File size: 2,119 Bytes
1e43b82 5fe9776 1e43b82 5fe9776 ca59431 5ec9328 5fe9776 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | ---
title: Homework Validation System
emoji: 📚
colorFrom: blue
colorTo: purple
sdk: docker
pinned: false
---
<<<<<<< HEAD
---
title: Homework Validation System
sdk: docker
app_port: 7860
---
hello
=======
---
title: Homework Validation System
sdk: docker
app_port: 7860
---
# Homework Validation System (FastAPI)
A backend API that validates student homework by extracting text from teacher and student files, comparing answers, and generating remarks using rule-based logic and optional AI.
---
## Features
- Upload teacher and student homework files
- OCR support for images and scanned PDFs
- Text extraction from PDF and DOCX
- Similarity matching using TF-IDF + cosine similarity
- Optional AI-generated remarks (OpenAI / Gemini)
- FastAPI Swagger documentation
---
## Tech Stack
- FastAPI
- Python
- pytesseract
- Pillow
- pypdf / pdf2image
- python-docx
- scikit-learn
- OpenAI / Gemini (optional)
---
## Project Structure
---
homework_validation_system/
│
├── app.py
├── requirements.txt
├── artifacts/
├── uploads/
├── src/
│ ├── extractors.py
│ ├── similarity.py
│ ├── llm_client.py
│ └── utils.py
└── README.md
## Installation
### 1. Create Virtual Environment
python -m venv myenv
### 2. Install Requirements
pip install -r requirements.txt
## OCR Setup (Required)
### Install Tesseract OCR
This project uses **Tesseract OCR** for extracting text from images and scanned PDFs.
#### Windows
1. Download and install Tesseract OCR.
2. Default installation path:
3. Add this path in your code:
pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
### Run API
uvicorn app:app --reload --host 0.0.0.0 --port 8000
### Swagger UI:
http://localhost:8000/docs
### Example API Response
{
"student_id": 1,
"homework_id": 10,
"status": "Needs Review",
"match_percentage": 72,
"teacher_extracted_text": "...",
"student_extracted_text": "...",
"ai_generated_remark": "Good attempt but missing key points.",
"llm_used": true
}
>>>>>>> cdb5b148e5facdea1aec264a5b4d0b6293132b6e
|