AI Mock Interviewer SaaS

A Django-based AI-powered SaaS platform for resume analysis, interview preparation, and user credit management. The application combines AI prompt pipelines, document parsing, audio transcription, payment integration, and optional anti-cheating support.

Overview

This repository contains a multi-app Django project named AI_mock_interviewer.

Key functionality:

• Resume analysis using AI and job descriptions
• Automated interview question generation from resume and JD
• Audio recording upload and AI evaluation of interview answers
• Wallet-based credit system for interview and resume credits
• Payment support using Razorpay and token/coupon management
• User authentication via Django Allauth and social login
• Optional webcam-based cheating detection pipeline

Core Apps

core

Handles the main home pages, dashboard, resume upload, and resume AI analysis.

• views.py — application entry points: home, dashboard, resume_analysis, and resume_result
• models.py — ResumeAnalysis model stores resume files, JD text, reports, and scores
• forms.py — ResumeForm for resume upload and JD input
• resume_analysis.py — AI resume evaluation workflow using langchain, groq, and custom prompts
• LLM_prompt.py — prompt templates for resume scoring, keyword extraction, JD generation, question generation, and answer evaluation
• consumers.py, detection.py, iris_calibration.py, routing.py — optional real-time cheating detection via webcam
• face_landmarker.task — Mediapipe model asset used by face detection

interview

Manages interview setup, question generation, answer submission, and result display.

• views.py — interview lifecycle: setup, rendering questions, saving audio transcripts, evaluating results, and showing reports
• models.py — Interview and QuestionAndAnswer models store interview metadata, questions, audio results, and AI evaluation JSON
• forms.py — InterviewForm for interview configuration
• question_and_answer_pipeline.py — question generation and batch answer evaluation using AI models

payment

Implements payment, wallet, coupon, vendor, and credit tracking workflows.

• models.py — Wallet, Transaction, Plan, Payment, Vendor, Coupon
• views.py — payment checkout, order processing, Razorpay verification, success pages, coupon generation, coupon redemption, and user wallet updates
• signals.py — automatically create a wallet for each new user with starter credits

Project Structure

Root files:

• manage.py — Django CLI entrypoint
• pyproject.toml — project dependencies and metadata
• README.md — project documentation
• db.sqlite3 — local development database file

Project package:

• AI_mock_interviewer/ — Django project configuration
  • settings.py — environment, installed apps, database config, authentication, and third-party keys
  • urls.py — route definitions
  • wsgi.py / asgi.py — web server interfaces

Apps:

• core/
• interview/
• payment/

Static and templates:

• static/ — CSS assets for pages like checkout, dashboard, interview, payment, resume analysis, etc.
• templates/ — HTML templates for the landing page, dashboard, interview flow, payment pages, user account flows, and partials like nav and aside

Important Features

Resume Analysis

• Upload a PDF resume and provide a job description
• Uses langchain, groq, and GPT-like models to compute:
  • resume score
  • keyword match percentage
  • missing skills
  • recruiter feedback
  • predictions for interview questions
• Stores the AI analysis output in JSON on ResumeAnalysis.report

Interview Preparation

• Users can create interviews by uploading a resume and JD, selecting experience and difficulty
• AI generates a set of interview questions tailored to resume skills and job requirements
• Interview answers are captured as uploaded audio files
• Uses audio transcription and evaluation through AI to compute question-level scores and feedback

Credit Wallet System

• Each user automatically receives a wallet on signup
• resume_credits and interview_credits control access to resume analysis and interview creation
• Credits are debited on use and credited on successful payment or coupon redemption

Payments and Coupons

• Razorpay integration for paid plans
• Plan objects define pricing and credit bundles
• Payment records track orders, verified payments, and linked plans
• Coupons may be generated by vendor users and redeemed by customers

Authentication

• Django built-in auth plus django-allauth
• Google social login provider configured
• Allauth middleware and authentication backends enabled

Optional Cheating Detection

• Websocket consumer for real-time analysis
• Mediapipe face landmarks and gaze/head movement detection
• Provides a reusable structure for online proctoring or user attention monitoring

Dependencies

Defined in pyproject.toml, including:

• django>=6.0.3
• django-allauth
• django-storages
• langchain-community, langchain-groq, langchain-huggingface, langchain-openai
• groq
• azure-storage-blob
• cashfree-pg
• razorpay
• mediapipe
• opencv-python, numpy
• sentence-transformers, scikit-learn
• python-dotenv
• psycopg[binary]

Environment Variables

The app uses .env values loaded in settings.py. Common required variables:

• DJANGO_SECRET_KEY
• ENVIRONMENT
• GROQ_API_KEY
• OPEN_ROUTER
• AI_CREDITS_API
• CASHFREE_CLIENT_ID
• CASHFREE_CLIENT_SECRET
• CASHFREE_ENV
• RAZORPAY_TEST_KEY
• RAZORPAY_TEST_SECRET
• RAZORPAY_LIVE_KEY
• RAZORPAY_LIVE_SECRET
• AZURE_ACCOUNT_NAME
• AZURE_ACCOUNT_KEY
• NEON_DB_URL (optional for production PostgreSQL)

Setup and Run

1. Create and activate a Python virtual environment.
2. Install dependencies:

   pip install -r requirements.txt

   If requirements.txt is not present, use pyproject.toml tooling or generate one from dependencies.
3. Add environment variables to .env.
4. Apply migrations:

   python manage.py migrate

5. Create a superuser and run the server:

   python manage.py createsuperuser
   python manage.py runserver

Key URLs

• / — landing page
• /dashboard/ — user dashboard
• /resume_analysis/ — resume analysis workflow
• /result/<analysis_id> — view resume analysis report
• /interview_setup/ — interview creation page
• /interview/<interview_id>/ — interview question session page
• /payment/checkout/<plan_id> — checkout page
• /payment/payment_success/<payment_id>/ — payment result page
• /accounts/ — authentication and social login routes

Notes

• The project is designed as a SaaS workflow with credit management and paid plan support.
• Some payment routes show a Cashfree flow that is currently commented out, while Razorpay is active.
• The AI model selection logic includes fallback to cheaper models if the primary model call fails.
• The app uses both local SQLite and optional remote database configuration.

Suggested Improvements

• Add a requirements.txt or Poetry lock file for reproducible installs
• Complete the optional Cheating detection UI and websocket activation
• Add tests for interview scoring, payment verification, and resume analysis flows
• Harden production settings, remove DEBUG=True, and configure ALLOWED_HOSTS
• Add documentation or comments for the AI prompt generation and expected JSON outputs

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This README describes the full SaaS app architecture, core modules, and major workflows for the AI mock interviewer platform.