AKKI-AFK/excel-mock-interviewer

Excel Mock Interviewer (Streamlit PoC)

This repository implements a single-process Streamlit Proof-of-Concept for the AI-Powered Excel Mock Interviewer.

Quickstart (local)

1. 1.Install dependencies

python -m pip install -r requirements.txt

1. 1.Run Streamlit app

streamlit run app.py

1. 1.Open http://localhost:8501

What each file does (step-by-step)

• —app.py — Streamlit UI and control; displays questions, captures answers and uploads, shows summary.
• —interview_logic.py — Manages interview state, history, session id, and orchestrates evaluation calls.
• —evaluator.py — Deterministic autograder helpers using openpyxl and an LLM wrapper (OpenAI when enabled). Returns parsed grading JSON.
• —prompts.py — Questions and prompt templates used by the grader.
• —requirements.txt — Python deps.

How grading works in the Streamlit PoC

• —For text answers: interview_logic calls evaluator.call_llm() which either calls OpenAI (chat completions) to obtain a JSON grade or uses a simple heuristic fallback.

Extending to production

• —Replace Gemini(free) grader with a high-quality production LLM and stronger prompt engineering.
• —Persist sessions to a database and files to object storage.
• —Add authentication and consent screens.
• —Implement active-learning pipeline where low-confidence grades are routed to human reviewers.