/Sales-Insights

Primary LanguagePythonMIT LicenseMIT

💼 Sales Insights 📞

This project allows anyone to upload a video file and be presented with a transcript and extracted questions & answers. The target user is a sales executive that wants to look back at questions asked during a call with a prospect/customer. Here is a link to a live demo if you don't want to deploy locally.

Authors / License

  • MIT License

Pre-reqs

Must have git installed. If you don't here is a great guide.

Environment Variables (Pre-Deployment)

To run this project, you will need to add the following environment variables to your .env file in the *same folder path as the app, summarizer.py.

$ touch .env
$ vi .env

copy and paste your key/value using API_KEY=<your_api_key_no_quotes> format

:wq -> ENTER

Local Deployment

Note: if you see a KeyError: 'upload_url' if means you just need to paste in your API key appropriately.

To deploy this project run:

  git clone https://github.com/ConnorBrereton/Sales-Insights.git

Next, naviate to the app directory.

cd Sales-Insights/

Install all of the dependencies using pip3

pip3 install -r requirements.txt

To run the application using Streamlit do the following:

python3 -m streamlit run summarizer.py

You should see this image pop up on localhost:8501 automatically.

App Screenshot

Demo

Below is a demo of how the application can be utilized for call analysis to extract questions and answers with full context.

Documentation

Roadmap

  • Change color scheme to match AssemblyAI.
  • Breakdown of speakers using Assembly's Speaker Diarization.
  • Dockerize the application to avoid all dependency management.
  • Remove filler words from Q&A - Breakdown.
  • Use profanity filtering to see if prospect/rep used profanity during the call.
  • Add webhooks to avoid using temporary variables to manage state within the application.
  • Can use the ChatGPT API like in this project to extract Q&A faster and put it into a ordered form without the parsing algorithm.