MockMate

Interview practice, without the nerves.

MockMate is an AI-powered mock interview platform that conducts real, adaptive interview sessions using voice, vision, and résumé-personalized question generation. It analyzes not just what you say but how you say it — scoring your tone, posture, vocabulary, and confidence in real time. At the end of every session, you receive a detailed feedback report and a mock hiring decision letter, so you walk into every real interview already knowing how it ends.

Live Link: getmockmate.com

This project was built for the Gemini Live Agent Challenge hackathon — category: Live Agents 🗣️

🎯 The Problem

Job interviews are high-stakes and almost impossible to practice realistically. Candidates rehearse alone in mirrors or pay hundreds for coaching they can only afford once. Existing AI platforms are text-based, generic, or only evaluate after the session. None simulate the real emotional dynamics of a live interview — the pressure, the follow-ups, the silence. None of them see you. And none tell you honestly whether you would have gotten the job.

💡 The Solution

MockMate is a real-time AI interview coach. Upload your résumé → pick an interviewer persona → sit down and talk. MockMate interviews you live with voice, watches your body language through your webcam, and at the end delivers a full multimodal feedback report with a mock hiring decision letter.

Upload Résumé  →  Pick Persona & Difficulty  →  Live Voice Interview  →  Get Feedback + Decision Letter

✨ Key Features

Feature	Description
📄 Résumé-Aware Questions	Reads your actual résumé and generates hyper-personalized questions. Claim you led a team of 30? Expect to be asked how you handled underperformance.
🎭 13 Interviewer Personas	From a warm HR manager to an aggressive investment banker, an algorithm guru to a system designer — each with distinct questioning styles, pressure levels, speech patterns, and follow-up behaviors.
⚡ Adaptive Follow-ups	The interviewer asks probing follow-ups, challenges weak answers, and digs deeper into your claims — just like a real interviewer would.
👁️ Posture & Presence Vision	Webcam-based scoring of posture, eye contact, and facial confidence in real time.
🎙️ Live Native Audio	Real-time bidirectional voice interview through the Gemini Live API — not text-to-speech, but native audio generation with natural intonation, interruption support, and persona-specific accents.
📬 Mock Hiring Decision	A simulated offer or rejection letter with personalized reasoning — making feedback feel consequential.
📈 Skill Progression Dashboard	Tracks improvement across communication, confidence, structure, technical depth, and domain vocabulary over time.
🪧 AI Performance Card	After every session, Imagen 4.0 generates a unique artistic background themed to your persona, role, and score. The card displays your result, a motivational quote from Gemini, and can be downloaded or shared to LinkedIn.
🧭 Next Interview Recommender	Gemini 2.5 Flash Lite analyzes your recent sessions to surface your weakest dimension, then recommends a specific persona, job role, and practice focus for your next session — with a one-click CTA to start.
�🌗 Dark Mode	Full dark/light/system theme support across the entire application.

🎬 Demo Video

A 4-minute walkthrough explaining what MockMate is, why it is different from other mock interview platforms, and demonstrating it in action — from résumé upload through a live voice interview to the final feedback report and hiring decision.

🏗 Architecture

System Architecture Diagram

---
config:
  layout: elk
  look: neo
---
flowchart LR
 subgraph subGraph0["Frontend — Next.js on Vercel"]
        UI["Web App"]
        AW["AudioWorklet<br>PCM Capture"]
        WC["Webcam<br>Frame Capture"]
  end
 subgraph subGraph1["Backend — FastAPI on <b><i>Cloud Run</i></b>"]
        API["REST API"]
        WSA["WebSocket<br>Audio Stream"]
        WSV["WebSocket<br>Vision Stream"]
        RP["Resume Parser<br>Agent"]
        QG["Question Generator<br>Agent"]
        IE["Interview Engine<br>Agent"]
        PA["Posture Analyzer<br>Agent"]
        FC["Feedback Compiler<br>Agent"]
        PC["Performance Card<br>Agent"]
        NR["Next Interview<br>Recommender Agent"]
        IA["Avatar Generator<br>Agent"]
  end
 subgraph subGraph2["Vertex AI — Models"]
        GLA["Gemini Live API<br>Native Audio"]
        GFL["Gemini 2.5<br>Flash Lite"]
        IG["Imagen 4.0 Fast"]
  end
 subgraph subGraph3["Google Cloud — Infrastructure"]
        FS[("Cloud Firestore")]
        GCS[("Cloud Storage")]
        PG[("Cloud SQL<br>PostgreSQL")]
  end
 subgraph subGraph4["Auth"]
        GA["Google OAuth"]
  end
    UI -- REST --> API
    AW -- PCM audio --> WSA
    WC -- JPEG frames --> WSV
    API --> RP & QG & FC & PC & NR & IA
    WSA --> IE
    WSV --> PA
    RP --> GFL & GCS & FS
    QG --> GFL
    IE --> GLA & FS
    PA --> GFL & FS
    FC --> GFL & FS
    PC --> GFL & IG & GCS & FS
    NR --> GFL & FS
    IA --> IG & GCS
    UI --> GA
    GA --> PG

Agent Pipeline

The backend is composed of 8 specialized AI agents, each handling a distinct part of the interview workflow:

Agent	Model Used	What It Does
ResumeParserAgent	Gemini 2.5 Flash Lite	Extracts structured JSON from PDF/DOCX résumés, stores raw files in Cloud Storage, persists structured data in Firestore
QuestionGeneratorAgent	Gemini 2.5 Flash Lite	Generates 8 personalized interview questions based on the candidate's résumé, chosen persona, and difficulty level
InterviewEngineAgent	Gemini Live API (native audio)	Manages the live bidirectional voice interview session — handles real-time audio streaming, adaptive follow-ups, interruption support, and transcript persistence
PostureAnalyzerAgent	Gemini 2.5 Flash Lite	Scores posture, eye contact, and facial confidence from webcam frames captured every 20 seconds
FeedbackCompilerAgent	Gemini 2.5 Flash Lite	Aggregates transcript, posture data, and session metadata to produce a detailed feedback report with scores across 6 dimensions and a mock hiring decision letter
PerformanceCardAgent	Gemini 2.5 Flash Lite + Imagen 4.0 Fast	Generates a unique AI performance card per session — Imagen creates a themed artistic background, Gemini writes a motivational quote. Cards are cached in GCS + Firestore.
NextInterviewRecommenderAgent	Gemini 2.5 Flash Lite	Analyzes recent feedback sessions to identify the weakest skill dimension and recommends a targeted persona, job role, and practice focus for the user's next interview
InterviewerAvatarAgent	Imagen 4.0 Fast	Generates and caches AI profile pictures for each interviewer persona

sequenceDiagram
    actor User
    participant FE as Frontend
    participant BE as Backend (Cloud Run)
    participant Gemini as Gemini Models (Vertex AI)

    User->>FE: Upload résumé
    FE->>BE: POST /resume/upload
    BE->>Gemini: Parse résumé (Gemini 2.5 Flash Lite)
    BE-->>FE: Structured résumé data

    User->>FE: Pick persona + difficulty
    FE->>BE: POST /session/start
    BE->>Gemini: Generate questions (Gemini 2.5 Flash Lite)
    BE-->>FE: Session ID + questions

    User->>FE: Start interview
    FE->>BE: WebSocket /ws/interview/{id}
    loop Real-time voice conversation
        FE->>BE: PCM audio chunks (16 kHz)
        BE->>Gemini: Gemini Live API (native audio)
        Gemini-->>BE: Interviewer audio + transcription
        BE-->>FE: Audio response (24 kHz) + transcript
    end

    par Posture analysis (parallel)
        FE->>BE: JPEG frames (every 30s)
        BE->>Gemini: Posture analysis
    end

    User->>FE: End interview
    FE->>BE: POST /feedback/generate
    BE->>Gemini: Compile feedback (Gemini 2.5 Flash Lite)
    BE-->>FE: Scores + decision letter

    BE->>Gemini: Generate performance card (Imagen 4.0 + Gemini 2.5 Flash Lite)
    BE-->>FE: Performance card metadata + image

    FE->>BE: GET /analytics/next-interview/{user_id}
    BE->>Gemini: Recommend next interview (Gemini 2.5 Flash Lite)
    BE-->>FE: Recommendation (persona, role, focus)

🛠 Technologies Used

Gemini Models & Google AI

Technology	Usage in MockMate
Gemini Live API (native audio)	Powers the real-time bidirectional voice interview — the core feature. Handles natural speech, interruptions, follow-ups, and persona-specific accents/intonation.
Gemini 2.5 Flash Lite	Used by 4 agents: résumé parsing (structured JSON extraction), question generation (personalized to résumé + persona), feedback compilation (multi-source aggregation into scored report) and posture analysis (vision-based scoring of posture, eye contact, and facial confidence from webcam frames).
Imagen 4.0 Fast	Generates unique AI profile pictures for interviewer personas and artistic performance card backgrounds — each themed to the persona, job role, and score.
Google ADK (Agent Development Kit)	Orchestrates the InterviewEngineAgent — manages live sessions, request queues, and streaming to/from the Gemini Live API.
Vertex AI	All Gemini and Imagen model calls are routed through Vertex AI endpoints.

Google Cloud Services

Service	How MockMate Uses It
Cloud Run	Hosts the FastAPI backend as a serverless container. Handles auto-scaling, HTTPS termination, and WebSocket upgrades for live interviews.
Cloud Firestore	Primary database for all application data — sessions, transcripts, parsed résumés, feedback reports, and posture scores.
Cloud Storage (GCS)	Stores raw résumé files (PDF/DOCX) and generated interviewer avatar images.
Cloud SQL (PostgreSQL)	Stores user authentication data (Better Auth sessions) and skill progression analytics.

Application Stack

Layer	Technology
Frontend	Next.js 16 (App Router, Turbopack), React 19, TailwindCSS 4, shadcn/ui (Radix), TanStack Query
Backend	FastAPI, Python 3.13, WebSockets, Uvicorn
Auth	Better Auth with Google OAuth → Cloud SQL (PostgreSQL)
Real-time Audio	Browser AudioWorklet (PCM Int16 @ 16 kHz capture, 24 kHz playback)
Real-time Video	react-webcam (640×480 JPEG frames every 20 seconds)
Deployment	Cloud Run (backend), Vercel (frontend)

🚀 Quick Start (Local Setup)

Prerequisites

Tool	Version	Why	Install
Python	3.13	Backend runtime. Pre-built wheels for all deps.	python.org/downloads
Node.js	18+	Frontend runtime	nodejs.org
Google Cloud SDK	latest	Auth + deploy	cloud.google.com/sdk
PostgreSQL	14+	Better Auth session storage	pgadmin.org

You also need a Google Cloud project with the following APIs enabled:

Vertex AI API
Cloud Firestore API
Cloud Storage API

And a Google OAuth 2.0 Client ID (for user login).

Step 1 — Clone the repository

git clone https://github.com/mockmate-app/mockmate.git
cd mockmate

Step 2 — Provision Google Cloud resources first

Before creating .env files, provision the required GCP resources and APIs:

Enable APIs: Vertex AI API, Cloud Firestore API, Cloud Storage API, Cloud SQL Admin API
Create a Firestore database (Native mode)
Create a Cloud SQL for PostgreSQL instance (for app/auth data)
Create a Cloud Storage bucket (for résumé files and generated assets)

After provisioning, populate backend/frontend .env files with these resource names (project ID, bucket, region, OAuth creds, Cloud SQL host/db/user/password).

Step 3 — Set up the backend

cd backend

# Create and activate a virtual environment
python3.13 -m venv .venv
source .venv/bin/activate        # macOS / Linux
# .venv\Scripts\activate         # Windows

# Install dependencies
pip install --upgrade pip
pip install -r requirements.txt

# Configure environment variables
cp .env.example .env
# Open .env and fill in your GCP project ID, region, bucket name,
# Postgres credentials, and other values. See .env.example for guidance.

# Authenticate with Google Cloud
gcloud auth application-default login
gcloud config set project YOUR_PROJECT_ID
gcloud auth application-default set-quota-project YOUR_PROJECT_ID

# Start the server
python main.py
# Backend is now running at http://localhost:8080
# Interactive API docs at http://localhost:8080/docs

Step 4 — Set up the frontend

cd frontend

# Install dependencies
npm install

# Configure environment variables
cp .env.example .env.local
# Open .env.local and fill in:
#   NEXT_PUBLIC_API_URL=http://localhost:8080
#   BETTER_AUTH_SECRET, BETTER_AUTH_URL
#   GOOGLE_CLIENT_ID, GOOGLE_CLIENT_SECRET
#   PGHOST, PGPORT, PGUSER, PGPASSWORD, PGDATABASE

# Start the dev server
npm run dev
# Frontend is now running at http://localhost:3000

Step 5 — Use MockMate

Open http://localhost:3000 in your browser
Sign in with Google
Upload your résumé (PDF or DOCX)
Choose an interviewer persona and difficulty level
Start the live voice interview — speak naturally through your microphone
When the interview ends, view your feedback report and mock hiring decision

For more detailed setup instructions, see the backend README and frontend README.

📂 Project Structure

mockmate/
├── README.md                           # This file
├── LICENSE                             # MIT License
│
├── backend/                            # FastAPI backend (Python)
│   ├── main.py                         # REST + WebSocket endpoints, lifespan
│   ├── Dockerfile                      # Multi-stage production container
│   ├── requirements.txt                # Python dependencies
│   ├── deploy.sh                       # Backend deployment script (Cloud Run)
│   ├── .env.example                    # Environment variable template
│   └── agents/
│       ├── config.py                   # Shared configuration & env var loading
│       ├── resume_parser.py            # Résumé extraction with Gemini 2.5 Flash Lite
│       ├── question_generator.py       # Personalized question generation
│       ├── interview_engine.py         # Live audio interview via Gemini Live API
│       ├── posture_analyzer.py         # Webcam posture scoring via Gemini 2.5 Flash Lite
│       ├── feedback_compiler.py        # Post-session feedback & decision letter
│       ├── performance_card.py         # AI performance card (Imagen 4.0 + Gemini)
│       ├── next_interview_recommender.py # Next interview recommendation engine
│       ├── interviewer_avatar.py       # AI avatar generation with Imagen 4.0
│       └── personas.json              # 13 interviewer persona definitions
│
├── frontend/                           # Next.js web app (TypeScript)
│   ├── deploy.sh                       # Frontend deployment script (Vercel)
│   ├── src/
│   │   ├── app/                        # Pages (App Router)
│   │   │   ├── dashboard/              # Session history, stats, performance card, next-interview recommendation
│   │   │   ├── interview/
│   │   │   │   ├── setup/              # Persona & difficulty selection
│   │   │   │   ├── live/               # Real-time voice interview engine
│   │   │   │   └── feedback/           # Feedback report + full-size performance card
│   │   │   ├── resume/                 # Résumé upload & preview
│   │   │   ├── sessions/               # Full session history
│   │   │   └── login/                  # Google OAuth login
│   │   ├── components/                 # Reusable UI components (shadcn/ui, PerformanceCard)
│   │   ├── constants/common.ts         # Shared constants & helpers
│   │   └── lib/                        # API client, auth, utilities
│   ├── public/
│   │   └── audio-processor.worklet.js  # PCM audio capture worklet
│   ├── package.json
│   └── next.config.ts
│
└── deploy.sh                           # Full-stack deployment orchestrator

☁️ Cloud Deployment

Backend → Google Cloud Run

The backend is containerized with a multi-stage Dockerfile and deployed on Cloud Run:

# Build container image using Cloud Build
gcloud builds submit --tag gcr.io/YOUR_PROJECT/mockmate-backend ./backend

# Deploy to Cloud Run (reads env vars from backend/cloudrun-env.yaml)
gcloud run deploy mockmate-backend \
  --image gcr.io/YOUR_PROJECT/mockmate-backend \
  --platform managed \
  --region us-central1 \
  --memory 2Gi \
  --timeout 3600 \
  --allow-unauthenticated \
  --env-vars-file backend/cloudrun-env.yaml

Frontend → Vercel

The frontend is deployed on Vercel with environment variables configured in the Vercel dashboard:

cd frontend
npx vercel --prod

Set these environment variables in Vercel:

NEXT_PUBLIC_API_URL → your Cloud Run backend URL (e.g., https://mockmate-backend-xxxxx.run.app)
BETTER_AUTH_SECRET, BETTER_AUTH_URL, GOOGLE_CLIENT_ID, GOOGLE_CLIENT_SECRET
PostgreSQL connection variables (PGHOST, PGPORT, PGUSER, PGPASSWORD, PGDATABASE)

🚀 Automating Cloud Deployment

Both the backend and frontend have fully automated continuous deployments linked directly to the GitHub repository:

Service	Platform	Trigger	How
Backend	Google Cloud Run	Push to `main` (changes in `backend/`)	Cloud Run continuous deployment connected to GitHub via the GCP Console
Frontend	Vercel	Push to `main` (changes in `frontend/`)	Vercel Git integration connected to the GitHub repository

Every push to main automatically builds and deploys the affected service — no manual steps required.

Google Cloud and Vercel integration in Github:

Deployment Scripts

For local or manual deployments, each service also has its own deployment script:

# Deploy backend only (Cloud Run)
chmod +x backend/deploy.sh
./backend/deploy.sh

# Deploy frontend only (Vercel)
chmod +x frontend/deploy.sh
./frontend/deploy.sh

# Deploy both at once from repo root
chmod +x deploy.sh
./deploy.sh                 # deploys backend + frontend
./deploy.sh backend         # backend only
./deploy.sh frontend        # frontend only

Note: The backend deploy.sh script parses backend/.env and passes every key-value pair to Cloud Run via --set-env-vars. Make sure your .env file is populated before deploying.

✅ Proof of Google Cloud Deployment

MockMate's backend runs entirely on Google Cloud. Here is the proof:

Proof.of.Google.Cloud.deployment.mp4

Code-level proof of Google Cloud service usage:

GCP Service	Code Pointer	What It Does
Vertex AI + Gemini Live API	`interview_engine.py#L696`	`vertexai.init()` initialises Vertex AI for the live interview session
Gemini Live API (ADK)	`interview_engine.py#L1269-L1327`	Creates `Agent()`, `Runner()`, and `LiveRequestQueue()` for real-time bidirectional audio streaming
Gemini 2.5 Flash Lite (GenAI SDK)	`feedback_compiler.py#L199-L200`	`genai.Client(vertexai=True)` — routes Gemini calls through Vertex AI for feedback compilation
Cloud Firestore	`interview_engine.py#L697`	`firestore.AsyncClient()` for session state, transcripts, and posture scores
Cloud Firestore	`config.py#L28-L33`	Centralised Firestore collection config used by all 6 agents
Cloud Storage	`resume_parser.py#L165`	`storage.Client()` for uploading raw résumé files to GCS
Cloud Storage	`resume_parser.py#L265-L279`	`_upload_to_gcs()` — uploads file bytes and returns `gs://` URI
Cloud Pub/Sub	`interview_engine.py#L698-L699`	`pubsub_v1.PublisherClient()` for session-end event publishing
Cloud Pub/Sub	`interview_engine.py#L913`	`self._publisher.publish()` fires session-end event
Imagen 4.0 (Vertex AI)	`interviewer_avatar.py#L209-L215`	`genai.Client(vertexai=True)` + `storage.Client()` for avatar generation and caching
Imagen 4.0 (Vertex AI)	`interviewer_avatar.py#L267-L277`	`generate_images()` call to Imagen model
Cloud Run	`Dockerfile`	Multi-stage production container for Cloud Run deployment
Cloud Run (deploy)	`deploy.sh`	Automated deployment script for Cloud Run with env var injection

🎬 How It Works — User Flow

Sign in — Log in with your Google account (OAuth 2.0 via Better Auth).
Upload résumé — Drag and drop your PDF or DOCX. Gemini 2.5 Flash Lite parses it into structured data (skills, experience, education, bold claims).
Choose your interviewer — Pick from 13 personas (e.g., Startup Founder, Investment Banker, Algorithm Guru) and set your difficulty level (easy, medium, hard).
Live interview — A real-time voice conversation begins. The AI interviewer asks personalized questions, follows up on your answers, challenges weak points, and adapts its questioning style based on your responses. Your webcam captures posture data in the background.
Get feedback — After the interview ends, Gemini 2.5 Flash Lite compiles all data (transcript, posture scores, résumé context) into a detailed feedback report scoring you across 6 dimensions: communication, confidence, structure, technical depth, domain vocabulary, and posture.
Hiring decision — You receive a mock offer or rejection letter with specific reasoning, making the feedback feel real and consequential.
Performance card — An AI-generated card with a unique Imagen 4.0 artistic background, your score, and a motivational quote from Gemini. Download it or share it to LinkedIn.
Track progress — Your dashboard shows session history, score trends, skill progression, and a personalized "Your Next Interview" recommendation strip powered by Gemini.

graph TD
    A[🔐 Sign in with Google] --> B[📄 Upload Résumé]
    B --> C{Résumé Parsed?}
    C -->|Yes| D[🎭 Choose Persona & Difficulty]
    C -->|No| B
    D --> E[🎙️ Live Voice Interview]
    E --> F[👁️ Posture Analysis<br/>runs in parallel]
    E --> G[📝 Real-time Transcript]
    E --> H[Interview Ends]
    H --> I[📊 Feedback Report]
    I --> J[✅ Offer or ❌ Rejection Letter]
    I --> K[🎨 Performance Card]
    K --> L[📈 Dashboard Updated + Next Interview Recommendation]

🏆 What Makes MockMate Different

Most interview platforms evaluate what you say. MockMate evaluates who you are under pressure — your voice, your body language, your vocabulary, your ability to handle tough follow-ups.

It is the only platform that combines:

✅ Live voice interviewing — not text-based; a real conversation powered by Gemini Live API with native audio
✅ Real-time vision analysis — not post-session review; live posture/confidence scoring during the interview
✅ Résumé personalization — not generic questions; every question is grounded in your actual experience and claims
✅ Adaptive follow-ups — not predictable scripts; the AI digs deeper based on your answers
✅ Persona diversity — 13 distinct interviewer personalities with unique speech styles, accents, and pressure levels
✅ Consequential decisions — not vague suggestions; a real offer or rejection letter with specific reasoning

All in a single, seamless session.

📝 Findings & Learnings

Building MockMate taught me several things about working with Gemini and Google Cloud:

Gemini Live API is remarkably natural — The native audio mode produces speech that feels genuinely human. Personas with different accents and speech patterns (fast-talking startup founders vs. measured consultants) emerge naturally from the system prompt without any special TTS configuration.
Prompt engineering is the real product work — Getting each of the 13 personas to feel distinct required extensive prompt iteration. I added structured SPEECH STYLE blocks (tone, pace, warmth, filler words, energy) and ACCENT GUIDANCE (22 accent types) to make each interviewer feel like a real person.
AudioWorklet is essential for real-time audio — The Web Audio API's AudioWorklet runs on a separate thread, which is critical for capturing PCM audio at 16 kHz without drops while the main thread handles UI updates and transcript rendering.
Vision analysis adds genuine value — Even simple posture/eye-contact scoring from webcam frames makes feedback significantly more actionable. Candidates often don't realize they're looking away from the camera or slouching until they see the data.
The ADK simplifies agent orchestration — Google's Agent Development Kit handled session management, request queuing, and streaming to/from the Gemini Live API, letting me focus on the product logic rather than infrastructure plumbing.
Firestore's flexibility accelerated development — The schemaless nature of Firestore let me iterate on data structures (sessions, transcripts, feedback reports) without migration overhead. Each agent writes to its own collection, keeping concerns cleanly separated.

📢 Content & Community

Blog post: Medium blog post
Google Developer Group Profile: GDG profile

All content was created for the purposes of entering the Gemini Live Agent Challenge hackathon. #GeminiLiveAgentChallenge

📜 License

MIT

Name		Name	Last commit message	Last commit date
Latest commit History 86 Commits
.vscode		.vscode
backend		backend
frontend		frontend
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
deploy.sh		deploy.sh

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MockMate

Table of Contents

🎯 The Problem

💡 The Solution

✨ Key Features

🎬 Demo Video

🏗 Architecture

System Architecture Diagram

Agent Pipeline

🛠 Technologies Used

Gemini Models & Google AI

Google Cloud Services

Application Stack

🚀 Quick Start (Local Setup)

Prerequisites

Step 1 — Clone the repository

Step 2 — Provision Google Cloud resources first

Step 3 — Set up the backend

Step 4 — Set up the frontend

Step 5 — Use MockMate

📂 Project Structure

☁️ Cloud Deployment

Backend → Google Cloud Run

Frontend → Vercel

🚀 Automating Cloud Deployment

Deployment Scripts

✅ Proof of Google Cloud Deployment

🎬 How It Works — User Flow

🏆 What Makes MockMate Different

📝 Findings & Learnings

📢 Content & Community

📜 License

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