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