MVS Analysis (TLP Result Finder)

A FastAPI-based web application that analyzes SRM TLP result PDFs and generates structured Excel reports with charts. It supports teacher-wise analysis, Faculty Advisor aggregation, overall multi-subject reporting, and low-attendance extraction.

Features

• Self Analysis: Teacher-wise extraction from one or more TLP PDFs.
• Faculty Advisor (FA) Analysis: Consolidated report grouped by test components (FT1, FT2, etc.).
• Overall Analysis: Single Excel workbook with one sheet per subject.
• Low Attendance Analysis: Finds students below 75% attendance (OCR-based) and exports Excel.
• PDF + OCR Processing: Native text extraction with OCR fallback for scanned PDFs.
• Excel Reports with Charts: Auto-formatted tables and summary charts.
• Modern Web UI: Tab-based workflow with light/dark mode.

Tech Stack

• Backend: FastAPI (Python)
• Frontend: HTML, CSS, JavaScript
• PDF Processing: pdfplumber
• OCR: OCR.Space API (optional for TLP PDFs; required for attendance PDFs)
• Excel Generation: XlsxWriter
• Data Processing: Pandas

Project Structure

• main.py — FastAPI app and API endpoints
• extractor.py — TLP PDF parsing and OCR fallback
• attendance_extractor.py — Low-attendance OCR extraction
• static/index.html — Web UI

Getting Started

1. Clone the repository:

git clone https://github.com/Vattsa-11/MVS-Analysis-.git
cd MVS-Analysis-

2. Install dependencies:

pip install -r requirements.txt

3. (Optional) Create a .env file for OCR fallback in TLP extraction:

OCR_API_KEY=your_api_key_here

4. Run the application:

uvicorn main:app --reload

5. Open the UI in your browser:

http://localhost:8000

Usage

• Self: Enter a teacher name, upload one or more TLP PDFs, and download the Excel report.
• FA: Enter FA name, add multiple faculty + subject codes, upload PDFs, and download a grouped report by test component.
• Overall: Upload all subject PDFs to generate a multi-sheet Excel workbook.
• Low Attendance: Upload attendance PDF to get a list of students with < 75% attendance.

API Endpoints

• POST /analyze — Teacher-wise TLP analysis
• POST /analyze_fa — Faculty Advisor consolidated analysis
• POST /analyze_overall — Overall multi-subject analysis
• POST /analyze_attendance — Low attendance extraction

Notes

• OCR is used automatically when PDFs are scanned or text extraction is weak.
• Attendance extraction relies on OCR and may require clean scans for best results.

License

MIT License