Welcome to CS Interview Prep Platform
An intelligent interview preparation system that combines Retrieval-Augmented Generation (RAG), real-time audio processing, and adaptive learning to help you ace your computer science interviews.Quick Start
Get started in minutes with our step-by-step setup guide
Features
Explore resume analysis, mock interviews, and adaptive learning
What Makes This Platform Unique
This platform goes beyond traditional interview prep tools by implementing research-grade speech analysis and AI-powered personalization.Intelligent Resume Analysis
Upload your resume and get personalized interview questions based on your actual experience. The system uses FAISS vector indexing to understand your skills and project history, generating targeted questions that interviewers might ask about your specific background.Real-Time Audio Interview Simulation
Practice with live mock interviews that analyze both what you say and how you say it:- Speech-to-Text: Powered by AssemblyAI for streaming transcription with local Faster-Whisper fallback
- Voice Quality Analysis: Pitch stability, speaking rate (WPM), and pause patterns using librosa’s YIN algorithm
- Confidence Scoring: Detects voice tremors and hesitation through shimmer/jitter analysis
- Real-time Feedback: Live captioning shows your answers as you speak
Adaptive Learning System
The platform tracks your mastery across topics and automatically adjusts difficulty:- Topic Mastery Tracking: Monitor progress in DBMS, OOP, Operating Systems, and more
- Concept-Level Analytics: Identifies weak concepts and recommends focused practice
- Difficulty Adaptation: Questions get harder as you improve (or easier if you struggle)
- Personalized Action Plans: AI generates study plans based on your performance gaps
Comprehensive Knowledge Base
Built on a curated database of 300+ computer science questions covering:- Database Management Systems (15 subtopics, 185 questions)
- Object-Oriented Programming (8 subtopics, 200 questions)
- Operating Systems (10 subtopics, 100 questions)
All questions are automatically categorized by topic, subtopic, and difficulty level (Beginner/Intermediate/Advanced) using keyword-based classification.
Technology Stack
The platform combines multiple cutting-edge technologies:AI & Machine Learning
Mistral AI
Primary LLM for question generation, answer evaluation, and feedback
Sentence Transformers
all-MiniLM-L6-v2 model for semantic similarity and resume matching
FAISS
Facebook AI Similarity Search for fast vector retrieval
Faster-Whisper
Local speech-to-text engine for offline transcription
Backend Architecture
- Flask: Python web framework with WebSocket support (Flask-SocketIO)
- SQLAlchemy: ORM for user data, sessions, and mastery tracking
- Librosa: Digital signal processing for voice analysis
- NumPy: Efficient audio buffer processing and statistics
Frontend
- React.js: Interactive UI with real-time audio capture
- MediaRecorder API: Browser-native audio streaming
- Socket.IO Client: Bidirectional communication for live interviews
Hybrid Processing Architecture
The platform uses a sophisticated parallel processing approach during live interviews:Stream A: Signal Processing (Local)
Raw audio bytes are analyzed immediately on the server:- Volume detection (RMS)
- Pitch tracking (YIN algorithm)
- Pause ratio calculation
- Speaking rate (WPM)
Stream B: Semantic Processing (External)
The same audio is sent to AssemblyAI for:- Real-time transcription
- Live captioning
- Text-based answer analysis
Final Synthesis
When you finish answering, both streams combine:- Speech metrics (confidence, clarity, pace)
- Transcript content (technical accuracy, completeness)
- AI-generated feedback highlighting both delivery and content quality
This dual-stream approach allows the platform to provide feedback on communication skills (how you present) and technical knowledge (what you know) simultaneously.
Key Metrics Tracked
The system calculates research-grade metrics during each interview:| Metric | Description | Technology |
|---|---|---|
| WPM | Words per minute during speech | Transcript timing analysis |
| Pause Ratio | Percentage of silence vs. speaking time | Voice Activity Detection |
| Pitch Stability | Coefficient of variation in voice frequency | Librosa YIN + Welford’s Algorithm |
| Semantic Similarity | Answer relevance to ideal response | Sentence Transformers cosine similarity |
| Keyword Coverage | Percentage of required technical terms mentioned | Keyword matching |
| Confidence Score | Voice steadiness (shimmer/jitter) | Amplitude perturbation analysis |
RAG Pipeline
Every technical question uses Retrieval-Augmented Generation:- Ingestion: User uploads resume (PDF/DOCX)
- Extraction: PyPDF2/python-docx extracts text
- Vectorization: SentenceTransformer converts text to embeddings
- Indexing: FAISS creates searchable vector index
- Retrieval: When generating questions, the system queries your specific skills
- Generation: Mistral AI creates personalized questions based on your background
Ready to Start?
Installation Guide
Set up the platform locally in under 10 minutes
Feature Deep Dive
Learn about resume analysis, mock interviews, and more