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📅 Steps AI Hackathon 2026 - Phase-wise Roadmap

This file tracks the status, goals, and technological stacks utilized across each implementation phase of the AI Mock Interview Platform (Groq & SSE Stream Stack).

🗺️ Phase Summary

Phase 4 Description Status Target Date / Time
Phase 1 Project Setup & Architecture Scaffolding ✅ COMPLETED Day 1
Phase 2 Resume Ingestion & Groq JSON Profiling ✅ COMPLETED Day 2
Phase 3 Conversational Mock Interview Engine with SSE Stream ✅ COMPLETED Day 3-4
Phase 4 Answer Evaluation & PDF Report Export ✅ COMPLETED Day 5
Phase 5 Next.js Premium SPA, Loguru Interceptors & Containerization ✅ COMPLETED Day 6
Phase 6 Final Demos, walk-throughs & GitHub Release ✅ COMPLETED Day 7

🔍 Phase Details

Phase 1 — Project Setup & Architecture (Completed)

  • Status: ✅ Completed
  • Goals Achieved:
    • Established a clean, professional, enterprise-grade Python backend layout.
    • Setup core settings management using pydantic-settings to handle environmental inputs (GROQ_API_KEY).
    • Implemented a REST controller /health to verify system health, runtime parameters, and config bindings.
    • Delivered initial scaffolding required for day-one evaluator visibility (.env.example, requirements.txt, .gitignore, README.md, PHASES.md).
  • Tech Stack: Python 3.11, FastAPI, Pydantic Settings, Pytest.
  • Services / Libraries: Uvicorn.

Phase 2 — Resume Parsing & Intelligence (Completed)

  • Status: ✅ Completed
  • Goals Achieved:
    • Enabled PDF uploads using PyMuPDF (fitz) and Word DOCX uploads using python-docx.
    • Implemented standard LLMService leveraging Groq's JSON Mode (response_format={"type": "json_object"}) to query llama-3.3-70b-versatile deterministically.
    • Cached parsed profiles in a thread-safe InMemoryResumeStore dictionary mapped to a unique session_id.
    • Created GET /api/v1/resume/{session_id} route to easily fetch previously parsed profiles.
    • Wrote automated mock testing suite inside app/tests/test_resume.py covering uploads, extraction layers, and cache retrievals.
  • Tech Stack: FastAPI, PyMuPDF, python-docx, Groq Llama 3.3.
  • Services / Libraries: official groq SDK.

Phase 3 — Interview Session Engine (Completed)

  • Status: ✅ Completed
  • Goals Achieved:
    • Developed thread-safe InMemoryInterviewStore capturing active conversational states, modes, difficulty, and message history logs.
    • Built POST /api/v1/interview/start fetching resume contexts, auto-detecting seniority tiers (Junior/Mid/Senior), and returning opening question Q1.
    • Configured 3 conversational focus modes (HR, Technical, Behavioral) driving dynamic prompt syllabi templates.
    • Built POST /api/v1/interview/answer progressing conversation and returning recruiter responses via Server-Sent Events (SSE) StreamingResponse (text/event-stream).
    • Drafted comprehensive mock testing suite in app/tests/test_interview.py asserting conversational states, SSE text chunks concatenation, and boundary limits.
  • Tech Stack: FastAPI, Groq llama-3.3-70b-versatile, SSE Streaming.
  • Services / Libraries: official groq SDK.

Phase 4 — Answer Evaluation & Feedback Report (Completed)

  • Status: ✅ Completed
  • Goals Achieved:
    • Formulated Pydantic schemas mapping multi-dimensional scoring rubrics (clarity, depth, relevance, confidence out of 10) in app/models/evaluation.py.
    • Implemented Groq JSON Mode evaluator service analyzing chronological transcript dialogue QA pairs, generating overall grades, strengths/weaknesses lists, and comprehensive recruiter coaching summaries.
    • Rendered a premium branded PDF using fpdf2 utilizing brand dark-blue banners, dynamic colored banners matching performance, side-by-side strengths/weaknesses splits, and detailed question breakdowns.
    • Bound POST /api/v1/interview/end compiling results and GET /api/v1/report/{session_id} download controllers.
    • Wrote automated mock testing suite inside app/tests/test_evaluation.py which passes successfully.
  • Tech Stack: FastAPI, Groq llama-3.3, fpdf2.
  • Services / Libraries: fpdf2.

Phase 5 — End-to-End Integration & Polish (Completed)

  • Status: ✅ Completed
  • Goals Achieved:
    • Implemented and verified the full end-to-end mock interview flow.
    • Setup unified Loguru logging that hooks into Uvicorn, FastAPI, and standard Python libraries with rotation ("10 MB") and zip-compression backups.
    • Created a robust, modularized, and fast Next.js 14+ SPA Frontend integrating GSAP micro-animations and custom dark glassmorphic styling.
    • Formulated discrete production Dockerfiles for both backend and frontend layers, bound and orchestrated under a unified docker-compose.yml config.
  • Tech Stack: Loguru, Docker, Next.js, Tailwind CSS, GSAP.
  • Services / Libraries: loguru, docker compose, next, gsap.

Phase 6 — Documentation & Submission (Completed)

  • Status: ✅ Completed
  • Goals Achieved:
    • Outlined a standard 3-minute demo video script to capture resume parsing, real-time streaming, scoring, and PDF report downloads under SUBMISSION_GUIDE.md.
    • Configured and pushed final documentation schemas including Markdown badges, architectural diagrams, Docker run guides, and automated tests instructions under README.md.
    • Drafted complete, production-ready Release notes for GitHub v1.0.0 tagging workflow.
  • Tech Stack: Documentation, Video Structuring, Release Planning.
  • Services / Libraries: GitHub Releases.