This project aims to enhance the BI Help Bot on Slack to better answer questions related to Superset. The improvements include integrating voice-to-text from a YouTube channel and web scraping from the official Superset blog.

To improve the BI Help Bot on Slack by:

1. Implementing a voice-to-text feature from the YouTube channel @preset.
2. Extracting data from blog posts on the official Superset website.

The project consists of two main scripts:

1. Youtube_to_text.ipynb: Converts voice to text from the YouTube channel @preset.
2. Webscraping_Preset_Blog.ipynb: Extracts data from blog posts on the official Superset website.

• Youtube_to_text.ipynb: Jupyter notebook containing the script for voice-to-text conversion from YouTube videos.
• Webscraping_Preset_Blog.ipynb: Jupyter notebook containing the script for web scraping the official Superset blog.

Before running the scripts, ensure you have the following installed:

• Python 3.7 or later
• Jupyter Notebook
• Required Python packages (listed in each notebook)

The Youtube_to_text.ipynb notebook converts audio from YouTube videos to text. Here's a detailed explanation of the code:

1. Import Libraries: The script imports necessary libraries such as pytube for downloading YouTube videos and speech_recognition for converting speech to text.

2. Download Video: Using pytube, the script downloads a video from the YouTube channel @preset specified by its URL.

3. Extract Audio: The script extracts audio from the downloaded video file using moviepy.editor.

4. Convert Speech to Text: With the speech_recognition library, the script converts the extracted audio to text. It splits the audio into smaller segments for more accurate recognition.

5. Output: The converted text is then outputted and can be saved or processed further.

The Webscraping_Preset_Blog.ipynb notebook extracts data from the official Superset blog. Here's a detailed explanation of the code:

1. Import Libraries: The script imports libraries such as requests for sending HTTP requests and BeautifulSoup for parsing HTML content.

2. Fetch Blog Content: The script sends a request to the Superset blog URL and retrieves the HTML content of the blog page.

3. Parse HTML: Using BeautifulSoup, the script parses the HTML content to identify and extract specific elements such as blog post titles, dates, and content.

4. Data Extraction: The script iterates through the parsed HTML, extracting relevant information from each blog post.

5. Output: The extracted data is then outputted in a structured format (e.g., a DataFrame) for further use or analysis.

For any questions or support, please contact [[email protected]],[[email protected]],[[email protected]],.

Note: Ensure you have the appropriate permissions to use the data from the YouTube channel and the official Superset blog for this project.