cokn / pdf2txt

pdf2txt

This Streamlit app is designed to convert PDF files into TXT files.

Description

The pdf2txt application provides a user-friendly interface for converting PDF documents into plain text files. It utilizes the Streamlit framework to offer an intuitive and straightforward experience.

Features

• Accepts PDF files as input for conversion.
• Converts the PDF files into TXT format.
• Preserves the text content and structure of the original PDF.
• Provides an interactive web-based interface powered by Streamlit.

Usage

1. Clone repository
2. Install the required dependencies: pip install -r requirements.txt.
3. Run the Streamlit app: streamlit run app.py.
4. Access the application in your web browser using the provided URL.
5. Upload the PDF file(s) you wish to convert. The conversion process starts automatically.
6. The resulting TXT file(s) will be available for download.

Demo

You can find a live demo of the pdf2txt app here.

Contributing

Contributions are welcome! If you encounter any issues or have suggestions for improvements, please feel free to submit an issue or pull request.

pdf2txt's Issues

Check the uploaded file for PDF format before parsing

Title: Check the uploaded file for PDF format before parsing

Description:
As part of the file parsing functionality, it is important to implement a validation step to ensure that the uploaded file is indeed a PDF document. This check is necessary to prevent errors or security vulnerabilities that may arise from attempting to parse non-PDF files.

To address this, we need to incorporate a validation mechanism that verifies the file format before proceeding with the parsing process. By performing this check upfront, we can avoid potential issues and provide users with appropriate feedback if an invalid file type is detected.

Tasks:

• Implement a file type validation function that examines the uploaded file's format.
• Specifically, check if the uploaded file is in PDF format using appropriate file signature or mime-type verification techniques.
• Display an error message or prompt the user to re-upload the correct file type if a non-PDF file is detected.
• Only proceed with parsing if the uploaded file is confirmed to be in the expected PDF format.

Expected Outcome:

By incorporating this file format validation step, we will enhance the reliability and security of the file parsing feature. Users will receive clear feedback when attempting to upload non-PDF files, ensuring that only valid PDF documents are processed further.

Additional Notes:

Consider implementing robust error handling and informative error messages to guide users during the file upload and validation process.

Screenshot:

Feature Request: Multiselect for languages and language detection Description

Feature Request: Multiselect for languages and language detection

Description

This issue proposes the addition of two features to enhance language processing capabilities:

1. Multiselect for languages: Currently, the language processing functionality supports a single language selection. However, in scenarios where documents contain mixed languages, it would be beneficial to have a multi-select option. This would allow users to select multiple languages for processing, enabling more accurate analysis and extraction of content.

2. Language detection function: It would be helpful to incorporate a language detection function that automatically identifies the language(s) present in a document. This feature would enable the system to determine the languages used within a document dynamically, eliminating the need for manual language selection.

Expected Behavior

With the proposed features implemented, users will have the ability to select multiple languages for processing documents and utilize an automated language detection function. This will enhance the accuracy and flexibility of the language processing functionality, accommodating documents with mixed languages.
Additional Information

The multi-select feature will enhance the user experience by allowing the system to process documents that contain content in different languages simultaneously. The language detection function will simplify the workflow by automatically identifying the languages present, reducing the manual effort required for language selection.

These features can benefit a wide range of applications, such as natural language processing, machine translation, sentiment analysis, and more, where dealing with multilingual documents is common.
Implementation Suggestions

1. For the multi-select feature, consider implementing a user-friendly interface that allows users to select multiple languages from a list easily.

2. For the language detection function, explore leveraging existing language detection libraries or APIs that provide reliable and accurate language identification.

3. Provide clear documentation and examples to guide users on how to utilize the multi-select and language detection features effectively.