Client/Runner.py at main · PICT-Chunked-Backup-Project/Client · GitHub

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from AbstractiveSummary import AbstractiveSummarizer
from FileManagement import FileManagement
from CodeConverter import FileConverter
from ImageClassification import ImageClassifier
from KeybertExtractor import KWE
from PIIData import PiiData
from VideoClassification import VC
from Sender import FileTransfer
import os


# takes the file path and returns all the data ML in a list in the format,[<path of file>,<Abstactive Summary>,<keywords>,<PII data>,< img/video tags>,]
class MLData:
    path = ""
    tessaractFilePath = ""
    piiDataTagsFilePath = ""
    objectClassificationConfigPath = ""
    frozenModelPath = ""

    def __init__(self, p, tessaract, pii, classification, frozen):
        self.path = p
        self.tessaractFileLocation = tessaract
        self.piiDataTagsFilePath = pii
        self.imageClassificationConfigPath = classification
        self.frozenModelPath = frozen

    def getMlData(self):
        list = []
        list.append(self.path)
        cc = FileConverter(self.Path, self.tessaractFileLocation)
        txtableSuffixes = (".pdf", ".docx", "xlsx", ".csv", ".pdf", ".jpg", ".png", ".gif", ".jpeg", ".raw", ".cr2", ".nef", ".orf",".sr2")
        imgSuffixes = (".jpg", ".png", ".gif", ".jpeg", ".raw", ".cr2", ".nef", ".orf", ".sr2")
        vidSuffixes = (".mp4", ".mov", ".wmv", ".avi", ".webm", ".avi", ".flv", ".mkv", ".mpeg4", ".gif")
        if self.path.endswith(txtableSuffixes):
            txtstr = cc.convert_tostr()
            asry = AbstractiveSummarizer(txtstr)  # abstractive summary
            list.append(asry.getSummary())
            kwrd = KWE(txtstr)
            list.append(kwrd.keywordExtract())  # keywords
            pdta = PiiData(txtstr, self.piiDataTagsFilePath)
            list.append(pdta.match_query_21())  # PII data result , can be low,medium, high or very high

        if self.path.endswith(imgSuffixes):
            imgc = ImageClassifier(self.path, self.objectClassificationConfigPath, self.frozenModelPath)
            list.append(imgc.classify())

        if self.path.endswith(vidSuffixes):
            vidc = VC(self.path, self.objectClassificationConfigPath, self.frozenModelPath)
            list.append((vidc.classify()))
        return list


if __name__ == '__main__':
    backupPath = input("Enter the path of the directory that has to backed up")
    tessaractFilePath = input("Enter the location of the tesseract executable")
    piiDataTagsFilePath = input("Enter the location of the PII data tags file")
    objectClassificationConfigFilePath = input("Enter the path of image classification config file")
    frozenModelFilePath = input("Enter the path of the frozen model")
    hostName = input("Enter the host name")

    fm = FileManagement(backupPath)

    filePaths = fm.getPathList()

    for i in range(len(filePaths)):
        chunkSize = input(
            f"Enter the chunk size for the file {os.path.basename(filePaths[i])} with file size {os.path.getsize(filePaths[i])}")
        metaobj = MLData(filePaths[i], tessaractFilePath, piiDataTagsFilePath, objectClassificationConfigFilePath,
                         frozenModelFilePath)
        MlList = metaobj.getMlData()
        ft = FileTransfer(hostName, filePaths[i], chunkSize, MlList[1], MlList[2], MlList[3], MlList[4])
        ft.establishConnection()
        ft.sendFilesDetails()
        ft.sendFileChunks()
        ft.sendMlDatails()