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Auto interview and evaluation parallel system

Description

Auto Interviewer is an autonomous interview agent which can perform interviews for any position. It has two processes which run in parallel to interview and evaluate and write to database in parallel asynchronously. It features threading for parallelization a queue system and observer pattern for automatic notification of the evaluator of every Q and A pairs. It writes to a sqlite database and save on gg sheets.

Features

  • Interviewer
  • Evaluator
  • Database local
  • Google Sheets
  • Threading
  • Queue
  • Observer Pattern
  • streamlit

Environment

  • Python 3.10.13

Installation

Requires

pip install -r requirements.txt

Before Deployment:

  • Config .streamlit/secrets.toml file
  • Create a google service account and download the json file and config the .streamlit/secrets.toml file with the path to the json file.
  • Create a google sheet and share it with the email in the json file.
  • Create a .streamlit/secrets.toml file and add the following:
[connections.gsheets]
spreadsheet = "https://docs.google.com/spreadsheets/d/xxx/edit#gid=0"
type = "service_account"
project_id = "chat-with-gg-meet"
private_key_id = "xxx"
private_key = "xxx"
client_email = "xxx@xxx.iam.gserviceaccount.com"
client_id = "xxx"
auth_uri = "https://accounts.google.com/o/oauth2/auth"
token_uri = "https://oauth2.googleapis.com/token"
auth_provider_x509_cert_url = "https://www.googleapis.com/oauth2/v1/certs"
client_x509_cert_url = "xxx"

Run app ⛄️

Run cli app:

python main.py

Run app with streamlit:

streamlit run app.py
# OR
sh bin/start.sh

Run with Docker 🐳

Docker build and run with Dockerfile:

docker build -t auto_interview_app .       # Build the docker image
docker run -p 5001:5001 auto_interview_app # Run the docker image

Run with docker-compose:

docker compose up --build -d