README.md

metadata

title: InterviewCoach
emoji: 🎙️
colorFrom: green
colorTo: gray
sdk: gradio
sdk_version: 6.16.0
python_version: '3.11'
app_file: app.py
pinned: false
short_description: Local interview coach with live feedback.
models:
  - build-small-hackathon/interview-coach-3b
  - Qwen/Qwen2.5-3B-Instruct
  - openai/whisper-small
tags:
  - gradio
  - interview
  - speech-to-text
  - langgraph
  - local-ai
  - track:backyard
  - sponsor:openai
  - achievement:offgrid
  - achievement:fieldnotes

Interview Coach

Created by @vadirajkrishna.

Interview Coach is a local-first assistant for live technical interviews. It listens to noisy interview audio, extracts the actual Data Science, ML, AI, or System Design question, and shows a compact coaching card with important pointers while the candidate is answering.

Development note: the live transcription flow, coaching-card timing, and transcript extraction loop were iterated with Codex assistance.

Links

• Hugging Face Space README: https://huggingface.co/spaces/build-small-hackathon/interview-copilot-local/blob/main/README.md
• Published article: https://huggingface.co/blog/build-small-hackathon/local-interview-copilot
• Demo video: https://www.loom.com/share/d44244e43927423b9be237fbb207a65b

Why It Matters

The goal is not to handhold the candidate through the interview or generate a scripted answer. The goal is to give timely, high-signal reminders so the candidate can cover the important parts of their own answer naturally.

Interview conversations are messy: greetings, interviewer transitions, repeated words, partial transcription, candidate clarifications, and answer fragments often appear in the same transcript. Interview Coach helps by:

• Capturing the core technical question from noisy live conversation.
• Showing concise pointers that help the candidate cover the expected areas.
• Separating interviewer questions from candidate answers after the session.
• Evaluating saved Q&A exchanges with critical feedback and improvement areas.

Multi-Model Approach

The app uses different local models (sourced from Hugging Face) for different jobs instead of asking one model to do everything:

• Speech-to-text: Whisper via mlx-whisper locally and openai/whisper-small on Hugging Face Spaces.
• Topic and pattern detection: fine-tuned build-small-hackathon/interview-coach-3b (available on Hugging Face) to identify the interview question type and coarse pattern.
• Coaching hints: Qwen/Qwen2.5-3B-Instruct (from Hugging Face) generates short, question-specific pointers for the live coaching card.
• Transcript cleanup and Q&A extraction: the general LLM extracts structured questions and candidate answers from noisy transcripts.
• Evaluation: the evaluator uses the saved candidate answer, not the coaching hints, to provide a benchmark answer, hiring band, strengths, weaknesses, and critical gaps.

This keeps the live coaching path fast while allowing more careful reasoning for post-session extraction and evaluation.

Agentic Architecture

The app follows a LangGraph-style pipeline where each step has a focused responsibility:

Audio Capture -> Transcription -> Question Extraction -> Topic/Pattern Agent
              -> Coaching Card UI

Saved Transcript -> Q&A Extraction -> SQLite Persistence -> Evaluation Agent

At runtime, the live path prioritizes speed: it listens to system audio, updates the transcript, extracts the latest likely technical question, classifies the question type, and renders a coaching card. After the session, the slower processing path extracts all Q&A exchanges from the full transcript and stores them in SQLite for evaluation and CSV export.

Running Locally

Local-first interview coaching app with a Hugging Face Space demo mode.

On Hugging Face Spaces, the app uses browser microphone recording and a Linux-compatible Transformers Whisper backend. Local-only system audio capture via BlackHole and Ollama-based LLM calls are not available in Space mode.