metadata
title: Graduation Project-v1.2
emoji: 🎓
colorFrom: indigo
colorTo: blue
sdk: docker
app_port: 7860
pinned: false
🤖 AI-Powered Graduation Project Recommendation System
📌 Overview
This project implements an intelligent AI-powered recommendation and semantic similarity platform for graduation projects using:
- Natural Language Processing (NLP)
- Semantic Search
- Vector Embeddings
- Hybrid Ranking Systems
- Large Language Models (LLMs)
The system helps students:
- discover unique graduation project ideas
- avoid duplicate projects
- analyze originality
- generate intelligent project features
- receive context-aware recommendations through an AI chatbot
⚙️ System Pipeline
1️⃣ Data Preprocessing
- Text normalization
- Duplicate removal
- Smart content merging
- Technical keyword extraction
- Feature engineering
2️⃣ Feature Extraction
- KeyBERT-based keyword extraction
- Automatic technical term detection
- Semantic feature generation
3️⃣ Embedding Generation
- SentenceTransformer embeddings
- Normalized vector representations
- Semantic encoding of projects
4️⃣ Semantic Retrieval
- FAISS vector indexing
- Nearest-neighbor semantic search
- Fast project similarity lookup
5️⃣ Hybrid Ranking
The final ranking combines:
- Semantic similarity
- Feature similarity
- Coverage ratio
- Confidence estimation
- Originality analysis
6️⃣ AI Recommendation Engine
- Context-aware project generation
- Feature recommendation
- Novelty checking
- Conversational chatbot assistance
🧠 AI & NLP Technologies Used
🔹 Machine Learning & NLP
- SentenceTransformers
- KeyBERT
- Scikit-learn
- SciPy
- FAISS
🔹 LLM Integration
- Google Gemini API
- Ollama
- Mistral
🔹 Backend & Infrastructure
- FastAPI
- Pandas
- NumPy
- Python
🏗️ Project Architecture
User Query
↓
Intent Classification
↓
Context Builder
↓
Feature Extraction
↓
Embedding Generation
↓
FAISS Semantic Search
↓
Hybrid Ranking Engine
↓
Originality & Duplicate Analysis
↓
AI Recommendation Response
🔍 Similarity Engine Workflow
Raw Dataset
↓
Preprocessing
↓
Feature Extraction
↓
Sentence Embeddings
↓
FAISS Indexing
↓
Semantic Retrieval
↓
Feature Similarity Matching
↓
Hybrid Re-ranking
↓
Final Recommendation
🚀 Features
✅ AI Chatbot
- Context-aware conversations
- Intent classification
- Domain-specific recommendations
- Memory-aware responses
✅ Semantic Similarity Search
- Embedding-based retrieval
- Semantic duplicate detection
- Vector search with FAISS
✅ Hybrid Recommendation System
- Multi-stage ranking pipeline
- Feature-level semantic comparison
- Adaptive scoring strategy
✅ Originality Detection
- Duplicate risk analysis
- Originality scoring
- Similarity confidence estimation
✅ Intelligent Feature Generation
- AI-generated project features
- Novelty-aware generation
- Domain-aware recommendations
📊 Evaluation
The system includes:
- Self-retrieval evaluation
- Real-query testing
- Hybrid ranking validation
- Confidence scoring
Evaluation Metrics
- Semantic Similarity Score
- Hybrid Score
- Originality Score
- Confidence Score
- Duplicate Risk Classification
📁 Project Structure
GRADUATION_PROJECT/
│
├── api/ # FastAPI backend
│
├── Data/
│ ├── raw/ # Original dataset
│ └── processed/ # Cleaned dataset
│
├── models/ # FAISS index & metadata
│
├── Notebooks/
│ └── TEST.ipynb # Training & evaluation notebook
│
├── src/
│ ├── recommendation_engine/ # Chatbot & recommendation logic
│ └── similarity_model/ # Semantic search engine
│
├── requirements.txt
├── README.md
└── .gitignore
🧩 Recommendation Engine Modules
recommendation_engine/
Contains:
- Chatbot engine
- Intent classification
- Prompt building
- Idea generation
- Feature generation
- Memory management
- Novelty checking
- Response formatting
🔬 Similarity Model Modules
similarity_model/
Contains:
- Semantic search
- Embedding engine
- Hybrid ranker
- Feature similarity engine
- Preprocessing pipeline
- Evaluation framework
⚡ Installation
1️⃣ Clone Repository
git clone https://github.com/YOUR_USERNAME/YOUR_REPOSITORY.git
cd YOUR_REPOSITORY
2️⃣ Create Virtual Environment
Windows
python -m venv .venv
.venv\Scripts\activate
Linux / Mac
python3 -m venv .venv
source .venv/bin/activate
3️⃣ Install Dependencies
pip install -r requirements.txt
🔑 Environment Variables
Create a .env file:
GEMINI_API_KEY=your_api_key_here
▶️ Running The Project
Run FastAPI Server
uvicorn api.main:app --reload
Run Notebook
jupyter notebook
Open:
Notebooks/TEST.ipynb
💡 Example Query
Input
AI-based smart library recommendation platform
Output
- Similar graduation projects
- Semantic similarity scores
- Originality analysis
- Duplicate risk estimation
- Recommended features
🎯 Future Improvements
- Full RAG integration
- Multi-agent orchestration
- GPU acceleration
- Advanced evaluation metrics
- Real-time deployment
- Database persistence
- Frontend dashboard
📚 Research Areas Covered
- Natural Language Processing (NLP)
- Semantic Search
- Recommendation Systems
- Vector Databases
- Conversational AI
- Information Retrieval
- Hybrid Ranking Systems
- Large Language Models (LLMs)
👨💻 Author
Yossef Assem
📄 License
This project is for educational and research purposes.