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balaji958685/
attendance-compliance-agent

Name: attendance-compliance-agent
Availability: InStock

ml-intern

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Attendance Compliance Agent

A fully functional FastAPI-based attendance tracking system compliant with VFSTR R22 regulations. It handles session scheduling, attendance percentage calculations, On-Duty (OD) leave workflows, condonation processing, holiday calendar integration, and report generation in JSON/CSV/XLSX formats.

Quick Start

bash

pip install -r requirements.txt
uvicorn app.main:app --reload

Open http://localhost:8000/docs for interactive API documentation.

Project Structure

attendance_agent/
├── app/
│   ├── main.py                    # FastAPI entry point
│   ├── models.py                  # SQLAlchemy ORM models + SQLite
│   ├── attendance_calculator.py   # Attendance % logic with R22 rules
│   ├── timetable.py               # Slot & session management
│   ├── holidays.py                # Holiday calendar integration
│   ├── od_workflow.py             # Faculty→HOD OD approval chain
│   ├── condonation.py             # Condonation rules (65%/55% thresholds)
│   ├── report.py                  # JSON, CSV, XLSX report exports
│   └── routers/
│       ├── students.py            # Student CRUD
│       ├── subjects.py            # Subject CRUD
│       ├── timetable_routes.py    # Timetable & session endpoints
│       ├── attendance.py          # Mark attendance & calculate %
│       ├── od_routes.py           # OD request/approval APIs
│       ├── condonation_routes.py  # Condonation APIs
│       ├── holidays_routes.py     # Holiday APIs
│       └── reports_routes.py      # Report generation APIs
├── tests/
│   ├── test_full.py               # 27 comprehensive unit tests
│   └── integration_test.py        # End-to-end integration test
└── requirements.txt

Core Features

Attendance Calculation

Formula: Attended Sessions / Total Sessions × 100
Excludes holidays automatically
OD counts as present after approval
Handles Lecture (L), Tutorial (T), Practical (P) session types

VFSTR R22 Rules

Percentage	Status
≥ 75%	Eligible
65–74%	First condonation (with fee)
55–64%	Second condonation (if first approved)
< 55%	Not eligible / repeat course

Session Management

Timetable slots define weekly recurring schedules
Sessions auto-generated for dates matching slots
No duplicates generated
Holiday dates skip session creation

OD Workflow

Student Request → Faculty Review → HOD Approval → Attendance Updated

Report Types

Daily Attendance — per-session records
Subject-wise Summary — % per student per subject
Monthly Report — attendance trends
Defaulter Report — students below 75%
OD/Condonation Report — pending and processed requests

Export Formats

JSON (default)
CSV
XLSX (Excel)

Testing

bash

# Unit tests (27 tests)
pytest tests/test_full.py -v

# Integration test (requires server running)
python tests/integration_test.py

API Endpoints

Feature	Endpoint
Health	`GET /health`
Students	`POST /students/`, `GET /students/`
Subjects	`POST /subjects/`, `GET /subjects/`
Timetable Slots	`POST /timetable/slots`, `GET /timetable/slots`
Generate Sessions	`POST /timetable/generate-sessions/{date}`
Mark Attendance	`POST /attendance/mark`
Calculate %	`POST /attendance/calculate`
Defaulters	`GET /attendance/defaulters/{section}`
OD Request	`POST /od/`
OD Faculty Approval	`POST /od/{id}/approve-faculty`
OD HOD Approval	`POST /od/{id}/approve-hod`
Condonation	`POST /condonation/`, `POST /condonation/{id}/approve`
Holidays	`POST /holidays/`, `GET /holidays/`
Reports	`POST /reports/` (json/csv/xlsx)

Dependencies

FastAPI ≥ 0.110.0
SQLAlchemy ≥ 2.0.0
Pydantic ≥ 2.6.0
openpyxl ≥ 3.1.0
pytest ≥ 8.0.0

Generated by ML Intern

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Try ML Intern: https://smolagents-ml-intern.hf.space
Source code: https://github.com/huggingface/ml-intern

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "balaji958685/attendance-compliance-agent"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.