How danishbasha/communication-readiness-platform works
This is an institutional communication-readiness platform for college placement preparation. Students use the frontend to practice voice interviews and listening comprehension, receive diagnostic feedback, and track syllabus checklist completion; mentors verify checklist items, while administrators and trainers manage cohorts and readiness. The backend provides institutional APIs and session/evaluation services, with a Python AI service for interview and learning workflows. The README documents additional capabilities and role workflows; some backend and AI internals are unsampled, so their links are omitted unless explicitly evidenced.
Main entry points: App.tsx Application Router · main.py AI Service API · checklist.routes.ts Placement Checklist · MockInterviewRoom.tsx Voice Interview Room
Components 23
Student Experience
- Application Router
frontend/src/App.tsx - Application State
frontend/src/context/AppContext.tsx - Student Dashboard
frontend/src/components/student/StudentDashboard.tsx - Voice Interview Room
frontend/src/components/student/MockInterviewRoom.tsx - Listening Room
frontend/src/components/student/ListeningRoom.tsx - Diagnostic Scorecard
frontend/src/components/student/DiagnosticReportView.tsx - Client API
frontend/src/services/api.ts
Institutional Workflows
- Stakeholder Portals
frontend/src/components/portals - Cohort Data
frontend/src/data/mockData.ts
Platform Backend
- Assessment Sessions
backend/src/modules/sessions/sessions.routes.ts - Placement Checklist
backend/src/modules/checklist/checklist.routes.ts - Placement Eligibility
backend/src/modules/placement/eligibility.service.ts - Mentor Verification
backend/src/modules/verifications/verifications.routes.ts - Live Interview Engine
backend/src/services/llmEvaluationService.ts - Platform Database
backend/src/shared/db/pool.ts
AI Services
- AI Service API
ai-service/app/main.py - Interview Workflows
ai-service/app/routers/interview.py - Learning Workflows
ai-service/app/routers/learning.py - Agent Workflows
ai-service/app/routers/agent.py - LLM Client
ai-service/app/services/llm_client.py
Other components
- Student
- Institutional Staff
- LLM Provider
Connections 24
- Student → Application Router: uses
README.md - Application Router → Student Dashboard: renders
frontend/src/App.tsx - Application Router → Voice Interview Room: renders
frontend/src/App.tsx - Application Router → Listening Room: renders
frontend/src/App.tsx - Application Router → Diagnostic Scorecard: renders
frontend/src/App.tsx - Application Router → Stakeholder Portals: renders
frontend/src/App.tsx - Application State → Client API: calls
frontend/src/context/AppContext.tsx - Application State → Cohort Data: loads
frontend/src/context/AppContext.tsx - Institutional Staff → Stakeholder Portals: uses
README.md - Student → Voice Interview Room: practices in
README.md - Student → Listening Room: takes test
README.md - Voice Interview Room → Diagnostic Scorecard: produces
README.md - Student → Placement Checklist: completes
README.md - Institutional Staff → Mentor Verification: verifies
README.md - Assessment Sessions → Platform Database: reads/writes
backend/src/modules/sessions/sessions.routes.ts - Placement Checklist → Platform Database: reads/writes
backend/src/modules/checklist/checklist.routes.ts - Placement Checklist → Placement Eligibility: checks
backend/src/modules/checklist/checklist.routes.ts - Mentor Verification → Placement Eligibility: checks
backend/src/modules/verifications/verifications.routes.ts - Placement Eligibility → Platform Database: queries
backend/src/modules/placement/eligibility.service.ts - Live Interview Engine → AI Service API: requests scoring
backend/src/services/llmEvaluationService.ts - AI Service API → Interview Workflows: registers
ai-service/app/main.py - AI Service API → Learning Workflows: registers
ai-service/app/main.py - AI Service API → Agent Workflows: registers
ai-service/app/main.py - LLM Client → LLM Provider: delegates to
ai-service/app/services/llm_client.py