FirstRound is an AI-powered technical pre-screening platform built during the HrFlow.AI 2026 hackathon.

The product covers the full early interview loop:

• a recruiter creates a job from a raw job description
• HrFlow.ai parses and indexes the job
• the backend generates interview questions with a local LLM
• a recruiter shares a candidate link
• a candidate uploads a CV and completes a voice interview
• the recruiter reviews the transcript, answer-by-answer scoring, and CV/job matching

FirstRound has two user journeys.

Recruiter flow

1. Paste a job description.
2. Generate and validate interview questions.
3. Create a session and share a candidate link.
4. Review candidate profiles, matching score, transcript, and per-question evaluation.

Candidate flow

1. Open the shared session link.
2. Upload a CV as PDF.
3. Allow microphone access.
4. Complete the AI-led interview in the browser.

• Job parsing and indexing with HrFlow.ai
• Candidate CV upload and profile parsing with HrFlow.ai
• Interview question generation with a local Ollama model
• Real-time voice interview over WebSocket
• Speech-to-text with mlx-whisper
• Voice activity detection with silero-vad
• Text-to-speech with edge-tts
• Recruiter dashboard to inspect results

.
├── backend/
│   ├── app/
│   │   ├── api/v1/endpoints/   # REST and WebSocket endpoints
│   │   ├── core/               # app config and model loading
│   │   ├── dependencies/       # FastAPI dependencies
│   │   ├── schemas/            # Pydantic schemas
│   │   └── services/           # HrFlow, interview orchestration, AI services
│   ├── requirements.txt
│   └── scripts/
├── frontend/
│   ├── src/
│   │   ├── api/                # frontend API client
│   │   ├── components/
│   │   ├── pages/              # recruiter and candidate pages
│   │   └── styles/
│   ├── package.json
│   └── public/
└── docs/
    └── architecture.md

For the live interview, audio is streamed from the browser to the backend through a WebSocket pipeline:

Candidate microphone
  -> VAD (detect end of speech)
  -> STT (transcribe spoken answer)
  -> LLM (score answer, evaluate, decide next question or follow-up)
  -> TTS (synthesize spoken response)
  -> audio streamed back to the browser

The project currently stores interview sessions in memory in the FastAPI process. That means:

• sessions are lost when the backend restarts
• this setup is suitable for local demo/hackathon usage
• a database or Redis would be needed for production-grade persistence

You need the following installed locally:

• Python 3.12+
• Node.js 18+
• npm
• Ollama installed locally
• a working HrFlow.ai account
• a HrFlow.ai API key
• a HrFlow.ai board key for jobs
• a HrFlow.ai source key for profiles

git clone git@github.com:Tooom123/Hackathon_HrFlow.AI_2026-FirstRound.git firstround
cd firstround

The backend reads its configuration from backend/.env or .env at the repository root.

Create backend/.env with the following values:

API_KEY=your_hrflow_api_key
USER_EMAIL=your_hrflow_account_email
HRFLOW_BOARD_KEY=your_board_key
HRFLOW_SOURCE_KEY=your_source_key

FRONTEND_BASE_URL=http://localhost:5173

OLLAMA_BASE_URL=http://localhost:11434
OLLAMA_MODEL=llama3.2
OLLAMA_TEMPERATURE=0.3

WHISPER_MODEL=mlx-community/whisper-large-v3-turbo
WHISPER_LANGUAGE=fr

VAD_THRESHOLD=0.5
VAD_MIN_SILENCE_MS=700
VAD_MIN_SPEECH_MS=250
VAD_INPUT_SAMPLE_RATE=24000

TTS_VOICE=fr-FR-HenriNeural
TTS_SAMPLE_RATE=24000
TTS_CHUNK_SIZE=4096

The frontend optionally supports:

VITE_API_BASE=http://localhost:8000

If you do not define it, the frontend defaults to http://localhost:8000.

Make sure Ollama is installed and running, then pull the model used by the backend:

ollama pull llama3.2

If you want to use another local model, update OLLAMA_MODEL in backend/.env.

From the repository root:

cd backend
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
uvicorn app.main:app --reload

The backend will be available at:

• API root: http://localhost:8000
• healthcheck: http://localhost:8000/health
• Swagger UI: http://localhost:8000/docs

In another terminal:

cd frontend
npm install
npm run dev

The frontend will be available at:

• app: http://localhost:5173

Use this order to avoid most local issues:

1. Start Ollama.
2. Start the backend.
3. Check http://localhost:8000/health.
4. Start the frontend.
5. Open http://localhost:5173.

1. Open http://localhost:5173.
2. Create a new interview flow from a job description.
3. Review or edit the generated questions.
4. Generate a candidate session link.
5. Copy the candidate link and open it in another browser tab or window.

1. Open the session link.
2. Upload a PDF CV.
3. Allow microphone access in the browser.
4. Complete the voice interview.

After the candidate finishes, the recruiter side can inspect:

• parsed candidate profile
• job/profile matching score
• transcript of answers
• per-question score and evaluation
• overall session score

• GET /health: basic health check

• GET /hrflow/ping: test HrFlow.ai connectivity
• GET /hrflow/jobs: list indexed jobs
• POST /hrflow/jobs/parse: parse raw job text
• POST /hrflow/jobs/setup: parse, index, and generate interview questions
• PUT /hrflow/jobs/questions: save validated questions to job metadata
• POST /hrflow/jobs/{job_key}/scoring: score profiles against a job

• POST /profiles/apply?job_key=...: upload a candidate CV
• GET /profiles/{reference}/status: poll CV parsing status
• GET /profiles/job/{job_key}: list profiles attached to a job

• GET /interview/sessions?job_key=...: retrieve waiting session for a job
• POST /interview/sessions?job_key=...: create a session
• POST /interview/sessions/{session_id}/join?profile_reference=...: attach candidate to session
• GET /interview/sessions/{session_id}: get session status and results
• WS /interview/ws/{session_id}: live interview socket

• CORS is configured for http://localhost:5173.
• Candidate CV upload only accepts PDF files.
• Maximum CV file size is 10 MB.
• The browser must be allowed to access the microphone.
• The frontend expects the backend on port 8000 unless VITE_API_BASE is overridden.

Check:

• your virtual environment is activated
• dependencies were installed with pip install -r requirements.txt
• backend/.env exists and contains valid HrFlow.ai credentials

Check:

• API_KEY is valid
• USER_EMAIL matches the HrFlow.ai account
• HRFLOW_BOARD_KEY and HRFLOW_SOURCE_KEY point to existing resources
• GET /hrflow/ping returns a successful response

Check:

• Ollama is running locally
• the configured model exists locally
• OLLAMA_BASE_URL matches the local Ollama server

Useful command:

ollama list

Check:

• the backend is running
• the session exists and has not been lost after a backend restart
• the browser granted microphone access
• the candidate uploaded a valid PDF file

Check:

• browser microphone permissions
• local audio output
• WebSocket connectivity to ws://localhost:8000/interview/ws/<session_id>
• model loading logs in the backend terminal

This repository is currently optimized for demo/local usage rather than production. Important limits:

• no persistent storage for sessions
• no authentication layer
• no deployment configuration
• no job queue for long-running AI tasks
• no production hardening around retries, observability, or scaling

• Backend notes: backend/README.md
• Frontend notes: frontend/README.md
• Architecture notes: docs/architecture.md

If you just want the shortest path to a local demo:

# terminal 1
ollama pull llama3.2

# terminal 2
cd backend
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
uvicorn app.main:app --reload

# terminal 3
cd frontend
npm install
npm run dev

Then open http://localhost:5173.