# Lecture Tool

Welcome to Lecture Tool! This is an AI application that leverages `llmware` to transcribe and analyze college lecture videos.

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### Features
1. Create `Libraries` to group transcripts and store them persistently.
2. Transcribe audio files using Whisper (built into the `llmware` library) and store them in a `Library`.
3. Ask general questions and questions about lecture content.
4. Summarize lecture content.
5. View all generated transcripts.

Each of these five features is implemented in its own file in the `pages` folder.

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### Prerequisites
1. *Python libraries*: the only required libraries to be installed are `streamlit` and `llmware`. You can install them from the `requirements.txt` file.
2. *MongoDB*: it is used to store lecture transcripts. The easiest way to install it is to use the Docker Compose file in the [LLMWare repository](https://github.com/llmware-ai/llmware/blob/main/docker-compose_mongo_milvus.yaml).
3. *FFmpeg*: it is used to convert MP3 files to WAV files that are compatible with Whipser. If you intend to use MP3 files instead of WAV files, you can [download FFmpeg here](https://www.ffmpeg.org/download.html). You will likely need to restart your computer after installation.

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### Usage
To run the program, ensure that you have `streamlit` installed. In your terminal, navigate to the `lecture_tool` directory and run `streamlit run Home.py`.

By default, Streamlit supports file uploads up to 200 MB. To increase this limit, run `streamlit run Home.py --server.maxUploadSize fileSize`, ensuring to replace `fileSize` with the maximum file size you want to upload in megabytes. For example, use 500 if you plan to upload audio files up to 500 MB in size.

Sample MP3 and WAV audio files to use the application with are available in the `sample_audio_files` directory.

The `saved_files` directory is used as a temporary location in the application's implementation and should not be modified by a user.