Integration of Machine Learning Model for Extracting Password-related Information from Text Files

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Overview
I propose integrating a machine-learning model within the Hashcat password generator to enhance its capabilities. The primary goal is to enable the model to read from a text file and extract pertinent information that can be used to generate personalized passwords related to the individual.

Feature Components
Machine Learning Model Integration:

Implement a machine-learning model capable of reading and processing text files.
Train the model to recognize and extract relevant information such as names, dates, and other personal details.
Text File Input Handling:

Extend Hashcat's functionality to accept input from text files for personalized password generation.
Personalized Password Generation:
You can use the information that was extracted to enhance the password generation process, creating passwords that are personalized.

Use Case Scenario
Consider a scenario where a user has a text file containing personal information about an individual (e.g., full name, birthdate, significant events). The proposed feature aims to leverage this information to generate passwords that are more relevant and potentially harder to crack, given the personalized nature of the data.

Additional Considerations
Ensure compatibility with common text file formats and structures.