": "Romeo"},
    {"user_id": 5, "first_name": "Lucia", "last_name": "Templeton"},
]

orders = [
    {"order_id": 1, "user_id": 3, "item_desc": "Book 1", "item_quantity": 2, "date": "2024-01-01"},
    {"order_id": 2, "user_id": 3, "item_desc": "Book 2", "item_quantity": 1, "date": "2024-02-01"},
    {"order_id": 3, "user_id": 3, "item_desc": "Book 3", "item_quantity": 3, "date": "2024-03-01"},
    {"order_id": 4, "user_id": 3, "item_desc": "Book 4", "item_quantity": 0, "date": "2024-04-01"},
    {"order_id": 5, "user_id": 3, "item_desc": "Book 5", "item_quantity": 4, "date": "2024-05-01"}
]

com_sdtk_api_ChatGPTAPI.queryAsReaderWithDataAPI().execute("What can we determine about the buying habits of all of our users?", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackData)
com_sdtk_api_ChatGPTAPI.queryWithDataAPI().execute("What can we determine about the buying habits of all of our users?  As a narrative.", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackText)

content = ""
with open('sample.html', 'r') as content_file:
    content = content_file.read()

com_sdtk_api_ChatGPTAPI.queryWithDataAPI().execute("We also have this document in HTML format on recent trends.\n" + content + "\n\nWhat can we determine about the buying habits of all of our users?  As a narrative.", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackText)

Below is an example which queries data from ChatGPT in a table format using a DataTableReader from SDTK.

from sdtk import com_sdtk_api_ChatGPTAPI

def callback(data, reader):
    print(reader.toArrayOfNativeMaps(None))

com_sdtk_api_ChatGPTAPI.queryAsReaderAPI().retrieveData({ "query": "List all cities in the USA with known population." }, callback)

from sdtk import com_sdtk_api_ChatGPTAPI, com_sdtk_table_ArrayOfMapsReader

#Set

def callbackData(reader):
    print(reader.toArrayOfNativeMaps(None))

def callbackText(data):
    print(data)

users = [
    {"user_id": 1, "first_name": "Sally", "last_name": "Franklin"},
    {"user_id": 2, "first_name": "Lucas", "last_name": "Franklin"},
    {"user_id": 3, "first_name": "Joe", "last_name": "Romeo"},
    {"user_id": 4, "first_name": "Julie", "last_name": "Romeo"},
    {"user_id": 5, "first_name": "Lucia", "last_name": "Templeton"},
]

orders = [
    {"order_id": 1, "user_id": 3, "item_desc": "Book 1", "item_quantity": 2, "date": "2024-01-01"},
    {"order_id": 2, "user_id": 3, "item_desc": "Book 2", "item_quantity": 1, "date": "2024-02-01"},
    {"order_id": 3, "user_id": 3, "item_desc": "Book 3", "item_quantity": 3, "date": "2024-03-01"},
    {"order_id": 4, "user_id": 3, "item_desc": "Book 4", "item_quantity": 0, "date": "2024-04-01"},
    {"order_id": 5, "user_id": 3, "item_desc": "Book 5", "item_quantity": 4, "date": "2024-05-01"}
]

com_sdtk_api_ChatGPTAPI.queryAsReaderWithDataAPI().execute("What can we determine about the buying habits of all of our users?", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackData)
com_sdtk_api_ChatGPTAPI.queryWithDataAPI().execute("What can we determine about the buying habits of all of our users?  As a narrative.", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackText)

content = ""
with open('sample.html', 'r') as content_file:
    content = content_file.read()

com_sdtk_api_ChatGPTAPI.queryWithDataAPI().execute("We also have this document in HTML format on recent trends.\n" + content + "\n\nWhat can we determine about the buying habits of all of our users?  As a narrative.", None, {"Users": com_sdtk_table_ArrayOfMapsReader.readWholeArray(users), "Orders": com_sdtk_table_ArrayOfMapsReader.readWholeArray(orders)}, callbackText)

Below is an example which queries data from ChatGPT in a table format using a DataTableReader from SDTK.

from sdtk import com_sdtk_api_ChatGPTAPI

def callback(data, reader):
    print(reader.toArrayOfNativeMaps(None))

com_sdtk_api_ChatGPTAPI.queryAsReaderAPI().retrieveData({ "query": "List all cities in the USA with known population." }, callback)

Atotium News - Business Aggregate - 2024-12-02Accolade Partners Continues its Leadership in Blockchain Investing, Closes its Third Blockchain Focused Fund of Funds https://www.accoladepartners.com/press-release-11202024 Summary: Accolade Partners has closed its third blockchain-focused fund of funds, Accolade Blockchain III, with $202 million in total commitments. The firm, an early leader in blockchain investments, manages $1.3 billion in blockchain-focused assets. This latest fund aims to support seed and early-stage blockchain managers, along with investments in secondary opportunities and GP stakes. Founded in 2000, Accolade manages $6.1 billion across venture capital, private equity, and blockchain strategies, and is known for its active role in sourcing and enhancing promising investment funds. Accolade's continued commitment to blockchain investment underscores its long-standing position as an industry thought leader. Citi Successfully Completes Separation of Consumer, Small and Middle Market Businesses from Institutional Business in Mexico https://www.citigroup.com/global/news/press-release/2024/citi-completes-separation-consumer-smb-mexico-institutional-business Summary: Citi has successfully separated its institutional banking business in Mexico from its consumer, small, and middle-market businesses, establishing two separate financial entities: Grupo Financiero Citi M xico and Grupo Financiero Banamex. This move is part of Citi's broader strategy to streamline its operations. Grupo Financiero Citi M xico will focus on institutional clients, while Grupo Financiero Banamex will concentrate on retail banking and related services. This separation positions both groups for growth and allows Citi to progress toward a planned IPO for Grupo Financiero Banamex, enhancing shareholder value. The bank continues its strategy of exiting consumer banking in various global markets as part of its simplification efforts. These Bricks-and-Mortar Stores Thrive in an Online World https://www.wsj.com/business/retail/these-bricks-and-mortar-stores-thrive-in-an-online-world-2d666308 Summary: Despite the growth of online shopping, many bricks-and-mortar stores are thriving by offering unique in-store experiences and blending their physical presence with online capabilities. Retailers like T.J. Maxx create a 'treasure hunt' atmosphere, attracting value-conscious customers across different income levels, while companies like PetSmart integrate physical stores into their delivery logistics, using them as fulfillment centers. Retailers have recognized the need to integrate the online and offline shopping experience to attract and retain customers, leading to resilient retail operations despite online competition. As e-commerce continues to grow, its penetration is expected to plateau, with physical stores remaining a key component of a competitive retail strategy. Are Old People The Only Ones Using Google? https://boomcycle.com/blog/only-old-people-use-google/ Summary: The article explores the misconception that only older generations use Google, highlighting how Google remains the dominant search engine across all age groups. Younger users increasingly opt for visually-oriented platforms like TikTok and Instagram for searches, while older users are more engaged with Google due to increasing digital literacy. Search behaviors vary by age, with Gen Z favoring longer queries and mobile devices, while older adults lean towards focused searches and voice search. Social media's role in product discovery is significant, challenging traditional search engines. Visual search and mobile optimization are growing trends among younger users. YouTube's dominance in video search results emphasizes Google's broad reach, despite the rise of platforms like TikTok and Instagram. Bing and alternative search engines like DuckDuckGo cater to privacy-conscious users. Overall, while Google's demographics may be aging slightly, it continues to adapt and maintain relevance across generations.Simple Data Toolkit - Python - Loading Images Through ChatGPTSimple Data Toolkit provides an API for passing images to ChatGPT and extracting related text. (At the time of this writing, the release of this is pending for complete support, but it is coming soon) To generate a LaTeX document based on a series of images (effectively doing OCR) we can do the following:

from sdtk import com_sdtk_api_ChatGPTAPI
import os
import fnmatch
import re

texData = ""
texBegin = "\n\\begin{document}\n"
texEnd = "\n\\end{document}"
header = ""
footer = ""
foundBody = False

# Get a list of all .png files in the current directory
png_files = [file for file in os.listdir('.') if fnmatch.fnmatch(file, '*.png')]

def result(text):
    global texData
    global header
    global footer
    global foundBody

    text = text.replace("```latex", "").replace("```", "")
    body_match = re.search(r'\\begin{document}(.*?)\\end{document}', text, re.DOTALL)

    if body_match:
        body_content = body_match.group(1).strip()
        if len(texData) > 0:
            texData = texData + "\n" + "\\newpage" + "\n"
        else:
            header_match = re.search(r'^(.*?)\\begin{document}', text, re.DOTALL)
            header = header_match.group(1).strip()
            footer_match = re.search(r'\\end{document}(.*)$', text, re.DOTALL)
            footer = footer_match.group(1).strip()
            
        texData = texData + body_content
        foundBody = True
    else:
        foundBody = False
        print(text)

# Loop over each .png file
for file_name in png_files:
    foundBody = False
    while foundBody == False:
        com_sdtk_api_ChatGPTAPI.instance().query(result, "Can you generate a LaTeX document to represent the text in this image and format it correctly and return only the LaTeX code?", file_name)
        with open("output.tex", "w+") as file:
            # Write the string to the file
            file.write(header + texBegin + texData + texEnd + footer)

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Simple Data Toolkit - Tutorial - BitTorrent API - Python

October 10, 2024 at 6:05 AM UTC

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