diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json new file mode 100644 index 0000000000000000000000000000000000000000..362820cf7a47ebff0989eccd72587217b300712e --- /dev/null +++ b/.devcontainer/devcontainer.json @@ -0,0 +1,33 @@ +{ + "name": "Python 3", + // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile + "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bookworm", + "customizations": { + "codespaces": { + "openFiles": [ + "README.md", + "src/book_recommender/apps/main_app.py" + ] + }, + "vscode": { + "settings": {}, + "extensions": [ + "ms-python.python", + "ms-python.vscode-pylance" + ] + } + }, + "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y 3MB) for <50ms query times. + * **Hybrid Search:** Combines dense vector retrieval with metadata filtering. + +--- + +## System Architecture + +The system consists of two main services and a frontend: + +```mermaid +graph LR + User((User)) --> Frontend[React App] + Frontend --> BooksAPI[Books API (FastAPI)] + BooksAPI --> DB[(Book Catalog CSV)] + BooksAPI --> PersonaliseAPI[Personalisation Engine] + PersonaliseAPI --> VectorDB[(FAISS Index)] +``` + +* **Frontend:** React, Tailwind, Vite (Static Build) +* **Books API:** Python, FastAPI (Deployed on Hugging Face Spaces) +* **Personalisation Engine:** Python, Sentence-Transformers, FAISS (Deployed on Hugging Face Spaces) + +--- + +## Getting Started + +### Prerequisites +* **Python 3.10+** (Essential for dependency compatibility) +* **Node.js 18+** +* **Git** + +### 1. Clone the Repository +```bash +git clone +cd books +``` + +### 2. Backend Setup (Books API) + +It is highly recommended to use `uv` for fast dependency management, but `pip` works too. + +```bash +# 1. Create virtual environment +pip install uv +uv venv .venv + +# 2. Activate environment +# Windows: +.venv\Scripts\activate +# Linux/Mac: +source .venv/bin/activate + +# 3. Install dependencies +uv pip install -r requirements.txt +``` + +**Configuration (`.env`):** +Copy `.env.example` to `.env` and configure the URL of your personalisation service. +```bash +cp .env.example .env +``` +In `.env`: +```ini +PORT=8000 +# URL of your deployed Personalisation Engine (or http://localhost:8001 if running locally) +PERSONALIZER_URL=https://nice-bill-personalisation-engine.hf.space +``` + +**Run the API:** +```bash +python src/book_recommender/api/main.py +# API will start at http://localhost:8000 +# Swagger Docs: http://localhost:8000/docs +``` + +### 3. Frontend Setup + +```bash +cd frontend + +# 1. Install dependencies +npm install + +# 2. Start Dev Server +npm run dev +``` +Open `http://localhost:5173` in your browser. + +--- + +## Deployment Guide + +### Option 1: Docker (Recommended) + +The project includes a production-ready `Dockerfile` optimized for Hugging Face Spaces. + +```bash +# Build the image +docker build -t serendipity-api -f docker/Dockerfile.backend . + +# Run the container +docker run -p 7860:7860 -e PERSONALIZER_URL=https://nice-bill-personalisation-engine.hf.space serendipity-api +``` + +### Option 2: Hugging Face Spaces + +1. Create a new **Docker** Space on Hugging Face. +2. Connect it to your GitHub repository (or push manually). +3. Set the following **Variables** in the Space settings: + * `PERSONALIZER_URL`: `https://nice-bill-personalisation-engine.hf.space` (or your engine's URL) +4. The Space will automatically build using `docker/Dockerfile.backend` and serve on port 7860. + +--- + +## Project Structure + +``` +books/ +├── data/ # Data storage +│ ├── catalog/ # CSV/Parquet metadata +│ └── embeddings_cache.npy # Vector data +├── docker/ # Docker configuration +│ └── Dockerfile.backend +├── frontend/ # React Application +│ ├── src/ +│ └── public/ +├── scripts/ # Data processing scripts +│ ├── download_data.py # Fetches datasets +│ └── prepare_100k_data.py # Raw data cleaning +├── src/ +│ └── book_recommender/ +│ ├── api/ # FastAPI endpoints +│ ├── ml/ # Machine Learning logic +│ └── services/ # External service connectors +└── tests/ # Pytest suite +``` + +## License +MIT License. \ No newline at end of file diff --git a/data/feedback/user_feedback.jsonl b/data/feedback/user_feedback.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..c785f5b548dbd3caf899f7262d61d8981250748a --- /dev/null +++ b/data/feedback/user_feedback.jsonl @@ -0,0 +1,4 @@ +{"timestamp": "2025-12-02T20:22:43.042683", "query": "fantasy adventure", "book_id": "4", "book_title": "Unrelated Book", "book_authors": "Author C", "feedback": "positive", "session_id": "test_session_1"} +{"timestamp": "2025-12-02T20:22:43.057774", "query": "sci-fi classic", "book_id": "5", "book_title": "Sci-Fi Classic", "book_authors": "Author D", "feedback": "negative", "session_id": "test_session_1"} +{"timestamp": "2025-12-02T21:43:22.362305", "query": "Cyberpunk noir detective", "book_id": "70962", "book_title": "Murder in the Rue de Paradis", "book_authors": "Cara Black", "feedback": "positive", "session_id": null} +{"timestamp": "2025-12-03T00:35:54.674627", "query": "Psychological horror 1920s", "book_id": "89722", "book_title": "Three Case Histories", "book_authors": "Sigmund Freud,Philip Rieff", "feedback": "positive", "session_id": null} diff --git a/docs/ARCHITECTURE.md b/docs/ARCHITECTURE.md new file mode 100644 index 0000000000000000000000000000000000000000..13570315ec9e0db108aaa5e44132d930546e8dad --- /dev/null +++ b/docs/ARCHITECTURE.md @@ -0,0 +1,176 @@ +# Architecture Overview: DeepShelf + +This document provides a detailed overview of the BookFinder application's architecture, outlining its components, data flow, and key technology decisions. + +## System Overview + +BookFinder is a content-based book recommendation system that leverages natural language processing (NLP) and vector similarity search to help users discover books. It comprises a Streamlit-based web application for user interaction, a FastAPI service for programmatic access, and a suite of Python scripts for data processing and embedding generation. + +The core principle is to transform book descriptions into high-dimensional numerical vectors (embeddings) using a pre-trained sentence transformer model. These embeddings are then used to find semantically similar books or to cluster books into thematic collections. + +## Component Descriptions + +The system is structured into several modular components: + +1. **Data Layer (`data/`)**: Stores raw, prepared, and processed data. + * `data/raw/`: Original datasets (e.g., `goodreads_data.csv`). + * `data/processed/`: Cleaned book metadata (`books_cleaned.parquet`), pre-computed embeddings (`book_embeddings.npy`), and embedding metadata (`embedding_metadata.json`). + +2. **Scripts Layer (`scripts/`)**: Utility scripts for data handling and model preparation. + * `prepare_goodreads_data.py`: Adapts raw data sources to a standardized format. + * `download_model.py` (optional): For pre-downloading the sentence transformer model. + +3. **Source Code Layer (`src/book_recommender/`)**: Contains the core logic of the application, organized into sub-packages. + * **`src/book_recommender/core/`**: Core utilities and configurations. + * `config.py`: Centralized configuration settings and constants. + * `exceptions.py`: Custom exception definitions for error handling. + * `logging_config.py`: Centralized logging configuration for the entire application. + * **`src/book_recommender/data/`**: Data processing components. + * `processor.py`: Handles data cleaning, normalization, deduplication, and feature engineering (e.g., creating `combined_text` for embeddings). + * **`src/book_recommender/ml/`**: Machine Learning related components. + * `embedder.py`: Manages the loading of the Sentence Transformer model and generation of embeddings for books and user queries. It uses `lru_cache` for efficient model loading. + * `recommender.py`: Implements the core recommendation logic using a FAISS index for fast Approximate Nearest Neighbor (ANN) search on book embeddings. + * `clustering.py`: Contains logic for K-Means clustering of book embeddings and generating descriptive names for these clusters based on common genres. + * `explainability.py`: Provides rule-based explanations for recommendations, detailing contributing factors like genre, keywords, and author similarity. + * `feedback.py`: Manages saving and retrieving user feedback on recommendations to a JSONL file. + * `src/book_recommender/utils.py`: General utility functions, including book cover fetching from various APIs (Google Books, Open Library) and batch processing. + +4. **User Interface Layer (`src/book_recommender/apps/`)**: Streamlit applications. + * `main_app.py`: The main interactive web application where users can get book recommendations, browse by genre/query, view explanations, and provide feedback. + * `analytics_app.py`: A separate Streamlit dashboard to visualize collected user feedback and system usage statistics. + +5. **API Layer (`src/book_recommender/api/`)**: FastAPI application for programmatic access. + * `main.py`: Defines the FastAPI application and its endpoints (recommendations, book listing, search, stats, clusters, explanations, feedback). + * `models.py`: Pydantic models for request and response data validation and serialization. + * `dependencies.py`: FastAPI dependency injection functions to manage and cache shared resources like the `BookRecommender` and embedding models. + +6. **CI/CD (`.github/workflows/ci-cd.yml`)**: GitHub Actions workflow for automated testing and code quality checks. + +7. **Containerization (`streamlit.Dockerfile`, `api.Dockerfile`, `analytics.Dockerfile`, `docker-compose.yml`)**: For packaging and orchestrating the application services. + * `streamlit.Dockerfile`: Defines the build process for the main Streamlit application into a Docker image. + * `api.Dockerfile`: Defines the build process for the FastAPI application into a Docker image. + * `analytics.Dockerfile`: Defines the build process for the analytics Streamlit application into a Docker image. + * `docker-compose.yml`: Orchestrates the `streamlit`, `api`, and `analytics` services for local development and deployment. + +## Data Flow + +The primary data flow for generating recommendations and user interaction is as follows: + +1. **Raw Data Ingestion**: Raw CSV datasets (e.g., `goodreads_data.csv`) are placed in `data/raw/`. +2. **Data Preparation**: The `scripts/prepare_goodreads_data.py` script and `src/book_recommender/data/processor.py` clean, standardize, and deduplicate the raw data, saving it as `books_cleaned.parquet` in `data/processed/`. +3. **Embedding Generation**: `src/book_recommender/ml/embedder.py` loads `books_cleaned.parquet` and generates semantic embeddings for each book's `combined_text`, saving them as `book_embeddings.npy` in `data/processed/`. +4. **Application Startup**: + * The `src/book_recommender/apps/main_app.py` (Streamlit) and `src/book_recommender/api/main.py` (FastAPI) applications load `books_cleaned.parquet` and `book_embeddings.npy` into memory. + * `src/book_recommender/ml/recommender.py` initializes a FAISS index with these embeddings for fast similarity search. + * `src/book_recommender/ml/clustering.py` generates book clusters and their names from the embeddings. +5. **User Interaction (Streamlit `src/book_recommender/apps/main_app.py`)**: + * User inputs a natural language query or browses clusters. + * If a query, `src/book_recommender/ml/embedder.py` generates an embedding for the query. + * `src/book_recommender/ml/recommender.py` uses the query embedding (or a book's embedding from a title search) to find similar books. + * `src/book_recommender/ml/explainability.py` generates reasons for recommendations. + * `src/book_recommender/utils.py` fetches book cover images. + * `src/book_recommender/ml/feedback.py` stores user feedback on recommendations. +6. **API Interaction (FastAPI `src/book_recommender/api/main.py`)**: + * External clients send requests to API endpoints (e.g., `/recommend/query`, `/books`, `/feedback`). + * `src/book_recommender/api/dependencies.py` ensures efficient loading and caching of `recommender` and `embedder` instances. + * Requests are validated using Pydantic models (`src/book_recommender/api/models.py`). + * Core logic in `src/book_recommender/` modules is invoked (e.g., `recommender.py`, `explainability.py`, `feedback.py`). + * Responses are returned, adhering to defined Pydantic response models. + + **Example API Calls:** + + ```bash + # Health Check + curl http://localhost:8000/health + + # Recommend by Query + curl -X POST "http://localhost:8000/recommend/query" \ + -H "Content-Type: application/json" \ + -d '{ + "query": "fantasy adventure with dragons", + "top_k": 5 + }' + + # List all Clusters + curl http://localhost:8000/clusters + + # Submit Feedback + curl -X POST "http://localhost:8000/feedback" \ + -H "Content-Type: application/json" \ + -d '{ + "query": "fantasy adventure with dragons", + "book_id": "example_book_id", + "feedback_type": "positive", + "session_id": "user_session_abc" + }' + ``` + +7. **Analytics (`src/book_recommender/apps/analytics_app.py`)**: + * Loads accumulated feedback data from `data/feedback/user_feedback.jsonl` using `src/book_recommender/ml/feedback.py`. + * Processes and visualizes statistics using Streamlit and Plotly. + +```mermaid +graph TD + subgraph Data Flow & Processing + raw_data[Raw Data (CSV)] --> A(scripts/prepare_goodreads_data.py); + A --> B[books_prepared.csv]; + B --> C{src/book_recommender/data/processor.py}; + C --> D[books_cleaned.parquet]; + D --contains text--> E{src/book_recommender/ml/embedder.py}; + E --> F[book_embeddings.npy]; + end + + subgraph Application Runtime + G(src/book_recommender/apps/main_app.py - Streamlit UI) --loads--> D & F; + G --uses--> H(src/book_recommender/ml/recommender.py); + G --uses--> I(src/book_recommender/ml/embedder.py); + G --uses--> J(src/book_recommender/ml/clustering.py); + G --uses--> K(src/book_recommender/ml/explainability.py); + G --uses--> L(src/book_recommender/ml/feedback.py); + + M(src/book_recommender/api/main.py - FastAPI) --uses--> N(src/book_recommender/api/dependencies.py); + N --loads--> D & F; + N --uses--> H & I & J & K & L; + + O(src/book_recommender/apps/analytics_app.py - Streamlit Dashboard) --loads--> P[user_feedback.jsonl]; + P --via--> L; + + User[User] --Interacts with--> G; + Client[External Client] --Interacts with--> M; + end + + subgraph Utilities & Configuration + Q[src/book_recommender/core/config.py]; + R[src/book_recommender/utils.py]; + S[src/book_recommender/core/logging_config.py]; + end + + style raw_data fill:#f9f,stroke:#333,stroke-width:2px + style D fill:#ccf,stroke:#333,stroke-width:2px + style F fill:#ccf,stroke:#333,stroke-width:2px + style P fill:#fcc,stroke:#333,stroke-width:2px +``` + +## Technology Decisions + +* **Python 3.10+**: Modern, versatile language. +* **Streamlit**: Chosen for rapid development of interactive web UIs with minimal frontend code. Its caching mechanisms (`@st.cache_resource`, `@st.cache_data`) are crucial for performance with ML models. +* **FastAPI**: Selected for building a high-performance, asynchronous API with automatic Pydantic-based data validation and Swagger/OpenAPI documentation. +* **Sentence-Transformers**: A powerful library for generating dense vector embeddings from text, suitable for semantic search. +* **FAISS**: An efficient library for similarity search and clustering of dense vectors, essential for scaling recommendations. +* **Pandas / NumPy**: Standard libraries for data manipulation and numerical operations. +* **Scikit-learn**: Used for traditional machine learning tasks like K-Means clustering. +* **Plotly Express**: For creating interactive and aesthetically pleasing visualizations in the analytics dashboard. +* **Pydantic**: Data validation and settings management using Python type hints, integral to FastAPI. +* **python-dotenv**: For managing environment variables, facilitating flexible configuration across environments. +* **GitHub Actions**: For Continuous Integration/Continuous Deployment (CI/CD), automating testing, linting, and Docker image builds. +* **Docker / Docker Compose**: For containerizing the application and orchestrating multi-service deployments, ensuring consistent environments. + +## Future Considerations + +* **Data Version Control (DVC)**: Implement DVC for robust tracking of data and model versions, enhancing reproducibility in production. +* **Scalability**: For extremely large datasets, consider distributed FAISS indexes or cloud-native vector databases. +* **Advanced Explanations**: Explore more sophisticated XAI techniques beyond rule-based, potentially involving LLMs or specific feature attribution methods. +* **User Authentication**: For multi-user scenarios, integrate an authentication system (e.g., OAuth2, JWT). +* **Database Integration**: Replace JSONL feedback storage with a dedicated database (e.g., PostgreSQL) for more robust data management and querying. +* **Full UI Testing**: Implement UI tests using tools like Playwright or Selenium to ensure frontend consistency and functionality. \ No newline at end of file diff --git a/frontend/.gitignore b/frontend/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..a547bf36d8d11a4f89c59c144f24795749086dd1 --- /dev/null +++ b/frontend/.gitignore @@ -0,0 +1,24 @@ +# Logs +logs +*.log +npm-debug.log* +yarn-debug.log* +yarn-error.log* +pnpm-debug.log* +lerna-debug.log* + +node_modules +dist +dist-ssr +*.local + +# Editor directories and files +.vscode/* +!.vscode/extensions.json +.idea +.DS_Store +*.suo +*.ntvs* +*.njsproj +*.sln +*.sw? diff --git a/frontend/README.md b/frontend/README.md new file mode 100644 index 0000000000000000000000000000000000000000..d2e77611fd3d959fee0487c41bd27b318be32b04 --- /dev/null +++ b/frontend/README.md @@ -0,0 +1,73 @@ +# React + TypeScript + Vite + +This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules. + +Currently, two official plugins are available: + +- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh +- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh + +## React Compiler + +The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation). + +## Expanding the ESLint configuration + +If you are developing a production application, we recommend updating the configuration to enable type-aware lint rules: + +```js +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + // Other configs... + + // Remove tseslint.configs.recommended and replace with this + tseslint.configs.recommendedTypeChecked, + // Alternatively, use this for stricter rules + tseslint.configs.strictTypeChecked, + // Optionally, add this for stylistic rules + tseslint.configs.stylisticTypeChecked, + + // Other configs... + ], + languageOptions: { + parserOptions: { + project: ['./tsconfig.node.json', './tsconfig.app.json'], + tsconfigRootDir: import.meta.dirname, + }, + // other options... + }, + }, +]) +``` + +You can also install [eslint-plugin-react-x](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-x) and [eslint-plugin-react-dom](https://github.com/Rel1cx/eslint-react/tree/main/packages/plugins/eslint-plugin-react-dom) for React-specific lint rules: + +```js +// eslint.config.js +import reactX from 'eslint-plugin-react-x' +import reactDom from 'eslint-plugin-react-dom' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + // Other configs... + // Enable lint rules for React + reactX.configs['recommended-typescript'], + // Enable lint rules for React DOM + reactDom.configs.recommended, + ], + languageOptions: { + parserOptions: { + project: ['./tsconfig.node.json', './tsconfig.app.json'], + tsconfigRootDir: import.meta.dirname, + }, + // other options... + }, + }, +]) +``` diff --git a/frontend/eslint.config.js b/frontend/eslint.config.js new file mode 100644 index 0000000000000000000000000000000000000000..5e6b472f583e34a1cca751440d4f241495475723 --- /dev/null +++ b/frontend/eslint.config.js @@ -0,0 +1,23 @@ +import js from '@eslint/js' +import globals from 'globals' +import reactHooks from 'eslint-plugin-react-hooks' +import reactRefresh from 'eslint-plugin-react-refresh' +import tseslint from 'typescript-eslint' +import { defineConfig, globalIgnores } from 'eslint/config' + +export default defineConfig([ + globalIgnores(['dist']), + { + files: ['**/*.{ts,tsx}'], + extends: [ + js.configs.recommended, + tseslint.configs.recommended, + reactHooks.configs.flat.recommended, + reactRefresh.configs.vite, + ], + languageOptions: { + ecmaVersion: 2020, + globals: globals.browser, + }, + }, +]) diff --git a/frontend/index.html b/frontend/index.html new file mode 100644 index 0000000000000000000000000000000000000000..04fdacd5cd0d09c2446c7b05661625370e124a4c --- /dev/null +++ b/frontend/index.html @@ -0,0 +1,16 @@ + + + + + + + + + + Serendipity + + +
+ + + diff --git a/frontend/package-lock.json b/frontend/package-lock.json new file mode 100644 index 0000000000000000000000000000000000000000..519f5e445d4a297854663f91d675f596fc35a078 --- /dev/null +++ b/frontend/package-lock.json @@ -0,0 +1,4476 @@ +{ + "name": "frontend", + "version": "0.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "frontend", + "version": "0.0.0", + "dependencies": { + "axios": "^1.13.2", + "clsx": "^2.1.1", + "lucide-react": "^0.555.0", + "react": "^19.2.0", + "react-dom": "^19.2.0", + "react-router-dom": "^7.9.6", + "tailwind-merge": "^3.4.0" + }, + "devDependencies": { + "@eslint/js": "^9.39.1", + "@types/node": "^24.10.1", + "@types/react": "^19.2.5", + "@types/react-dom": "^19.2.3", + "@vitejs/plugin-react": "^5.1.1", + "autoprefixer": "^10.4.22", + "eslint": "^9.39.1", + "eslint-plugin-react-hooks": "^7.0.1", + "eslint-plugin-react-refresh": "^0.4.24", + "globals": "^16.5.0", + "postcss": "^8.5.6", + "tailwindcss": 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"peer": true, + "funding": { + "url": "https://github.com/sponsors/colinhacks" + } + }, + "node_modules/zod-validation-error": { + "version": "4.0.2", + "resolved": "https://registry.npmjs.org/zod-validation-error/-/zod-validation-error-4.0.2.tgz", + "integrity": "sha512-Q6/nZLe6jxuU80qb/4uJ4t5v2VEZ44lzQjPDhYJNztRQ4wyWc6VF3D3Kb/fAuPetZQnhS3hnajCf9CsWesghLQ==", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=18.0.0" + }, + "peerDependencies": { + "zod": "^3.25.0 || ^4.0.0" + } + } + } +} diff --git a/frontend/package.json b/frontend/package.json new file mode 100644 index 0000000000000000000000000000000000000000..7178e42d7eb43cb7ec531a22d69f90a8ee810c35 --- /dev/null +++ b/frontend/package.json @@ -0,0 +1,38 @@ +{ + "name": "serendipity-web", + "private": true, + "version": "0.0.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "tsc -b && vite build", + "lint": "eslint .", + "preview": "vite preview" + }, + "dependencies": { + "axios": "^1.13.2", + "clsx": "^2.1.1", + "lucide-react": "^0.555.0", + "react": "^19.2.0", + "react-dom": "^19.2.0", + "react-router-dom": "^7.9.6", + "tailwind-merge": "^3.4.0" + }, + "devDependencies": { + "@eslint/js": "^9.39.1", + "@types/node": "^24.10.1", + "@types/react": "^19.2.5", + "@types/react-dom": "^19.2.3", + "@vitejs/plugin-react": "^5.1.1", + "autoprefixer": "^10.4.22", + "eslint": "^9.39.1", + "eslint-plugin-react-hooks": "^7.0.1", + "eslint-plugin-react-refresh": "^0.4.24", + "globals": "^16.5.0", + "postcss": "^8.5.6", + "tailwindcss": "^3.4.17", + "typescript": "~5.9.3", + "typescript-eslint": "^8.46.4", + "vite": "^7.2.4" + } +} diff --git a/frontend/postcss.config.js b/frontend/postcss.config.js new file mode 100644 index 0000000000000000000000000000000000000000..2e7af2b7f1a6f391da1631d93968a9d487ba977d --- /dev/null +++ b/frontend/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/frontend/public/vite.svg b/frontend/public/vite.svg new file mode 100644 index 0000000000000000000000000000000000000000..e7b8dfb1b2a60bd50538bec9f876511b9cac21e3 --- /dev/null +++ b/frontend/public/vite.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/App.css b/frontend/src/App.css new file mode 100644 index 0000000000000000000000000000000000000000..b9d355df2a5956b526c004531b7b0ffe412461e0 --- /dev/null +++ b/frontend/src/App.css @@ -0,0 +1,42 @@ +#root { + max-width: 1280px; + margin: 0 auto; + padding: 2rem; + text-align: center; +} + +.logo { + height: 6em; + padding: 1.5em; + will-change: filter; + transition: filter 300ms; +} +.logo:hover { + filter: drop-shadow(0 0 2em #646cffaa); +} +.logo.react:hover { + filter: drop-shadow(0 0 2em #61dafbaa); +} + +@keyframes logo-spin { + from { + transform: rotate(0deg); + } + to { + transform: rotate(360deg); + } +} + +@media (prefers-reduced-motion: no-preference) { + a:nth-of-type(2) .logo { + animation: logo-spin infinite 20s linear; + } +} + +.card { + padding: 2em; +} + +.read-the-docs { + color: #888; +} diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx new file mode 100644 index 0000000000000000000000000000000000000000..9726049fb3d60d1b939bbe60009c7418135d394f --- /dev/null +++ b/frontend/src/App.tsx @@ -0,0 +1,616 @@ +import { useState, useEffect, useRef } from 'react'; +import { + Search, BookOpen, Loader2, X, Sparkles, LayoutGrid, + CheckCircle, Moon, Sun, Info, Bookmark, ArrowLeft, Users +} from 'lucide-react'; +import { api } from './api'; +import { Loader } from './Loader'; +import { BookCover } from './BookCover'; +import { BookCard } from './BookCard'; +import { DEMO_PERSONAS } from './data/personas'; +import type { RecommendationResult, BookCluster } from './types'; + +function App() { + const [query, setQuery] = useState(''); + const [results, setResults] = useState([]); + const [loading, setLoading] = useState(false); + const [longLoading, setLongLoading] = useState(false); + const [hasSearched, setHasSearched] = useState(false); + + // Modal State + const [selectedResult, setSelectedResult] = useState(null); + const [historyStack, setHistoryStack] = useState([]); + const [explanation, setExplanation] = useState<{ summary: string; details: Record } | null>(null); + const [explaining, setExplaining] = useState(false); + const [showAbout, setShowAbout] = useState(false); + const modalRef = useRef(null); + + // Related Books State + const [relatedBooks, setRelatedBooks] = useState([]); + const [loadingRelated, setLoadingRelated] = useState(false); + + // Dynamic Clusters State + const [clusters, setClusters] = useState([]); + const [loadingClusters, setLoadingClusters] = useState(true); + + // Toast State + const [toast, setToast] = useState<{ message: string; visible: boolean } | null>(null); + + // Settings / Theme State + const [darkMode, setDarkMode] = useState(false); + + // Personalization / History State + const [readHistory, setReadHistory] = useState(() => { + try { + return JSON.parse(localStorage.getItem('bookfinder_read_history') || '[]'); + } catch { + return []; + } + }); + + useEffect(() => { + if (selectedResult && modalRef.current) { + modalRef.current.scrollTop = 0; + } + }, [selectedResult]); + + useEffect(() => { + const savedTheme = localStorage.getItem('bookfinder_theme_mode'); + if (savedTheme) { + setDarkMode(savedTheme === 'dark'); + } else if (window.matchMedia && window.matchMedia('(prefers-color-scheme: dark)').matches) { + setDarkMode(true); + } + }, []); + + useEffect(() => { + localStorage.setItem('bookfinder_theme_mode', darkMode ? 'dark' : 'light'); + if (darkMode) { + document.documentElement.classList.add('dark'); + } else { + document.documentElement.classList.remove('dark'); + } + }, [darkMode]); + + useEffect(() => { + localStorage.setItem('bookfinder_read_history', JSON.stringify(readHistory)); + }, [readHistory]); + + const toggleReadBook = (title: string) => { + setReadHistory(prev => { + if (prev.includes(title)) { + showToast("Removed from library"); + return prev.filter(t => t !== title); + } else { + showToast("Added to library"); + return [...prev, title]; + } + }); + triggerHaptic(); + }; + + const handlePersonalize = async () => { + if (readHistory.length === 0) { + showToast("Mark some books as read first!"); + return; + } + triggerHaptic(); + setLoading(true); + setHasSearched(true); + setResults([]); + setQuery("✨ Selected for you"); + + try { + const data = await api.recommendPersonalized(readHistory); + setResults(data); + } catch (err) { + console.error(err); + showToast("Failed to personalize. Try again."); + } finally { + setLoading(false); + } + }; + + const handlePersonaSelect = (persona: typeof DEMO_PERSONAS[0]) => { + triggerHaptic(); + setReadHistory(persona.history); + showToast(`Switched to ${persona.name} mode`); + + setLoading(true); + setHasSearched(true); + setResults([]); + setQuery(`👤 Demo: ${persona.name}`); + + api.recommendPersonalized(persona.history) + .then(setResults) + .catch(err => { + console.error(err); + showToast("Failed to load persona."); + }) + .finally(() => setLoading(false)); + }; + + const toggleTheme = async (e: React.MouseEvent) => { + const isDark = !darkMode; + if (!(document as any).startViewTransition) { + setDarkMode(isDark); + return; + } + const x = e.clientX; + const y = e.clientY; + const endRadius = Math.hypot(Math.max(x, window.innerWidth - x), Math.max(y, window.innerHeight - y)); + const transition = (document as any).startViewTransition(() => setDarkMode(isDark)); + await transition.ready; + const clipPath = [`circle(0px at ${x}px ${y}px)`, `circle(${endRadius}px at ${x}px ${y}px)`]; + document.documentElement.animate( + { clipPath: isDark ? clipPath : [...clipPath].reverse() }, + { duration: 500, easing: 'ease-in-out', pseudoElement: isDark ? '::view-transition-new(root)' : '::view-transition-old(root)' } + ); + }; + + const triggerHaptic = () => { + if (typeof navigator !== 'undefined' && navigator.vibrate) { + navigator.vibrate(10); + } + }; + + useEffect(() => { + const fetchClusters = async () => { + try { + const data = await api.getClusters(); + setClusters(data.slice(0, 6)); + } catch (err) { + console.error("Failed to fetch clusters", err); + } finally { + setLoadingClusters(false); + } + }; + fetchClusters(); + }, []); + + const showToast = (message: string) => { + setToast({ message, visible: true }); + setTimeout(() => setToast(null), 3000); + }; + + const handleSearch = async (e?: React.FormEvent) => { + if (e) e.preventDefault(); + if (!query.trim()) return; + triggerHaptic(); + setLoading(true); + setLongLoading(false); + setHasSearched(true); + setResults([]); + const timer = setTimeout(() => setLongLoading(true), 3000); + try { + const data = await api.recommendByQuery(query); + setResults(data); + } catch (err) { + console.error(err); + } finally { + clearTimeout(timer); + setLoading(false); + setLongLoading(false); + } + }; + + const handleQuickSearch = (text: string) => { + setQuery(text); + triggerHaptic(); + setLoading(true); + setLongLoading(false); + setHasSearched(true); + setResults([]); + const timer = setTimeout(() => setLongLoading(true), 3000); + api.recommendByQuery(text).then(setResults).catch(console.error).finally(() => { + clearTimeout(timer); + setLoading(false); + setLongLoading(false); + }); + }; + + const handleFeedback = async (bookId: string, type: 'positive' | 'negative') => { + triggerHaptic(); + try { + await api.submitFeedback(query, bookId, type); + showToast(type === 'positive' ? "Thanks! We'll show more like this." : "Thanks! We'll tune our results."); + } catch (err) { + console.error(err); + } + }; + + const handleReadMore = async (result: RecommendationResult, isDrillingDown = false) => { + triggerHaptic(); + if (isDrillingDown && selectedResult) { + setHistoryStack(prev => [...prev, selectedResult]); + } else if (!isDrillingDown) { + setHistoryStack([]); + } + setSelectedResult(result); + setExplaining(true); + setExplanation(null); + setRelatedBooks([]); + setLoadingRelated(true); + try { + const [expl, related] = await Promise.allSettled([ + api.explainRecommendation(query || result.book.title, result.book, result.similarity_score), + api.getRelatedBooks(result.book.title) + ]); + if (expl.status === 'fulfilled') setExplanation(expl.value); + if (related.status === 'fulfilled') setRelatedBooks(related.value); + } catch (err) { + console.error("Failed to fetch details", err); + } finally { + setExplaining(false); + setLoadingRelated(false); + } + }; + + const handleBack = () => { + triggerHaptic(); + if (historyStack.length > 0) { + const prev = historyStack[historyStack.length - 1]; + setHistoryStack(curr => curr.slice(0, -1)); + setSelectedResult(prev); + setRelatedBooks([]); + setExplanation(null); + setLoadingRelated(true); + setExplaining(true); + Promise.allSettled([ + api.explainRecommendation(query || prev.book.title, prev.book, prev.similarity_score), + api.getRelatedBooks(prev.book.title) + ]).then(([expl, related]) => { + if (expl.status === 'fulfilled') setExplanation(expl.value); + if (related.status === 'fulfilled') setRelatedBooks(related.value); + setExplaining(false); + setLoadingRelated(false); + }); + } + }; + + const closeModal = () => { + setSelectedResult(null); + setHistoryStack([]); + setExplanation(null); + setRelatedBooks([]); + }; + + const handleClusterClick = (clusterName: string) => { + triggerHaptic(); + setQuery(clusterName); + setLoading(true); + setHasSearched(true); + setResults([]); + api.recommendByQuery(clusterName).then(setResults).catch(console.error).finally(() => setLoading(false)); + }; + + return ( +
+ +
+
+
+
+ + {toast && ( +
+ + {toast.message} +
+ )} + +
+
+
setActiveView('search')}> +
+
+ +
+ Serendipity +
+ +
+ + +
+
+
+ +
+
+ {!hasSearched && ( +
+

+ Find your next
+ obsession. +

+

+ Describe the vibe, plot, or character you're looking for. +

+
+ )} + +
+ setQuery(e.target.value)} + placeholder="e.g. A sci-fi thriller about AI consciousness..." + className="relative w-full bg-white dark:bg-zinc-900 border-2 border-zinc-200 dark:border-zinc-800 focus:border-indigo-500 dark:focus:border-indigo-500 rounded-full py-4 pl-6 pr-24 text-lg outline-none transition-all placeholder:text-zinc-400 dark:placeholder:text-zinc-600 shadow-xl shadow-zinc-200/50 dark:shadow-black/50 text-zinc-900 dark:text-white" + autoFocus + /> + {query && ( + + )} + +
+ + {loading && longLoading && ( +
+

+ + Waking up... this might take a moment 😴 +

+
+ )} + + {!hasSearched && !loading && ( +
+
+ {[ + "Cyberpunk noir detective", + "Cozy cottagecore mystery", + "Space opera with politics", + "Psychological horror 1920s" + ].map((prompt) => ( + + ))} +
+ + {/* Demo Personas */} +
+
+ + Try a Demo Persona +
+
+ {DEMO_PERSONAS.map(persona => ( + + ))} +
+
+ + {/* Personalization Trigger */} + {readHistory.length > 0 && ( + + )} +
+ )} + + {!hasSearched && ( +
+
+ + Curated Collections +
+ + {loadingClusters ? ( +
+ {[1,2,3,4,5,6].map(i => ( +
+ ))} +
+ ) : ( +
+ {clusters.map(cluster => ( + + ))} +
+ )} +
+ )} +
+ +
+ {loading && results.length === 0 ? ( +
+ +
+ ) : ( + results.map((result) => ( + handleReadMore(result)} + onFeedback={handleFeedback} + /> + )) + )} +
+ + {hasSearched && results.length === 0 && !loading && ( +
+ No books found matching that description. +
+ )} +
+ + {selectedResult && ( +
+
+
+ + {historyStack.length > 0 && ( + + )} +
+
+ +
+

{selectedResult.book.title}

+
+
+
+

{selectedResult.book.title}

+

by {selectedResult.book.authors.join(', ')}

+
+
+

{selectedResult.book.description}

+
+
+
+ + Why this match? +
+ {explaining ? ( +
+ + Reading your mind... +
+ ) : explanation ? ( +
+

{explanation.summary}

+
+ {Object.entries(explanation.details).map(([key, val]) => ( + + {key} {val}% + + ))} +
+
+ ) : ( +

Could not generate explanation.

+ )} +
+ +
+

You might also like

+ {loadingRelated ? ( +
+ {[1, 2, 3].map(i => ( +
+ ))} +
+ ) : ( +
+ {relatedBooks.map(rel => ( +
handleReadMore(rel)} className="group cursor-pointer active:scale-95 transition-transform"> +
+ +
+

{rel.book.title}

+

{rel.book.authors[0]}

+
+ ))} +
+ )} +
+
+
+
+
+ )} + + {showAbout && ( +
+
setShowAbout(false)} /> +
+ +
+

About Serendipity

+
+

+ Serendipity is an intelligent book discovery interface powered by the DeepShelf Engine. +

+

+ Unlike traditional keyword search, DeepShelf uses semantic vector embeddings to understand the meaning and feeling of your request. It connects you with books that match your specific wavelength, even if they don't share a single keyword. +

+
+
+

How it works:

+
    +
  • Semantic Search: Understands the meaning and context of your queries.
    • +
  • Vector Embeddings: DeepShelf maps books and queries into a high-dimensional vector space.
    • +
  • Neural Retrieval: Finds the nearest neighbors to your thought in the library of 100,000+ books.
    • +
+
+

+ Describe your ideal read and let Serendipity uncover your next literary obsession! +

+
+
+
+ )} +
+ ); +} + +export default App; \ No newline at end of file diff --git a/frontend/src/BookCard.tsx b/frontend/src/BookCard.tsx new file mode 100644 index 0000000000000000000000000000000000000000..c70f71ced8bb947195b46ca2406e8732ae62dde9 --- /dev/null +++ b/frontend/src/BookCard.tsx @@ -0,0 +1,122 @@ +import React from 'react'; +import { ThumbsUp, ThumbsDown, ArrowRight, Sparkles, Bookmark, Check } from 'lucide-react'; +import { BookCover } from './BookCover'; +import type { RecommendationResult } from './types'; + +interface BookCardProps { + result: RecommendationResult; + isRead?: boolean; + onToggleRead?: (title: string) => void; + onClick: () => void; + onFeedback: (id: string, type: 'positive' | 'negative') => void; +} + +export function BookCard({ result, isRead, onToggleRead, onClick, onFeedback }: BookCardProps) { + const { book, similarity_score } = result; + const percentage = Math.round(similarity_score * 100); + + const handleToggleRead = (e: React.MouseEvent) => { + e.stopPropagation(); + if (onToggleRead) { + onToggleRead(book.title); + } + }; + + return ( +
+ {/* Read Status Toggle (Absolute Top Right) */} + {onToggleRead && ( + + )} + + {/* Cover Image Section */} +
+ + + {/* Match Badge (Mobile Overlay) */} +
+ + {percentage}% Match + +
+
+ + {/* Content Section */} +
+
+

+ {book.title} +

+ {/* Desktop Match Badge */} + + + {percentage}% + +
+ +

+ by {book.authors?.join(', ') || 'Unknown Author'} +

+ +

+ {book.description || 'No description available.'} +

+ +
+ {/* Mobile: Simple 'Read more' */} + + + {/* Feedback Actions (Desktop Hover / Mobile Always Visible but subtle) */} +
e.stopPropagation()} + > + + +
+
+ + {/* Genre Tags */} +
+ {book.genres?.slice(0, 3).map(genre => ( + + {genre} + + ))} +
+
+
+ ); +} diff --git a/frontend/src/BookCover.tsx b/frontend/src/BookCover.tsx new file mode 100644 index 0000000000000000000000000000000000000000..6319bdc1d115a317bcade320e128bc722d81e53e --- /dev/null +++ b/frontend/src/BookCover.tsx @@ -0,0 +1,75 @@ +import { useState } from 'react'; +import { BookOpen } from 'lucide-react'; + +// Deterministic color generator based on string +const getColor = (str: string) => { + const colors = [ + 'from-red-500 to-orange-500', + 'from-orange-500 to-amber-500', + 'from-amber-500 to-yellow-500', + 'from-yellow-500 to-lime-500', + 'from-lime-500 to-green-500', + 'from-green-500 to-emerald-500', + 'from-emerald-500 to-teal-500', + 'from-teal-500 to-cyan-500', + 'from-cyan-500 to-sky-500', + 'from-sky-500 to-blue-500', + 'from-blue-500 to-indigo-500', + 'from-indigo-500 to-violet-500', + 'from-violet-500 to-purple-500', + 'from-purple-500 to-fuchsia-500', + 'from-fuchsia-500 to-pink-500', + 'from-pink-500 to-rose-500', + 'from-slate-500 to-zinc-500', + ]; + let hash = 0; + for (let i = 0; i < str.length; i++) { + hash = str.charCodeAt(i) + ((hash << 5) - hash); + } + return colors[Math.abs(hash) % colors.length]; +}; + +interface BookCoverProps { + src?: string | null; + title: string; + author?: string; + className?: string; +} + +export function BookCover({ src, title, author, className = "" }: BookCoverProps) { + const [error, setError] = useState(false); + const gradient = getColor(title); + + if (src && !error) { + return ( + {title} setError(true)} + className={`object-cover ${className}`} + /> + ); + } + + // Fallback: Generated Cover + return ( +
+ {/* Texture Overlay */} +
+ +
+

+ {title} +

+
+ + {author && ( +

+ {author} +

+ )} + + +
+ ); +} diff --git a/frontend/src/Loader.tsx b/frontend/src/Loader.tsx new file mode 100644 index 0000000000000000000000000000000000000000..b0eb1a7d779c69b1dcf55230a30f865b7b40af62 --- /dev/null +++ b/frontend/src/Loader.tsx @@ -0,0 +1,50 @@ +import { BookOpen } from 'lucide-react'; +import { useState, useEffect } from 'react'; + +const LOADING_MESSAGES = [ + "Scanning literary universe...", + "Analyzing plot vectors...", + "Connecting thematic dots...", + "Filtering hidden gems...", + "Synthesizing recommendations..." +]; + +export function Loader() { + const [messageIndex, setMessageIndex] = useState(0); + + useEffect(() => { + const interval = setInterval(() => { + setMessageIndex((prev) => (prev + 1) % LOADING_MESSAGES.length); + }, 800); + return () => clearInterval(interval); + }, []); + + return ( +
+ + {/* Orbiting / Pulsing Effect */} +
+ {/* Core */} +
+ +
+ + {/* Orbital Ring 1 */} +
+ + {/* Orbital Ring 2 */} +
+ + {/* Pulsing Aura */} +
+
+ + {/* Text */} +
+

+ {LOADING_MESSAGES[messageIndex]} +

+
+
+ ); +} diff --git a/frontend/src/api.ts b/frontend/src/api.ts new file mode 100644 index 0000000000000000000000000000000000000000..3c6118c674ef8cd1cb5161d4b13f259b7731419d --- /dev/null +++ b/frontend/src/api.ts @@ -0,0 +1,59 @@ +import axios from 'axios'; +import type { RecommendationResult, Book, BookCluster } from './types'; + +const API_URL = import.meta.env.VITE_API_URL || 'http://localhost:8000'; + +export const api = { + getClusters: async (): Promise => { + const response = await axios.get(`${API_URL}/clusters`); + return response.data; + }, + + recommendByQuery: async (query: string): Promise => { + const response = await axios.post(`${API_URL}/recommend/query`, { + query, + top_k: 12, + }); + return response.data; + }, + + searchBooks: async (query: string): Promise<{ books: Book[] }> => { + const response = await axios.get(`${API_URL}/books/search`, { + params: { query, page: 1, page_size: 20 }, + }); + return response.data; + }, + + submitFeedback: async (query: string, bookId: string, type: 'positive' | 'negative') => { + await axios.post(`${API_URL}/feedback`, { + query, + book_id: bookId, + feedback_type: type, + }); + }, + + explainRecommendation: async (query: string, book: Book, score: number) => { + const response = await axios.post(`${API_URL}/explain`, { + query_text: query, + recommended_book: book, + similarity_score: score + }); + return response.data; + }, + + getRelatedBooks: async (title: string): Promise => { + const response = await axios.post(`${API_URL}/recommend/title`, { + title, + top_k: 6, + }); + return response.data; + }, + + recommendPersonalized: async (history: string[]): Promise => { + const response = await axios.post(`${API_URL}/recommend/personalize`, { + user_history: history, + top_k: 12, + }); + return response.data; + } +}; diff --git a/frontend/src/assets/react.svg b/frontend/src/assets/react.svg new file mode 100644 index 0000000000000000000000000000000000000000..6c87de9bb3358469122cc991d5cf578927246184 --- /dev/null +++ b/frontend/src/assets/react.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/frontend/src/data/personas.ts b/frontend/src/data/personas.ts new file mode 100644 index 0000000000000000000000000000000000000000..d5602eac955c5ec4b25088053e178a142430d585 --- /dev/null +++ b/frontend/src/data/personas.ts @@ -0,0 +1,65 @@ +export interface Persona { + id: string; + name: string; + emoji: string; + description: string; + history: string[]; +} + +export const DEMO_PERSONAS: Persona[] = [ + { + id: 'scifi_fan', + name: 'The Futurist', + emoji: '🤖', + description: 'Loves cyberpunk, AI, and space opera.', + history: [ + "Neuromancer", + "Snow Crash", + "Dune", + "I, Robot", + "Do Androids Dream of Electric Sheep?", + "Foundation" + ] + }, + { + id: 'horror_fan', + name: 'The Thrill Seeker', + emoji: '👻', + description: 'Obsessed with ghosts, psychological terror, and Stephen King.', + history: [ + "It", + "The Shining", + "Dracula", + "Pet Sematary", + "The Haunting of Hill House", + "Misery" + ] + }, + { + id: 'romance_fan', + name: 'The Hopeless Romantic', + emoji: '💘', + description: 'Enjoys period dramas, deep emotions, and happy endings.', + history: [ + "Pride and Prejudice", + "Jane Eyre", + "The Notebook", + "Sense and Sensibility", + "Me Before You", + "Outlander" + ] + }, + { + id: 'history_buff', + name: 'The Time Traveler', + emoji: '📜', + description: 'Fascinated by WWII, ancient civilizations, and biographies.', + history: [ + "The Diary of a Young Girl", + "The Guns of August", + "1776", + "Sapiens: A Brief History of Humankind", + "Alexander Hamilton" + ] + } +]; diff --git a/frontend/src/index.css b/frontend/src/index.css new file mode 100644 index 0000000000000000000000000000000000000000..1d2a0f8a99ed69dc068b451018131e2c7de69e1b --- /dev/null +++ b/frontend/src/index.css @@ -0,0 +1,136 @@ +@tailwind base; +@tailwind components; +@tailwind utilities; + +:root { + --font-serif: "Crimson Pro", serif; + --font-sans: "Inter", sans-serif; +} + +body { + font-family: var(--font-sans); +} + +.font-serif { + font-family: var(--font-serif); +} + +@layer utilities { + .animate-fade-in { + animation: fadeIn 0.5s ease-out forwards; + } + + .animate-slide-up { + animation: slideUp 0.5s ease-out forwards; + } + + .animation-delay-2000 { + animation-delay: 2s; + } + + .animation-delay-4000 { + animation-delay: 4s; + } + + .animation-delay-500 { + animation-delay: 500ms; + } + + /* Custom Scrollbar Utilities */ + .no-scrollbar::-webkit-scrollbar { + display: none; + } + .no-scrollbar { + -ms-overflow-style: none; + scrollbar-width: none; + } + + .bg-noise { + background-image: url("data:image/svg+xml,%3Csvg viewBox='0 0 200 200' xmlns='http://www.w3.org/2000/svg'%3E%3Cfilter id='noiseFilter'%3E%3CfeTurbulence type='fractalNoise' baseFrequency='0.65' numOctaves='3' stitchTiles='stitch'/%3E%3C/filter%3E%3Crect width='100%25' height='100%25' filter='url(%23noiseFilter)' opacity='0.4'/%3E%3C/svg%3E"); + } + + .bg-dot-pattern { + background-image: radial-gradient(circle, #6366f1 1.5px, transparent 1.5px); + background-size: 32px 32px; + opacity: 0.4; + } + .dark .bg-dot-pattern { + opacity: 0.2; + } +} + +/* Global Custom Scrollbar */ +::-webkit-scrollbar { + width: 8px; + height: 8px; +} + +::-webkit-scrollbar-track { + background: transparent; +} + +::-webkit-scrollbar-thumb { + background-color: #d4d4d8; /* zinc-300 */ + border-radius: 9999px; + border: 2px solid transparent; + background-clip: content-box; +} + +.dark ::-webkit-scrollbar-thumb { + background-color: #3f3f46; /* zinc-700 */ +} + +::-webkit-scrollbar-thumb:hover { + background-color: #a1a1aa; /* zinc-400 */ +} + +.dark ::-webkit-scrollbar-thumb:hover { + background-color: #52525b; /* zinc-600 */ +} + +@keyframes fadeIn { + from { opacity: 0; } + to { opacity: 1; } +} + +@keyframes slideUp { + from { + opacity: 0; + transform: translateY(20px); + } + to { + opacity: 1; + transform: translateY(0); + } +} + +@keyframes spin-slow { + from { transform: rotate(0deg); } + to { transform: rotate(360deg); } +} +.animate-spin-slow { + animation: spin-slow 8s linear infinite; +} + +@keyframes spin-reverse-slower { + from { transform: rotate(360deg); } + to { transform: rotate(0deg); } +} +.animate-spin-reverse-slower { + animation: spin-reverse-slower 12s linear infinite; +} + +@keyframes bounce-slight { + 0%, 100% { transform: translateY(0); } + 50% { transform: translateY(-5px); } +} +.animate-bounce-slight { + animation: bounce-slight 2s ease-in-out infinite; +} + +@keyframes ping-slow { + 75%, 100% { transform: scale(2); opacity: 0; } +} +.animate-ping-slow { + animation: ping-slow 3s cubic-bezier(0, 0, 0.2, 1) infinite; +} diff --git a/frontend/src/main.tsx b/frontend/src/main.tsx new file mode 100644 index 0000000000000000000000000000000000000000..bef5202a32cbd0632c43de40f6e908532903fd42 --- /dev/null +++ b/frontend/src/main.tsx @@ -0,0 +1,10 @@ +import { StrictMode } from 'react' +import { createRoot } from 'react-dom/client' +import './index.css' +import App from './App.tsx' + +createRoot(document.getElementById('root')!).render( + + + , +) diff --git a/frontend/src/types.ts b/frontend/src/types.ts new file mode 100644 index 0000000000000000000000000000000000000000..031b8c869a3fc85ef891f742a9a6dedeb87cd128 --- /dev/null +++ b/frontend/src/types.ts @@ -0,0 +1,25 @@ +export interface Book { + id: string; + title: string; + authors: string[]; + description?: string; + genres: string[]; + cover_image_url?: string; +} + +export interface RecommendationResult { + book: Book; + similarity_score: number; +} + +export interface RecommendByQueryRequest { + query: string; + top_k: number; +} + +export interface BookCluster { + id: number; + name: string; + size: number; + top_books: Book[]; +} diff --git a/frontend/tailwind.config.js b/frontend/tailwind.config.js new file mode 100644 index 0000000000000000000000000000000000000000..d3208282077bb5bb7ae4c7eb7141a3d35c3d3fa5 --- /dev/null +++ b/frontend/tailwind.config.js @@ -0,0 +1,12 @@ +/** @type {import('tailwindcss').Config} */ +export default { + darkMode: 'class', + content: [ + "./index.html", + "./src/**/*.{js,ts,jsx,tsx}", + ], + theme: { + extend: {}, + }, + plugins: [], +} \ No newline at end of file diff --git a/frontend/tsconfig.app.json b/frontend/tsconfig.app.json new file mode 100644 index 0000000000000000000000000000000000000000..a9b5a59ca647ca0102d2501b56cedd83ad7b6818 --- /dev/null +++ b/frontend/tsconfig.app.json @@ -0,0 +1,28 @@ +{ + "compilerOptions": { + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.app.tsbuildinfo", + "target": "ES2022", + "useDefineForClassFields": true, + "lib": ["ES2022", "DOM", "DOM.Iterable"], + "module": "ESNext", + "types": ["vite/client"], + "skipLibCheck": true, + + /* Bundler mode */ + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "verbatimModuleSyntax": true, + "moduleDetection": "force", + "noEmit": true, + "jsx": "react-jsx", + + /* Linting */ + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "erasableSyntaxOnly": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedSideEffectImports": true + }, + "include": ["src"] +} diff --git a/frontend/tsconfig.json b/frontend/tsconfig.json new file mode 100644 index 0000000000000000000000000000000000000000..1ffef600d959ec9e396d5a260bd3f5b927b2cef8 --- /dev/null +++ b/frontend/tsconfig.json @@ -0,0 +1,7 @@ +{ + "files": [], + "references": [ + { "path": "./tsconfig.app.json" }, + { "path": "./tsconfig.node.json" } + ] +} diff --git a/frontend/tsconfig.node.json b/frontend/tsconfig.node.json new file mode 100644 index 0000000000000000000000000000000000000000..8a67f62f4ceebff3424e6e8cd4b3c25b17347546 --- /dev/null +++ b/frontend/tsconfig.node.json @@ -0,0 +1,26 @@ +{ + "compilerOptions": { + "tsBuildInfoFile": "./node_modules/.tmp/tsconfig.node.tsbuildinfo", + "target": "ES2023", + "lib": ["ES2023"], + "module": "ESNext", + "types": ["node"], + "skipLibCheck": true, + + /* Bundler mode */ + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "verbatimModuleSyntax": true, + "moduleDetection": "force", + "noEmit": true, + + /* Linting */ + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "erasableSyntaxOnly": true, + "noFallthroughCasesInSwitch": true, + "noUncheckedSideEffectImports": true + }, + "include": ["vite.config.ts"] +} diff --git a/frontend/vite.config.ts b/frontend/vite.config.ts new file mode 100644 index 0000000000000000000000000000000000000000..0d267c2b082166214f89ba981db41445ea87e97a --- /dev/null +++ b/frontend/vite.config.ts @@ -0,0 +1,16 @@ +import { defineConfig } from 'vite' +import react from '@vitejs/plugin-react' + +// https://vite.dev/config/ +export default defineConfig({ + plugins: [react()], + build: { + rollupOptions: { + output: { + manualChunks: { + vendor: ['react', 'react-dom'], + }, + }, + }, + }, +}) diff --git a/logs/.gitkeep b/logs/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..b82d3c81e1315d58ac3fce931448480438132ebc --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,106 @@ +[project] +name = "deepshelf-engine" +version = "0.1.0" +description = "DeepShelf - Semantic Book Recommendation Engine" +readme = "README.md" +requires-python = ">=3.10" +license = {text = "MIT"} +authors = [ + {name = "Your Name", email = "your.email@example.com"} +] +keywords = [ + "machine-learning", + "nlp", + "recommendation-system", + "fastapi", + "streamlit", + "books" +] +classifiers = [ + "Development Status :: 4 - Beta", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Topic :: Scientific/Engineering :: Artificial Intelligence", +] + +dependencies = [ + "faiss-cpu>=1.13.0", + "numpy>=1.26.4", + "pandas>=2.3.3", + "pyarrow>=22.0.0", + "requests>=2.32.5", + "scikit-learn>=1.7.2", + "sentence-transformers==2.5.1", + "streamlit>=1.40.0", + "torch==2.2.2", + "transformers==4.38.2", + "fastapi>=0.104.1", + "uvicorn[standard]>=0.24.0", + "pydantic>=2.5.2", + "python-dotenv>=1.0.0", + "plotly>=5.18.0", + "slowapi>=0.1.9", + "pytest>=9.0.1", + "groq>=0.36.0", +] + +[project.optional-dependencies] +dev = [ + "pytest>=9.0.1", + "pytest-cov>=4.1.0", + "ruff>=0.1.0", + "black>=23.0.0", + "mypy>=1.7.0", + "safety>=2.3.0", + "pip-audit>=2.6.0", + "pre-commit>=3.5.0", +] + +[project.urls] +Homepage = "https://github.com/yourusername/bookfinder-ai" +Documentation = "https://github.com/yourusername/bookfinder-ai/tree/main/docs" +Repository = "https://github.com/yourusername/bookfinder-ai" +"Bug Tracker" = "https://github.com/yourusername/bookfinder-ai/issues" + +[project.scripts] +bookfinder-web = "src.book_recommender.apps.main_app:main" +bookfinder-api = "src.book_recommender.api.main:main" +bookfinder-analytics = "src.book_recommender.apps.analytics_app:main" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/book_recommender"] + + + + + +[tool.ruff] +line-length = 120 +target-version = "py310" + +[tool.ruff.lint] +select = ["E", "F", "W", "I"] +ignore = ["E203", "E501"] + +[tool.black] +line-length = 120 +target-version = ["py310", "py311", "py312"] + +[tool.mypy] +python_version = "3.10" +warn_return_any = true +warn_unused_configs = true +disallow_untyped_defs = false +ignore_missing_imports = true + +[[tool.mypy.overrides]] +module = "requests.*" +ignore_missing_imports = true diff --git a/requirements-dev.txt b/requirements-dev.txt new file mode 100644 index 0000000000000000000000000000000000000000..b9a1e331ea6a49fed377fb7fd441f65d84264ff1 --- /dev/null +++ b/requirements-dev.txt @@ -0,0 +1,9 @@ +# For running tests +pytest-cov + +# For UI testing (optional) +# streamlit-testing-library + +black +ruff +mypy diff --git a/requirements.backend.txt b/requirements.backend.txt new file mode 100644 index 0000000000000000000000000000000000000000..6f3999932ef93da6a8a67b9c805d410a9a50689f --- /dev/null +++ b/requirements.backend.txt @@ -0,0 +1,17 @@ +--extra-index-url https://download.pytorch.org/whl/cpu +torch==2.2.2 +sentence-transformers==2.5.1 +transformers==4.38.2 +pandas +numpy<2.0.0 +scikit-learn +pyarrow +faiss-cpu +requests +fastapi +uvicorn[standard] +pydantic +python-dotenv +slowapi +groq +huggingface_hub<1.0 \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..3a63c2084f58a6c3f64826d21e4f82c297b09bf1 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,19 @@ +--extra-index-url https://download.pytorch.org/whl/cpu +torch==2.2.2 +streamlit>=1.40.0 +sentence-transformers==2.5.1 +transformers==4.38.2 +pandas +numpy +scikit-learn +pytest +pyarrow +faiss-cpu +requests +fastapi +uvicorn[standard] +pydantic +python-dotenv +slowapi +groq +huggingface_hub diff --git a/scripts/download_data.py b/scripts/download_data.py new file mode 100644 index 0000000000000000000000000000000000000000..6e14abdcdbd9562bd6748a13f548b9fc53976f4d --- /dev/null +++ b/scripts/download_data.py @@ -0,0 +1,82 @@ +import logging +import os +import sys +from pathlib import Path + +from huggingface_hub import snapshot_download + +# Configure logging +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") +logger = logging.getLogger(__name__) + +# Define paths directly to avoid importing from src (which isn't copied yet in Docker build) +PROCESSED_DATA_DIR = Path("data/processed") +PROCESSED_DATA_PATH = PROCESSED_DATA_DIR / "books_with_embeddings.parquet" +EMBEDDINGS_PATH = PROCESSED_DATA_DIR / "embeddings.npy" +CLUSTERS_CACHE_PATH = PROCESSED_DATA_DIR / "clusters_cache.pkl" + +def download_processed_data(repo_id: str): + """ + Downloads processed data files (parquet, npy, pkl) from a private Hugging Face Dataset. + + Args: + repo_id (str): The Hugging Face dataset ID (e.g., 'username/dataset-name'). + """ + hf_token = os.getenv("HF_TOKEN") + if not hf_token: + logger.warning("HF_TOKEN environment variable not found. If the dataset is private, download will fail.") + + logger.info(f"Starting download from Hugging Face Dataset: {repo_id}") + logger.info(f"Target directory: {PROCESSED_DATA_DIR}") + + try: + # Ensure directory exists + PROCESSED_DATA_DIR.mkdir(parents=True, exist_ok=True) + + # Download only specific files to avoid clutter + allow_patterns = [ + "*.parquet", + "*.npy", + "*.pkl", + "*.json" + ] + + snapshot_download( + repo_id=repo_id, + repo_type="dataset", + local_dir=PROCESSED_DATA_DIR, + local_dir_use_symlinks=False, # Important for Docker/Deployment + allow_patterns=allow_patterns, + token=hf_token + ) + + logger.info("Successfully downloaded all data files.") + + # Verify files + expected_files = [ + PROCESSED_DATA_PATH, + EMBEDDINGS_PATH, + CLUSTERS_CACHE_PATH + ] + + missing = [f.name for f in expected_files if not f.exists()] + if missing: + logger.error(f"Warning: The following expected files are still missing after download: {missing}") + else: + logger.info("Verification successful: All core data files are present.") + + except Exception as e: + logger.error(f"Failed to download data from Hugging Face: {e}") + sys.exit(1) + +if __name__ == "__main__": + # Default repo ID - WILL BE OVERRIDDEN by environment variable in production + DEFAULT_REPO_ID = "nice-bill/book-recommender-data" + + repo_id = os.getenv("HF_DATASET_ID", DEFAULT_REPO_ID) + + if repo_id == "PLACEHOLDER_USERNAME/PLACEHOLDER_DATASET": + logger.error("Please set the HF_DATASET_ID environment variable or update the script.") + sys.exit(1) + + download_processed_data(repo_id) diff --git a/scripts/download_model.py b/scripts/download_model.py new file mode 100644 index 0000000000000000000000000000000000000000..e89e747a841575ca98d96c122c2f5cf6de13efab --- /dev/null +++ b/scripts/download_model.py @@ -0,0 +1,22 @@ +import os +import sys + +from sentence_transformers import SentenceTransformer + +# sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) +# from src.book_recommender.core import config + +MODEL_NAME = "all-MiniLM-L6-v2" + +def download_model(): + """ + Downloads the sentence-transformer model specified in the config file. + This is useful for pre-downloading the model in a Docker build. + """ + print(f"Downloading model: {MODEL_NAME}...") + _ = SentenceTransformer(MODEL_NAME) + print("Model downloaded successfully.") + + +if __name__ == "__main__": + download_model() diff --git a/scripts/enrich_book_covers.py b/scripts/enrich_book_covers.py new file mode 100644 index 0000000000000000000000000000000000000000..f36c2ccee60f34ae6475392c10d517432c50baff --- /dev/null +++ b/scripts/enrich_book_covers.py @@ -0,0 +1,81 @@ +import pandas as pd +import requests +import time +import logging +from pathlib import Path + +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +DATA_DIR = Path("data/raw") +INPUT_FILE = DATA_DIR / "books_prepared.csv" + +def get_openlibrary_cover(title, author): + try: + # Simple cleaning + clean_title = title.replace('&', '').split('(')[0].strip() + clean_author = author.split(',')[0].strip() if author else "" + + query = f"title={clean_title}&author={clean_author}" + url = f"https://openlibrary.org/search.json?{query}&limit=1" + + response = requests.get(url, timeout=5) + if response.status_code == 200: + data = response.json() + if data.get("docs"): + doc = data["docs"][0] + if "cover_i" in doc: + return f"https://covers.openlibrary.org/b/id/{doc['cover_i']}-L.jpg" + except Exception as e: + logger.warning(f"Error fetching cover for {title}: {e}") + return None + +def enrich_data(): + if not INPUT_FILE.exists(): + logger.error(f"File not found: {INPUT_FILE}") + return + + df = pd.read_csv(INPUT_FILE) + logger.info(f"Loaded {len(df)} books.") + + if "cover_image_url" not in df.columns: + df["cover_image_url"] = None + + # Filter for rows without covers + # We check for NaN or empty string + mask = df["cover_image_url"].isna() | (df["cover_image_url"] == "") + indices = df[mask].index + + logger.info(f"Found {len(indices)} books missing covers.") + + # Process a batch (e.g., 50) to demonstrate improvement without timeout + # The user can run this script repeatedly or increase limit + BATCH_SIZE = 20 + count = 0 + + for idx in indices: + if count >= BATCH_SIZE: + break + + row = df.loc[idx] + title = row['title'] + author = row['authors'] + + logger.info(f"[{count+1}/{BATCH_SIZE}] Fetching cover for: {title}") + cover_url = get_openlibrary_cover(title, author) + + if cover_url: + df.at[idx, 'cover_image_url'] = cover_url + logger.info(f" -> Found: {cover_url}") + else: + logger.info(" -> No cover found.") + + time.sleep(0.2) # Polite delay + count += 1 + + # Save back + df.to_csv(INPUT_FILE, index=False) + logger.info(f"Saved enriched data to {INPUT_FILE}") + +if __name__ == "__main__": + enrich_data() diff --git a/scripts/precompute_clusters.py b/scripts/precompute_clusters.py new file mode 100644 index 0000000000000000000000000000000000000000..8d2efb1661edce209f37a9761e56fdaf112b69e3 --- /dev/null +++ b/scripts/precompute_clusters.py @@ -0,0 +1,56 @@ +import logging +import os +import pickle +import sys +import numpy as np +import pandas as pd + +# Add project root +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) + +import src.book_recommender.core.config as config +from src.book_recommender.ml.clustering import cluster_books, get_cluster_names + +# Configure logging +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") +logger = logging.getLogger(__name__) + +def precompute_clusters(): + logger.info("--- Starting Cluster Pre-computation ---") + + # 1. Load Data + if not os.path.exists(config.PROCESSED_DATA_PATH) or not os.path.exists(config.EMBEDDINGS_PATH): + logger.error("Data files missing. Run data processor and embedder first.") + return + + logger.info(f"Loading book data from {config.PROCESSED_DATA_PATH}...") + book_data_df = pd.read_parquet(config.PROCESSED_DATA_PATH) + + logger.info(f"Loading embeddings from {config.EMBEDDINGS_PATH}...") + embeddings_arr = np.load(config.EMBEDDINGS_PATH) + + # 2. Cluster + n_clusters = config.NUM_CLUSTERS + logger.info(f"Clustering {len(book_data_df)} books into {n_clusters} clusters...") + + clusters_arr, _ = cluster_books(embeddings_arr, n_clusters=n_clusters) + + # 3. Name Clusters + book_data_df["cluster_id"] = clusters_arr + names = get_cluster_names(book_data_df, clusters_arr) + + # 4. Save Cache + cache_path = config.PROCESSED_DATA_DIR / "cluster_cache.pkl" + logger.info(f"Saving cache to {cache_path}...") + + try: + with open(cache_path, "wb") as f: + # Must match the tuple structure expected by api/dependencies.py + # (clusters_arr, names, book_data_df) + pickle.dump((clusters_arr, names, book_data_df), f) + logger.info("Successfully pre-computed and cached clusters.") + except Exception as e: + logger.error(f"Failed to save cache: {e}") + +if __name__ == "__main__": + precompute_clusters() diff --git a/scripts/prepare_100k_data.py b/scripts/prepare_100k_data.py new file mode 100644 index 0000000000000000000000000000000000000000..3e3acce9cc06db17372a7ee86374be43dad33c00 --- /dev/null +++ b/scripts/prepare_100k_data.py @@ -0,0 +1,71 @@ +import logging +import os +import sys +import pandas as pd + +# Setup logging +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") +logger = logging.getLogger(__name__) + +def prepare_100k_data(): + """ + Convert GoodReads_100k_books.csv format to the format expected by data_processor. + + Input Columns: author, bookformat, desc, genre, img, isbn, isbn13, link, pages, rating, reviews, title, totalratings + Target Columns: title, authors, genres, description, tags, rating, cover_image_url + """ + input_path = "data/raw/GoodReads_100k_books.csv" + output_path = "data/raw/books_prepared.csv" + + logger.info(f"Loading new 100k dataset from {input_path}...") + + if not os.path.exists(input_path): + logger.error(f"Input file not found: {input_path}") + return + + try: + # Load data + df = pd.read_csv(input_path) + logger.info(f"Loaded {len(df)} books.") + + # Rename columns + logger.info("Mapping columns...") + df = df.rename(columns={ + "author": "authors", + "desc": "description", + "genre": "genres", + "img": "cover_image_url", + "rating": "rating" + }) + + # Create tags column (using genres as base if available, else empty) + df["tags"] = df["genres"].fillna("") + + # Select and Reorder + target_cols = ["title", "authors", "genres", "description", "tags", "rating", "cover_image_url"] + + # Ensure all target columns exist + for col in target_cols: + if col not in df.columns: + df[col] = "" + logger.warning(f"Column {col} missing in source, filled with empty strings.") + + df = df[target_cols] + + # Clean up + logger.info("Cleaning data...") + # Remove rows with no title + df = df.dropna(subset=["title"]) + # Fill NaNs in text columns + df[["authors", "genres", "description", "cover_image_url"]] = df[["authors", "genres", "description", "cover_image_url"]].fillna("") + + logger.info(f"Saving prepared data to {output_path}...") + df.to_csv(output_path, index=False) + logger.info(f"Successfully prepared {len(df)} books.") + + except Exception as e: + logger.error(f"Error processing data: {e}") + raise + +if __name__ == "__main__": + prepare_100k_data() diff --git a/scripts/prepare_goodreads_data.py b/scripts/prepare_goodreads_data.py new file mode 100644 index 0000000000000000000000000000000000000000..6f63d354c2cf7c2f498c85b5371bf1244df2f3e1 --- /dev/null +++ b/scripts/prepare_goodreads_data.py @@ -0,0 +1,104 @@ +import logging +import os +import sys + +import pandas as pd + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + +logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") +logger = logging.getLogger(__name__) + + +def prepare_goodreads_data(): + """ + Convert Goodreads CSV format to the format expected by data_processor. + + Maps: + - Book → title + - Author → authors + - Avg_Rating → rating + - Description → description + - Genres → genres + - Creates 'tags' from genres + - Drops: Unnamed: 0, Num_Ratings, URL + """ + input_path = "data/raw/goodreads_data.csv" + output_path = "data/raw/books_prepared.csv" + + logger.info(f"Loading Goodreads data from {input_path}...") + + try: + df = pd.read_csv(input_path) + logger.info(f"Loaded {len(df)} books") + logger.info(f"Original columns: {df.columns.tolist()}") + + if "Unnamed: 0" in df.columns or "" in df.columns: + df = df.drop(columns=[col for col in df.columns if "Unnamed" in str(col) or col == ""]) + logger.info("Dropped unnamed index column") + + logger.info("Renaming columns...") + df = df.rename( + columns={ + "Book": "title", + "Author": "authors", + "Avg_Rating": "rating", + "Description": "description", + "Genres": "genres", + } + ) + + df["tags"] = "" + logger.info("Created 'tags' column (empty - can be populated later)") + + columns_to_keep = ["title", "authors", "genres", "description", "tags", "rating"] + + missing_cols = [col for col in columns_to_keep if col not in df.columns] + if missing_cols: + logger.error(f"Missing columns after mapping: {missing_cols}") + logger.error(f"Available columns: {df.columns.tolist()}") + raise ValueError(f"Column mapping failed. Missing: {missing_cols}") + + df = df[columns_to_keep] + + logger.info(f"Final shape: {df.shape}") + logger.info(f"Final columns: {df.columns.tolist()}") + logger.info(f"Sample row:\n{df.iloc[0]}") + + null_rows = df.isnull().all(axis=1).sum() + if null_rows > 0: + logger.warning(f"Found {null_rows} completely null rows - will be removed by processor") + + logger.info(f"Saving prepared data to {output_path}...") + df.to_csv(output_path, index=False) + logger.info(f" Successfully prepared {len(df)} books") + + logger.info("\n Dataset Summary:") + logger.info(f" Total books: {len(df)}") + logger.info(f" Books with ratings: {df['rating'].notna().sum()}") + logger.info(f" Books with descriptions: {(df['description'] != '').sum()}") + logger.info(f" Average rating: {df['rating'].mean():.2f}") + + print("\n Data preparation complete!") + print(f" Input: {input_path}") + print(f" Output: {output_path}") + print("\n Next steps:") + print(" 1. Update src/config.py line 17:") + print(" RAW_DATA_PATH = os.path.join(RAW_DATA_DIR, 'books_prepared.csv')") + print(" 2. Run: python src/data_processor.py") + print(" 3. Run: python src/embedder.py") + print(" 4. Run: streamlit run app.py") + + return df + + except FileNotFoundError: + logger.error(f"File not found: {input_path}") + logger.error("Make sure goodreads_data.csv exists in data/raw/") + raise + except Exception as e: + logger.error(f"Error processing data: {e}") + raise + + +if __name__ == "__main__": + prepare_goodreads_data() diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/book_recommender/__init__.py b/src/book_recommender/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..904ed5f427305362bafe6bc8f0e6dec852e0d425 --- /dev/null +++ b/src/book_recommender/__init__.py @@ -0,0 +1,23 @@ +"""BookFinder-AI - Semantic Book Recommendation Engine""" + +__version__ = "1.0.0" +__author__ = "Your Name" + +import os +import sys + +# Add the project root to the Python path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))) + + +# Expose main classes at package level +from src.book_recommender.ml.clustering import cluster_books, get_cluster_names +from src.book_recommender.ml.embedder import generate_embedding_for_query +from src.book_recommender.ml.recommender import BookRecommender + +__all__ = [ + "BookRecommender", + "generate_embedding_for_query", + "cluster_books", + "get_cluster_names", +] diff --git a/src/book_recommender/api/__init__.py b/src/book_recommender/api/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/book_recommender/api/dependencies.py b/src/book_recommender/api/dependencies.py new file mode 100644 index 0000000000000000000000000000000000000000..70ded1775a73a88a16ca1e23704dc8c5ac8d1d6b --- /dev/null +++ b/src/book_recommender/api/dependencies.py @@ -0,0 +1,108 @@ +import logging +import os +import pickle +import sys +from functools import lru_cache + +# Add the project root to the Python path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))) + +import numpy as np +import pandas as pd +from sentence_transformers import SentenceTransformer +from slowapi import Limiter +from slowapi.util import get_remote_address + +import src.book_recommender.core.config as config +from src.book_recommender.core.exceptions import DataNotFoundError +from src.book_recommender.ml.clustering import ( + cluster_books, + get_cluster_names, +) +from src.book_recommender.ml.embedder import ( + load_model as embedder_load_model, +) +from src.book_recommender.ml.recommender import BookRecommender + +logger = logging.getLogger(__name__) + +limiter = Limiter(key_func=get_remote_address, default_limits=["10/minute"]) + +CLUSTER_CACHE_PATH = config.PROCESSED_DATA_DIR / "cluster_cache.pkl" +MODEL_CACHE_PATH = config.PROCESSED_DATA_DIR / "model_cache" + + +@lru_cache(maxsize=1) +def get_recommender() -> BookRecommender: + """Load and cache BookRecommender (fast - uses cached files)""" + try: + logger.info("Loading book data and embeddings...") + book_data_df = pd.read_parquet(config.PROCESSED_DATA_PATH) + embeddings_arr = np.load(config.EMBEDDINGS_PATH) + + recommender = BookRecommender(book_data=book_data_df, embeddings=embeddings_arr) + logger.info(f"Recommender ready | {len(book_data_df)} books loaded") + return recommender + except FileNotFoundError as e: + logger.error(f"Data file not found: {e}") + raise DataNotFoundError( + f"Missing data files. Check '{config.PROCESSED_DATA_PATH}' and '{config.EMBEDDINGS_PATH}'" + ) + except Exception as e: + logger.error(f"Error initializing recommender: {e}") + raise + + +@lru_cache(maxsize=1) +def get_sentence_transformer_model() -> SentenceTransformer: + """ + Load model using the centralized, robust loader from embedder.py. + The loader handles checking for a local cache and downloading if missing. + """ + logger.info("Requesting embedding model...") + return embedder_load_model(config.EMBEDDING_MODEL) + + +@lru_cache(maxsize=1) +def get_clusters_data() -> tuple[np.ndarray, dict, pd.DataFrame]: + """ + Get clusters data with intelligent caching. + + Loads from cache if available, otherwise generates and caches. + """ + logger.info("Loading cluster data...") + + if CLUSTER_CACHE_PATH.exists(): + try: + cache_mtime = os.path.getmtime(CLUSTER_CACHE_PATH) + embeddings_mtime = os.path.getmtime(config.EMBEDDINGS_PATH) + + if cache_mtime > embeddings_mtime: + logger.info("Loading clusters from cache...") + with open(CLUSTER_CACHE_PATH, "rb") as f: + clusters_arr, names, book_data_df = pickle.load(f) + logger.info(f"Clusters loaded from cache | {len(names)} clusters") + return clusters_arr, names, book_data_df + else: + logger.info("Cache outdated, regenerating...") + except Exception as e: + logger.warning(f"Cache load failed: {e}, regenerating...") + + logger.info("Generating clusters (this takes ~30 seconds)...") + recommender = get_recommender() + book_data_df = recommender.book_data.copy() + embeddings_arr = recommender.embeddings + + clusters_arr, _ = cluster_books(embeddings_arr, n_clusters=config.NUM_CLUSTERS) + book_data_df["cluster_id"] = clusters_arr + names = get_cluster_names(book_data_df, clusters_arr) + + try: + with open(CLUSTER_CACHE_PATH, "wb") as f: + pickle.dump((clusters_arr, names, book_data_df), f) + logger.info(f"Clusters cached to {CLUSTER_CACHE_PATH}") + except Exception as e: + logger.warning(f"Failed to cache clusters: {e}") + + logger.info(f"Clusters ready | {len(names)} clusters generated") + return clusters_arr, names, book_data_df diff --git a/src/book_recommender/api/main.py b/src/book_recommender/api/main.py new file mode 100644 index 0000000000000000000000000000000000000000..e565183b5f495fcfd61bd4be1142c876e194ff23 --- /dev/null +++ b/src/book_recommender/api/main.py @@ -0,0 +1,758 @@ +import logging +import os +import sys +import time +import warnings +from contextlib import asynccontextmanager +from typing import Dict, List + +# Add the project root to the Python path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))) + +import numpy as np +import pandas as pd +from fastapi import Depends, FastAPI, HTTPException, Query, Request, status +from fastapi.middleware.cors import CORSMiddleware +from sentence_transformers import SentenceTransformer +from slowapi import _rate_limit_exceeded_handler +from slowapi.errors import RateLimitExceeded + +from src.book_recommender.api.dependencies import ( + get_clusters_data, + get_recommender, + get_sentence_transformer_model, + limiter, +) +from src.book_recommender.api.models import ( + Book, + BookCluster, + BookSearchResult, + BookStats, + ExplainRecommendationRequest, + ExplanationResponse, + FeedbackRequest, + FeedbackStatsResponse, + RecommendationResult, + RecommendByQueryRequest, + RecommendByTitleRequest, + RecommendByHistoryRequest +) +from src.book_recommender.core.exceptions import DataNotFoundError +from src.book_recommender.core.logging_config import configure_logging +from src.book_recommender.ml.explainability import explain_recommendation +from src.book_recommender.ml.feedback import get_all_feedback, save_feedback +from src.book_recommender.ml.recommender import BookRecommender +from src.book_recommender.utils import load_book_covers_batch +from src.book_recommender.services.personalizer import PersonalizationService + +personalizer = PersonalizationService() + +warnings.filterwarnings("ignore", message="resume_download is deprecated") +warnings.filterwarnings("ignore", category=FutureWarning, module="huggingface_hub") + +configure_logging(log_file="api.log", log_level=os.getenv("LOG_LEVEL", "INFO")) + +logger = logging.getLogger(__name__) + +IS_TESTING = os.getenv("TESTING_ENV", "False").lower() == "true" + + +def log_exception(e: Exception): + """Logs an exception with traceback if in DEBUG mode, otherwise logs a generic message.""" + if logger.isEnabledFor(logging.DEBUG): + logger.error(f"An unexpected error occurred: {e}", exc_info=True) + else: + logger.error("An unexpected error occurred.") + + +@asynccontextmanager +async def lifespan(app: FastAPI): + """Lifecycle manager with startup timing""" + if not IS_TESTING: + logger.info("=" * 30) + logger.info("Starting DeepShelf API...") + logger.info("=" * 30) + + start_time = time.time() + + try: + t0 = time.time() + get_recommender() + logger.info(f"Recommender loaded in {time.time() - t0:.1f}s") + + t0 = time.time() + get_sentence_transformer_model() + logger.info(f"Model loaded in {time.time() - t0:.1f}s") + + t0 = time.time() + get_clusters_data() + logger.info(f"Clusters loaded in {time.time() - t0:.1f}s") + + total_time = time.time() - start_time + port = os.getenv("PORT", "8000") + logger.info("=" * 30) + logger.info(f"API ready in {total_time:.1f}s | http://0.0.0.0:{port}") + logger.info("=" * 30) + + except Exception as e: + logger.error(f"Startup failed: {e}") + raise + else: + logger.info("Test mode - skipping model loading") + + yield + + logger.info("Shutting down DeepShelf API...") + + +app = FastAPI( + title="DeepShelf API", + description="API for content-based book recommendations and book management.", + version="0.1.0", + lifespan=lifespan, +) + +app.state.limiter = limiter +app.add_exception_handler(RateLimitExceeded, lambda request, exc: _rate_limit_exceeded_handler(request, exc)) + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + + +@app.middleware("http") +async def add_security_headers(request: Request, call_next): + response = await call_next(request) + response.headers["X-Content-Type-Options"] = "nosniff" + response.headers["X-Frame-Options"] = "DENY" + return response + + +@app.get( + "/health", + summary="Perform a health check", + response_description="Return HTTP Status Code 200 (OK)", +) +@limiter.limit("10/minute") +async def health_check(request: Request): + """ + Checks the health of the API and its core components. + """ + try: + _ = get_recommender() + + _ = get_sentence_transformer_model() + + get_clusters_data() + + return {"status": "OK", "message": "BookFinder API is healthy and core services are loaded."} + except Exception as e: + log_exception(e) + raise HTTPException( + status_code=status.HTTP_503_SERVICE_UNAVAILABLE, + detail=f"Core services (recommender/embedding model/clusters) are not available: {e}", + ) + + +@app.post( + "/recommend/query", + response_model=List[RecommendationResult], + summary="Get book recommendations based on a natural language query", +) +@limiter.limit("10/minute") +async def recommend_by_query( + request: Request, + body: RecommendByQueryRequest, + recommender: BookRecommender = Depends(get_recommender), + model: SentenceTransformer = Depends(get_sentence_transformer_model), +): + """ + Provides book recommendations by semantically comparing a natural language query + against the book embedding database. + """ + try: + query_embedding = model.encode(body.query, show_progress_bar=False) + recommendations = recommender.get_recommendations_from_vector(query_embedding, top_k=body.top_k) + + # Identify books with missing covers + books_needing_covers = [ + rec for rec in recommendations if not rec.get("cover_image_url") + ] + + # Fetch missing covers in batch + if books_needing_covers: + covers_map = load_book_covers_batch(books_needing_covers) + for rec in recommendations: + if not rec.get("cover_image_url"): + rec["cover_image_url"] = covers_map.get(rec["title"]) + + results = [] + for rec in recommendations: + book = Book( + id=str(rec["id"]), + title=rec["title"], + authors=(rec.get("authors", "").split(", ") if isinstance(rec.get("authors"), str) else []), + description=rec.get("description"), + genres=(rec.get("genres", "").split(", ") if isinstance(rec.get("genres"), str) else []), + cover_image_url=rec.get("cover_image_url"), + ) + results.append(RecommendationResult(book=book, similarity_score=rec["similarity"])) + return results + except DataNotFoundError as e: + raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(e)) + except Exception as e: + log_exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal server error during recommendation.", + ) + +@app.post( + "/recommend/personalize", + response_model=List[RecommendationResult], + summary="Get book recommendations based on user reading history", +) +@limiter.limit("10/minute") +async def recommend_personalized( + request: Request, + body: RecommendByHistoryRequest, + recommender: BookRecommender = Depends(get_recommender), +): + """ + Calls the external Personalization Engine (Port 8001) to get + semantic recommendations, then hydrates the results with local book metadata (cover, authors, etc). + """ + try: + semantic_recs = personalizer.get_recommendations(body.user_history,top_k=body.top_k) + if not semantic_recs: + return [] + results = [] + books_needing_covers = [] + for rec in semantic_recs: + # Try exact match first + mask = recommender.book_data['title'] == rec['title'] + local_book_df = recommender.book_data[mask] + + # Fallback to loose match + if local_book_df.empty: + mask = recommender.book_data['title'].str.lower().str.strip() == rec['title'].lower().strip() + local_book_df = recommender.book_data[mask] + + if not local_book_df.empty: + row = local_book_df.iloc[0] + + book = Book( + id=str(row["id"]), + title=row["title"], + authors=(row.get("authors", "").split(", ") if isinstance(row.get("authors"), str) else []), + description=row.get("description"), + genres=(row.get("genres", "").split(", ") if isinstance(row.get("genres"), str) else []), + cover_image_url=row.get("cover_image_url") + ) + if not book.cover_image_url: + books_needing_covers.append(dict(row)) + + results.append(RecommendationResult(book=book, similarity_score=rec["score"])) + + if books_needing_covers: + covers_map = load_book_covers_batch(books_needing_covers) + for rec in results: + if not rec.book.cover_image_url: + rec.book.cover_image_url = covers_map.get(rec.book.title) + + return results + except Exception as e: + log_exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal server error during personalization.", + ) + + +@app.post( + "/recommend/title", + response_model=List[RecommendationResult], + summary="Get similar books based on a book title", +) +@limiter.limit("10/minute") +async def recommend_by_title( + request: Request, + body: RecommendByTitleRequest, + recommender: BookRecommender = Depends(get_recommender), +): + """ + Provides recommendations for books similar to a given title. + """ + try: + recommendations = recommender.get_recommendations(body.title, top_k=body.top_k) + if not recommendations: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Book with title '{body.title}' not found or no recommendations met the similarity threshold.", + ) + + # Identify books with missing covers + books_needing_covers = [ + rec for rec in recommendations if not rec.get("cover_image_url") + ] + + # Fetch missing covers in batch + if books_needing_covers: + covers_map = load_book_covers_batch(books_needing_covers) + for rec in recommendations: + if not rec.get("cover_image_url"): + rec["cover_image_url"] = covers_map.get(rec["title"]) + + results = [] + for rec in recommendations: + book = Book( + id=str(rec["id"]), + title=rec["title"], + authors=(rec.get("authors", "").split(", ") if isinstance(rec.get("authors"), str) else []), + description=rec.get("description"), + genres=(rec.get("genres", "").split(", ") if isinstance(rec.get("genres"), str) else []), + cover_image_url=rec.get("cover_image_url"), + ) + results.append(RecommendationResult(book=book, similarity_score=rec["similarity"])) + return results + except DataNotFoundError as e: + raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=str(e)) + except HTTPException: + raise + except Exception as e: + log_exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal server error during recommendation.", + ) + + +@app.get( + "/books", + response_model=BookSearchResult, + summary="List all books with pagination", +) +@limiter.limit("10/minute") +async def list_books( + request: Request, + recommender: BookRecommender = Depends(get_recommender), + page: int = Query(1, ge=1, description="Page number"), + page_size: int = Query(10, ge=1, le=100, description="Number of items per page"), +): + """ + Retrieves a paginated list of all books in the catalog. + """ + try: + all_books_df = recommender.book_data + total_books = len(all_books_df) + + start_index = (page - 1) * page_size + end_index = start_index + page_size + paginated_books_df = all_books_df.iloc[start_index:end_index] + + books = [] + for _, rec in paginated_books_df.iterrows(): + book = Book( + id=str(rec["id"]), + title=rec["title"], + authors=(rec.get("authors", "").split(", ") if isinstance(rec.get("authors"), str) else []), + description=rec.get("description"), + genres=(rec.get("genres", "").split(", ") if isinstance(rec.get("genres"), str) else []), + cover_image_url=rec.get("cover_image_url"), + ) + books.append(book) + + return BookSearchResult(books=books, total=total_books, page=page, page_size=page_size) + except Exception as e: + log_exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal server error while listing books.", + ) + + +@app.get( + "/books/search", + response_model=BookSearchResult, + summary="Search books by title or author with pagination", +) +@limiter.limit("10/minute") +async def search_books( + request: Request, + recommender: BookRecommender = Depends(get_recommender), + query: str = Query( + ..., + min_length=2, + max_length=255, + description="Search query for title or author", + ), + page: int = Query(1, ge=1, description="Page number"), + page_size: int = Query(10, ge=1, le=100, description="Number of items per page"), +): + """ + Searches for books by matching the query against book titles or author names. + The search is case-insensitive. + """ + try: + sanitized_query = query.strip() + if not sanitized_query: + raise HTTPException( + status_code=status.HTTP_400_BAD_REQUEST, + detail="Query cannot be empty or just whitespace.", + ) + + all_books_df = recommender.book_data + + mask = (all_books_df["title_lower"].str.contains(sanitized_query.lower(), na=False)) | ( + all_books_df["authors_lower"].str.contains(sanitized_query.lower(), na=False) + ) + + filtered_books_df = all_books_df[mask] + total_books = len(filtered_books_df) + + start_index = (page - 1) * page_size + end_index = start_index + page_size + paginated_books_df = filtered_books_df.iloc[start_index:end_index] + + books = [] + for _, rec in paginated_books_df.iterrows(): + book = Book( + id=str(rec["id"]), + title=rec["title"], + authors=(rec.get("authors", "").split(", ") if isinstance(rec.get("authors"), str) else []), + description=rec.get("description"), + genres=(rec.get("genres", "").split(", ") if isinstance(rec.get("genres"), str) else []), + cover_image_url=rec.get("cover_image_url"), + ) + books.append(book) + + return BookSearchResult(books=books, total=total_books, page=page, page_size=page_size) + except Exception as e: + log_exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal server error while searching books.", + ) + + +@app.get( + "/stats", + response_model=BookStats, + summary="Get database statistics", +) +@limiter.limit("10/minute") +async def get_stats(request: Request, recommender: BookRecommender = Depends(get_recommender)): + """ + Provides various statistics about the book dataset, including total book count, + genre distribution, and author distribution. + """ + try: + all_books_df = recommender.book_data + total_books = len(all_books_df) + + all_genres = all_books_df["genres"].str.lower().str.split(", ").explode().dropna() + genres_count = all_genres.value_counts().to_dict() + + all_authors = all_books_df["authors"].str.lower().str.split(", ").explode().dropna() + authors_count = all_authors.value_counts().to_dict() + + return BookStats( + total_books=total_books, + genres_count=genres_count, + authors_count=authors_count, + ) + except Exception as e: + log_exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal server error while fetching statistics.", + ) + + +@app.get( + "/clusters", + response_model=List[BookCluster], + summary="List all book clusters", +) +@limiter.limit("10/minute") +async def list_clusters( + request: Request, + clusters_data: tuple[np.ndarray, dict, pd.DataFrame] = Depends(get_clusters_data), +): + """ + Retrieves a list of all identified book clusters, including their names, sizes, + and a sample of top books from each cluster. + """ + try: + clusters_arr, cluster_names, book_data_with_clusters = clusters_data + + all_clusters = [] + for cluster_id, name in cluster_names.items(): + cluster_books_df = book_data_with_clusters[book_data_with_clusters["cluster_id"] == cluster_id] + + sample_books = [] + if not cluster_books_df.empty: + sample_recs = cluster_books_df.sample(min(len(cluster_books_df), 3)).to_dict(orient="records") + for rec in sample_recs: + sample_books.append( + Book( + id=str(rec["id"]), + title=rec["title"], + authors=(rec.get("authors", "").split(", ") if isinstance(rec.get("authors"), str) else []), + description=rec.get("description"), + genres=(rec.get("genres", "").split(", ") if isinstance(rec.get("genres"), str) else []), + cover_image_url=rec.get("cover_image_url"), + ) + ) + + all_clusters.append( + BookCluster( + id=cluster_id, + name=name, + size=len(cluster_books_df), + top_books=sample_books, + ) + ) + + return all_clusters + except Exception as e: + log_exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal server error while listing clusters.", + ) + + +@app.get( + "/clusters/{cluster_id}", + response_model=BookSearchResult, + summary="Get books in a specific cluster with pagination", +) +@limiter.limit("10/minute") +async def get_books_in_cluster( + request: Request, + cluster_id: int, + clusters_data: tuple[np.ndarray, dict, pd.DataFrame] = Depends(get_clusters_data), + page: int = Query(1, ge=1, description="Page number"), + page_size: int = Query(10, ge=1, le=100, description="Number of items per page"), +): + """ + Retrieves a paginated list of books belonging to a specific cluster. + """ + try: + clusters_arr, cluster_names, book_data_with_clusters = clusters_data + + if cluster_id not in cluster_names: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Cluster with ID {cluster_id} not found.", + ) + + cluster_books_df = book_data_with_clusters[book_data_with_clusters["cluster_id"] == cluster_id] + total_books = len(cluster_books_df) + + start_index = (page - 1) * page_size + end_index = start_index + page_size + paginated_books_df = cluster_books_df.iloc[start_index:end_index] + + books = [] + for _, rec in paginated_books_df.iterrows(): + book = Book( + id=str(rec["id"]), + title=rec["title"], + authors=(rec.get("authors", "").split(", ") if isinstance(rec.get("authors"), str) else []), + description=rec.get("description"), + genres=(rec.get("genres", "").split(", ") if isinstance(rec.get("genres"), str) else []), + cover_image_url=rec.get("cover_image_url"), + ) + books.append(book) + + return BookSearchResult(books=books, total=total_books, page=page, page_size=page_size) + except HTTPException: + raise + except Exception as e: + log_exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal server error while fetching books in cluster.", + ) + + +@app.get( + "/clusters/{cluster_id}/sample", + response_model=List[Book], + summary="Get a random sample of books from a specific cluster", +) +@limiter.limit("10/minute") +async def get_cluster_sample( + request: Request, + cluster_id: int, + clusters_data: tuple[np.ndarray, dict, pd.DataFrame] = Depends(get_clusters_data), + sample_size: int = Query(5, ge=1, le=20, description="Number of sample books to return"), +): + """ + Retrieves a random sample of books from a specified cluster. + """ + try: + clusters_arr, cluster_names, book_data_with_clusters = clusters_data + + if cluster_id not in cluster_names: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Cluster with ID {cluster_id} not found.", + ) + + cluster_books_df = book_data_with_clusters[book_data_with_clusters["cluster_id"] == cluster_id] + + if cluster_books_df.empty: + return [] + + sample_df = cluster_books_df.sample(min(len(cluster_books_df), sample_size)) + + books = [] + for _, rec in sample_df.iterrows(): + book = Book( + id=str(rec["id"]), + title=rec["title"], + authors=(rec.get("authors", "").split(", ") if isinstance(rec.get("authors"), str) else []), + description=rec.get("description"), + genres=(rec.get("genres", "").split(", ") if isinstance(rec.get("genres"), str) else []), + cover_image_url=rec.get("cover_image_url"), + ) + books.append(book) + + return books + except HTTPException: + raise + except Exception as e: + log_exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal server error while fetching cluster sample.", + ) + + +@app.post( + "/explain", + response_model=ExplanationResponse, + summary="Get an explanation for a book recommendation", +) +@limiter.limit("10/minute") +async def explain_recommendation_endpoint(request: Request, body: ExplainRecommendationRequest): + """ + Generates a human-readable explanation for why a specific book was recommended + based on a user query and the book's attributes. + """ + try: + book_dict = body.recommended_book.model_dump() + + explanation = explain_recommendation( + query_text=body.query_text, recommended_book=book_dict, similarity_score=body.similarity_score + ) + return ExplanationResponse(**explanation) + except Exception as e: + log_exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal server error during explanation generation.", + ) + + +@app.post( + "/feedback", + status_code=status.HTTP_204_NO_CONTENT, + summary="Submit user feedback on a recommendation", +) +@limiter.limit("10/minute") +async def submit_feedback( + request: Request, + body: FeedbackRequest, + recommender: BookRecommender = Depends(get_recommender), +): + """ + Allows users to submit positive or negative feedback on a book recommendation. + """ + try: + book_details_df = recommender.book_data[recommender.book_data["id"] == body.book_id] + if book_details_df.empty: + raise HTTPException( + status_code=status.HTTP_404_NOT_FOUND, + detail=f"Book with ID {body.book_id} not found.", + ) + + book_details = book_details_df.iloc[0].to_dict() + + save_feedback( + query=body.query, + book_details=book_details, + feedback_type=body.feedback_type, + session_id=body.session_id, + ) + return {"message": "Feedback submitted successfully"} + except HTTPException: + raise + except Exception as e: + log_exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal server error while submitting feedback.", + ) + + +@app.get( + "/feedback/stats", + response_model=FeedbackStatsResponse, + summary="Get aggregate feedback statistics", +) +@limiter.limit("10/minute") +async def get_feedback_stats(request: Request): + """ + Retrieves aggregated statistics about the collected user feedback. + """ + try: + all_feedback = get_all_feedback() + + total_feedback = len(all_feedback) + positive_feedback = sum(1 for f in all_feedback if f["feedback"] == "positive") + negative_feedback = sum(1 for f in all_feedback if f["feedback"] == "negative") + + feedback_by_book_title: Dict[str, Dict[str, int]] = {} + feedback_by_query: Dict[str, Dict[str, int]] = {} + + for entry in all_feedback: + book_title = entry.get("book_title", "Unknown Book") + query = entry.get("query", "Unknown Query") + feedback_type = entry["feedback"] + + feedback_by_book_title.setdefault(book_title, {"positive": 0, "negative": 0})[feedback_type] += 1 + feedback_by_query.setdefault(query, {"positive": 0, "negative": 0})[feedback_type] += 1 + + return FeedbackStatsResponse( + total_feedback=total_feedback, + positive_feedback=positive_feedback, + negative_feedback=negative_feedback, + feedback_by_book_title=feedback_by_book_title, + feedback_by_query=feedback_by_query, + ) + except Exception as e: + log_exception(e) + raise HTTPException( + status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, + detail="Internal server error while fetching feedback statistics.", + ) + + +def main(): + """Entry point for uvicorn""" + import uvicorn + + port = int(os.getenv("PORT", "8000")) + uvicorn.run(app, host="0.0.0.0", port=port) + + +if __name__ == "__main__": + main() diff --git a/src/book_recommender/api/models.py b/src/book_recommender/api/models.py new file mode 100644 index 0000000000000000000000000000000000000000..95d4e75115d8a93e7a695479365d9e53aa8fb11b --- /dev/null +++ b/src/book_recommender/api/models.py @@ -0,0 +1,112 @@ +from typing import Dict, List, Optional, Annotated + +from pydantic import BaseModel, Field, BeforeValidator + + +def _strip_whitespace(v: Optional[str]) -> Optional[str]: + return v.strip() if v else v + + +StrippedString = Annotated[str, BeforeValidator(_strip_whitespace)] +OptionalStrippedString = Annotated[Optional[str], BeforeValidator(_strip_whitespace)] + + +class RecommendByQueryRequest(BaseModel): + query: StrippedString = Field( + ..., + min_length=1, + max_length=255, + json_schema_extra={"example": "science fiction about space travel"}, + ) + top_k: int = Field(5, gt=0, le=100, json_schema_extra={"example": 5}) + + +class RecommendByTitleRequest(BaseModel): + title: StrippedString = Field( + ..., + min_length=1, + max_length=255, + json_schema_extra={"example": "Dune"}, + ) + top_k: int = Field(5, gt=0, le=100, json_schema_extra={"example": 5}) + + +class Book(BaseModel): + id: str + title: str + authors: List[str] + description: Optional[str] = None + genres: List[str] + cover_image_url: Optional[str] = None + + +class RecommendationResult(BaseModel): + book: Book + similarity_score: float + + +class BookStats(BaseModel): + total_books: int + genres_count: dict + authors_count: dict + + +class BookSearchResult(BaseModel): + books: List[Book] + total: int + page: int + page_size: int + + +class BookCluster(BaseModel): + id: int + name: str + size: int + top_books: List[Book] + + +class ExplainRecommendationRequest(BaseModel): + query_text: str = Field(..., json_schema_extra={"example": "A fantasy novel with dragons"}) + recommended_book: Book + similarity_score: float = Field(..., ge=0.0, le=1.0) + + +class ExplanationResponse(BaseModel): + match_score: int + confidence: str + summary: str + details: Dict[str, int] + + +class FeedbackRequest(BaseModel): + query: StrippedString = Field( + ..., + min_length=1, + max_length=255, + json_schema_extra={"example": "books about dragons"}, + ) + book_id: StrippedString = Field( + ..., + min_length=1, + max_length=36, + json_schema_extra={"example": "b1b2b3b4-b5b6-b7b8-b9b0-b1b2b3b4b5b6"}, + ) + feedback_type: str = Field(..., pattern="^(positive|negative)$") + session_id: OptionalStrippedString = Field( + None, + min_length=1, + max_length=128, + json_schema_extra={"example": "user-session-12345"}, + ) + + +class FeedbackStatsResponse(BaseModel): + total_feedback: int + positive_feedback: int + negative_feedback: int + feedback_by_book_title: Dict[str, Dict[str, int]] + feedback_by_query: Dict[str, Dict[str, int]] + +class RecommendByHistoryRequest(BaseModel): + user_history: List[str] + top_k: int = 5 \ No newline at end of file diff --git a/src/book_recommender/apps/__init__.py b/src/book_recommender/apps/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/book_recommender/apps/analytics_app.py b/src/book_recommender/apps/analytics_app.py new file mode 100644 index 0000000000000000000000000000000000000000..e3f397dfe2429e096597819a061e47ad08c650d2 --- /dev/null +++ b/src/book_recommender/apps/analytics_app.py @@ -0,0 +1,139 @@ +import logging +import os + +import pandas as pd +import plotly.express as px +import streamlit as st +import sys +import os + +# Add the project root to the Python path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))) + +from src.book_recommender.core.logging_config import configure_logging +from src.book_recommender.ml.feedback import get_all_feedback + +configure_logging(log_file="analytics.log", log_level=os.getenv("LOG_LEVEL", "INFO")) +logger = logging.getLogger(__name__) + +st.set_page_config( + page_title="DeepShelf Analytics Dashboard", page_icon="📊", layout="wide", initial_sidebar_state="collapsed" +) + +st.title("DeepShelf Analytics Dashboard") + + +@st.cache_data +def load_feedback_data(): + """Load and process feedback data.""" + logger.info("Loading feedback data for analytics...") + feedback_entries = get_all_feedback() + if not feedback_entries: + logger.warning("No feedback data found.") + return pd.DataFrame() + + df = pd.DataFrame(feedback_entries) + df["timestamp"] = pd.to_datetime(df["timestamp"]) + df["date"] = df["timestamp"].dt.date + logger.info(f"Loaded {len(df)} feedback entries.") + return df + + +feedback_df = load_feedback_data() + +if feedback_df.empty: + st.info("No feedback data available yet to display analytics.") +else: + st.header("Key Metrics") + total_feedback = len(feedback_df) + positive_feedback = len(feedback_df[feedback_df["feedback"] == "positive"]) + negative_feedback = len(feedback_df[feedback_df["feedback"] == "negative"]) + satisfaction_percentage = (positive_feedback / total_feedback * 100) if total_feedback > 0 else 0 + + col1, col2, col3, col4 = st.columns(4) + with col1: + st.metric("Total Feedback", total_feedback) + with col2: + st.metric("Positive Feedback", positive_feedback) + with col3: + st.metric("Negative Feedback", negative_feedback) + with col4: + st.metric("Satisfaction Rate", f"{satisfaction_percentage:.1f}%") + + st.markdown("---") + + st.header("Feedback Over Time") + feedback_by_date = feedback_df.groupby(["date", "feedback"]).size().unstack(fill_value=0) + fig_time = px.line( + feedback_by_date, + x=feedback_by_date.index, + y=feedback_by_date.columns, + title="Feedback Count Over Time", + labels={"value": "Count", "date": "Date", "variable": "Feedback Type"}, + color_discrete_map={"positive": "green", "negative": "red"}, + ) + st.plotly_chart(fig_time, use_container_width=True) + + st.markdown("---") + + st.header("Top Queries") + top_queries = feedback_df["query"].value_counts().reset_index() + top_queries.columns = ["Query", "Count"] + fig_queries = px.bar( + top_queries.head(10), + x="Query", + y="Count", + title="Top 10 Most Frequent Queries", + labels={"Count": "Number of Times Queried"}, + color_discrete_sequence=px.colors.qualitative.Pastel, + ) + st.plotly_chart(fig_queries, use_container_width=True) + + st.markdown("---") + + st.header("Most Liked / Disliked Books") + + book_feedback_counts = feedback_df.groupby(["book_title", "feedback"]).size().unstack(fill_value=0) + book_feedback_counts["net_positive"] = book_feedback_counts.get("positive", 0) - book_feedback_counts.get( + "negative", 0 + ) + + most_liked_books = book_feedback_counts.sort_values(by="net_positive", ascending=False).head(5) + most_disliked_books = book_feedback_counts.sort_values(by="net_positive", ascending=True).head(5) + + col_liked, col_disliked = st.columns(2) + with col_liked: + st.subheader("Top 5 Most Liked Books (Net Positive)") + if not most_liked_books.empty: + st.dataframe( + most_liked_books[["positive", "negative", "net_positive"]].rename( + columns={"positive": "👍", "negative": "👎", "net_positive": "Net Score"} + ) + ) + else: + st.info("No liked book data.") + with col_disliked: + st.subheader("Top 5 Most Disliked Books (Net Negative)") + if not most_disliked_books.empty: + st.dataframe( + most_disliked_books[["positive", "negative", "net_positive"]].rename( + columns={"positive": "👍", "negative": "👎", "net_positive": "Net Score"} + ) + ) + else: + st.info("No disliked book data.") + + st.markdown("---") + + if st.checkbox("Show Raw Feedback Data"): + st.subheader("Raw Feedback Data") + st.dataframe(feedback_df) + + +def main(): + """Entry point for analytics dashboard""" + pass + + +if __name__ == "__main__": + main() diff --git a/src/book_recommender/apps/main_app.py b/src/book_recommender/apps/main_app.py new file mode 100644 index 0000000000000000000000000000000000000000..632f8c56d217de773659f31230031d2053dcb902 --- /dev/null +++ b/src/book_recommender/apps/main_app.py @@ -0,0 +1,988 @@ +import json +import logging +import os +import uuid +import pickle +from datetime import datetime +from typing import Optional +from contextlib import contextmanager +from pathlib import Path + +import numpy as np +import pandas as pd +import streamlit as st +import sys +from sentence_transformers import SentenceTransformer +from huggingface_hub import snapshot_download + +# Add the project root to the Python path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))) + +import src.book_recommender.core.config as config +from src.book_recommender.core.exceptions import ( + DataNotFoundError, +) +from src.book_recommender.core.logging_config import ( + configure_logging, +) +from src.book_recommender.data.processor import clean_and_prepare_data +from src.book_recommender.ml.clustering import ( + cluster_books, + get_cluster_names, +) +from src.book_recommender.ml.embedder import generate_embedding_for_query +from src.book_recommender.ml.explainability import explain_recommendation +from src.book_recommender.ml.feedback import save_feedback +from src.book_recommender.ml.recommender import BookRecommender +from src.book_recommender.utils import get_cover_url_multi_source, load_book_covers_batch + +configure_logging(log_file="app.log", log_level=os.getenv("LOG_LEVEL", "INFO")) +logger = logging.getLogger(__name__) +st.set_page_config( + page_title="DeepShelf Legacy Demo", page_icon="📚", layout="wide", initial_sidebar_state="collapsed" +) + +@st.cache_resource(show_spinner=False) +def ensure_data_available(): + """ + Checks if data exists. If not, downloads from Hugging Face. + Uses st.secrets or env vars for configuration. + """ + if ( + config.PROCESSED_DATA_PATH.exists() + and config.EMBEDDINGS_PATH.exists() + and config.CLUSTERS_CACHE_PATH.exists() + ): + return + + # Try getting ID from env var or Streamlit secrets + dataset_id = os.getenv("HF_DATASET_ID") + if not dataset_id and "HF_DATASET_ID" in st.secrets: + dataset_id = st.secrets["HF_DATASET_ID"] + + if not dataset_id: + st.warning("HF_DATASET_ID not found. Using local data generation (slow). Set HF_DATASET_ID to download pre-computed data.") + return + + try: + with st.spinner(f"Downloading 100MB+ dataset from Hugging Face ({dataset_id})... this happens only once!"): + config.PROCESSED_DATA_DIR.mkdir(parents=True, exist_ok=True) + + snapshot_download( + repo_id=dataset_id, + repo_type="dataset", + local_dir=config.PROCESSED_DATA_DIR, + local_dir_use_symlinks=False, + allow_patterns=["*.parquet", "*.npy", "*.pkl", "*.json"], + token=os.getenv("HF_TOKEN") # Optional for public datasets + ) + st.success("Data downloaded successfully!") + except Exception as e: + st.error(f"Failed to download data: {e}") + logger.error(f"Download failed: {e}") + + +@st.cache_resource(show_spinner=False) +def load_embedding_model() -> SentenceTransformer: + """ + Load and cache the sentence-transformer model. + This ensures the model is loaded only once per session/process. + """ + logger.info(f"Loading embedding model: {config.EMBEDDING_MODEL}") + return SentenceTransformer(config.EMBEDDING_MODEL) + + +@contextmanager +def custom_spinner(text="Loading..."): + """ + A custom spinner that uses CSS/HTML for a better loading animation. + """ + placeholder = st.empty() + placeholder.markdown( + f""" +
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{text}

+
+ + """, + unsafe_allow_html=True, + ) + try: + yield + finally: + placeholder.empty() + + +st.markdown( + """ + +""", + unsafe_allow_html=True, +) + + +@st.cache_resource(show_spinner=False) +def load_recommender() -> BookRecommender: + """Load recommender without cluttering UI with status messages.""" + files_exist = ( + os.path.exists(config.PROCESSED_DATA_PATH) + and os.path.exists(config.EMBEDDINGS_PATH) + and os.path.exists(config.EMBEDDING_METADATA_PATH) + ) + + model_changed = False + if files_exist: + with open(config.EMBEDDING_METADATA_PATH, "r", encoding="utf-8") as f: + metadata = json.load(f) + if metadata.get("model_name") != config.EMBEDDING_MODEL: + model_changed = True + + if files_exist and not model_changed: + book_data = pd.read_parquet(config.PROCESSED_DATA_PATH) + embeddings = np.load(config.EMBEDDINGS_PATH) + else: + from src.book_recommender.ml.embedder import generate_embeddings + + if not os.path.exists(config.RAW_DATA_PATH): + raise DataNotFoundError(f"Raw data file not found at: {config.RAW_DATA_PATH}") + + book_data = clean_and_prepare_data(str(config.RAW_DATA_PATH), str(config.PROCESSED_DATA_PATH)) + embeddings = generate_embeddings(book_data, model_name=config.EMBEDDING_MODEL, show_progress_bar=False) + np.save(config.EMBEDDINGS_PATH, embeddings) + + metadata = {"model_name": config.EMBEDDING_MODEL} + with open(config.EMBEDDING_METADATA_PATH, "w", encoding="utf-8") as f: + json.dump(metadata, f) + + recommender = BookRecommender(book_data=book_data, embeddings=embeddings) + return recommender + + +@st.cache_resource(show_spinner=False) +def load_cluster_data() -> tuple[np.ndarray, dict, pd.DataFrame]: + """ + Generates and returns cached book clusters, cluster names, and book data with cluster IDs. + Tries to load from disk first, then falls back to computation and saves to disk. + """ + logger.info("Generating/Loading cluster data for Streamlit app...") + recommender = load_recommender() + book_data_df = recommender.book_data.copy() + + # Try loading from disk + if os.path.exists(config.CLUSTERS_CACHE_PATH): + try: + logger.info(f"Loading cached clusters from {config.CLUSTERS_CACHE_PATH}") + with open(config.CLUSTERS_CACHE_PATH, "rb") as f: + cached_data = pickle.load(f) + + clusters_arr = cached_data["clusters_arr"] + names = cached_data["names"] + + # Verify consistency + if len(clusters_arr) == len(book_data_df): + book_data_df["cluster_id"] = clusters_arr + logger.info("Cluster data loaded from disk cache.") + return clusters_arr, names, book_data_df + else: + logger.warning("Cached cluster data size mismatch. Recomputing...") + except Exception as e: + logger.error(f"Failed to load cluster cache: {e}. Recomputing...") + + # Recompute if not found or failed + embeddings_arr = recommender.embeddings + clusters_arr, _ = cluster_books(embeddings_arr, n_clusters=config.NUM_CLUSTERS) + book_data_df["cluster_id"] = clusters_arr + + names = get_cluster_names(book_data_df, clusters_arr) + + # Save to disk + try: + with open(config.CLUSTERS_CACHE_PATH, "wb") as f: + pickle.dump({"clusters_arr": clusters_arr, "names": names}, f) + logger.info(f"Cluster data saved to {config.CLUSTERS_CACHE_PATH}") + except Exception as e: + logger.error(f"Failed to save cluster cache: {e}") + + logger.info("Cluster data generated/loaded and cached for Streamlit app.") + return clusters_arr, names, book_data_df + + +def render_header(): + """Modern header with gradient background.""" + st.markdown( + """ +
+

📚 DeepShelf Legacy Demo

+

Explore the power of semantic search

+
+ """, + unsafe_allow_html=True, + ) + + +def render_search_section(): + """Enhanced search interface with examples.""" + st.markdown('
', unsafe_allow_html=True) + + st.markdown("### What kind of book are you looking for?") + + col1, col2, col3, col4, col5 = st.columns([1, 2, 2, 2, 1]) + with col2: + if st.button("Fantasy", key="example1", width="stretch"): + st.session_state.query = "A fantasy adventure with magic and dragons" + with col3: + if st.button("Mystery", key="example2", width="stretch"): + st.session_state.query = "A psychological thriller with unexpected twists" + with col4: + if st.button("Romance", key="example3", width="stretch"): + st.session_state.query = "A heartwarming romance set in a small town" + + # Main search input + query = st.text_area( + "Describe the book you want", + value=st.session_state.get("query", ""), + height=100, + placeholder=( + "Example: A science fiction novel about time travel with " "complex characters and philosophical themes..." + ), + label_visibility="collapsed", + ) + + col1, col2, col3 = st.columns([1, 2, 1]) + with col2: + search_button = st.button("Find My Perfect Books", type="primary", width="stretch") + + st.markdown("
", unsafe_allow_html=True) + + return query, search_button + + +def render_book_card(rec, index, cover_url, query_text: Optional[str] = None): + """Render a beautiful book card with all details.""" + + # Generate stars string + rating_html = '
 
' + if rec.get("rating") and rec.get("rating") != "N/A" and pd.notna(rec["rating"]): + try: + rating = float(rec["rating"]) + stars = "⭐" * int(rating) + rating_html = f'
{stars} {rating:.1f}/5
' + except Exception: + pass + + # Generate genres HTML + genres_html = '
 
' + if rec.get("genres") and isinstance(rec.get("genres"), str): + genres = [g.strip() for g in rec["genres"].split(",")[:3]] + pills = "".join([f'{g.title()}' for g in genres]) + genres_html = f'
{pills}
' + + # Generate match badge + match_html = "" + if "similarity" in rec: + similarity_percent = rec["similarity"] * 100 + match_html = f'
{similarity_percent:.0f}% Match
' + + # Single HTML block for the card content + card_html = f""" +
+
+ {rec['title']} +
+
+ {match_html} +
{rec['title']}
+
by {rec.get("authors", "Unknown Author")}
+ {rating_html} + {genres_html} +
+
+ """ + st.markdown(card_html, unsafe_allow_html=True) + + # Interactive elements (outside the HTML block) + with st.container(): + if rec.get("description"): + with st.expander("Read More"): + desc = rec["description"][:250] + "..." if len(rec["description"]) > 250 else rec["description"] + st.write(desc) + + if query_text and "similarity" in rec: + with st.expander("Why this recommendation?"): + from src.book_recommender.ml.explainability import ( + explain_recommendation, + ) + explanation = explain_recommendation( + query_text=query_text, recommended_book=rec, similarity_score=rec["similarity"] + ) + st.info(explanation["summary"]) + if explanation.get("matching_features"): + st.markdown("**Matching features:**") + for feature in explanation["matching_features"]: + st.markdown(f"• {feature}") + + st.markdown('
', unsafe_allow_html=True) + if query_text: + col_a, col_b, col_c = st.columns([1, 1, 3]) + with col_a: + if st.button("👍", key=f"like_{index}", help="Good recommendation", use_container_width=True): + save_feedback(query_text, rec, "positive", st.session_state.session_id) + st.toast(f"Feedback saved!", icon="👍") + with col_b: + if st.button("👎", key=f"dislike_{index}", help="Not relevant", use_container_width=True): + save_feedback(query_text, rec, "negative", st.session_state.session_id) + st.toast(f"Feedback saved!", icon="👎") + with col_c: + if st.button("View Details", key=f"details_{index}", use_container_width=True): + show_book_details(rec, query_text=query_text) + else: + if st.button("View Details", key=f"details_{index}", use_container_width=True): + show_book_details(rec, query_text=query_text) + st.markdown('
', unsafe_allow_html=True) + + +@st.dialog("Book Details") +def show_book_details(book, query_text: Optional[str] = None): + col1, col2 = st.columns([1, 2]) + + with col1: + st.image(get_cover_url_multi_source(book["title"], book.get("authors", ""))) + + with col2: + st.markdown(f"## {book['title']}") + st.markdown(f"**by {book.get('authors', 'Unknown Author')}**") + + if query_text and "similarity" in book: + explanation = explain_recommendation( + query_text=query_text, recommended_book=book, similarity_score=book["similarity"] + ) + st.markdown(f"**Match Score:** {explanation['match_score']}% (Confidence: {explanation['confidence']})") + st.write(explanation["summary"]) + st.divider() + + similarity_percent = book["similarity"] * 100 + st.markdown(f"**{similarity_percent:.0f}% Match**") + + if book.get("rating") and pd.notna(book["rating"]): + try: + rating = float(book["rating"]) + stars = "⭐" * int(rating) + st.markdown(f"{stars} **{rating:.1f}/5**") + except Exception: + pass + + if book.get("genres"): + st.markdown(f"**Genres:** {book['genres']}") + + st.divider() + + if book.get("description"): + st.markdown("### Description") + st.write(book["description"]) + + st.divider() + col_a, col_b = st.columns(2) + with col_a: + st.link_button( + "Search on Google", f"https://www.google.com/search?q={book['title']}+{book.get('authors', '')}" + ) + with col_b: + st.link_button("View on Goodreads", f"https://www.goodreads.com/search?q={book['title']}") + + +def render_sidebar(recommender): + """Renders the sidebar with filters and search history.""" + with st.sidebar: + st.markdown("## Recent Searches") + if not st.session_state.search_history: + st.info("Your recent searches will appear here.") + else: + for item in reversed(st.session_state.search_history): + if st.button(f"'{item['query'][:20]}...' ({item['results_count']} found)", key=item["timestamp"]): + st.session_state.query = item["query"] + st.rerun() + + st.markdown("---") + st.markdown("## Filters") + + if "recommendations" in st.session_state and st.session_state.recommendations: + st.session_state.min_rating = st.slider( + "Minimum Rating", 0.0, 5.0, st.session_state.get("min_rating", 0.0), 0.5 + ) + + all_genres = set() + for rec in st.session_state.recommendations: + if rec.get("genres"): + genres = [g.strip() for g in rec["genres"].split(",")] + all_genres.update(genres) + + st.session_state.selected_genres = st.multiselect( + "Genres", sorted(list(all_genres)), default=st.session_state.get("selected_genres", []) + ) + else: + st.info("Perform a search to see filters.") + + +def main(): + """Main application with UI.""" + # Ensure data is present before doing anything else + ensure_data_available() + + render_header() + + if "query" not in st.session_state: + st.session_state.query = "" + if "search_history" not in st.session_state: + st.session_state.search_history = [] + if "recommendations" not in st.session_state: + st.session_state.recommendations = [] + if "session_id" not in st.session_state: + st.session_state.session_id = str(uuid.uuid4()) + + try: + recommender = load_recommender() + render_sidebar(recommender) + + tab1, tab2 = st.tabs(["Search", "Browse Collections"]) + + with tab1: + query, search_button = render_search_section() + + if (search_button and query.strip()) or (query and query != st.session_state.get("last_query", "")): + st.session_state.last_query = query + with custom_spinner("Finding the perfect books for you..."): + # Use the cached model instance + embedding_model = load_embedding_model() + query_embedding = generate_embedding_for_query(query, model=embedding_model) + + st.session_state.recommendations = recommender.get_recommendations_from_vector( + query_embedding, + top_k=10, + similarity_threshold=0.25, + ) + + if st.session_state.recommendations: + st.session_state.search_history.append( + { + "query": query, + "timestamp": datetime.now(), + "results_count": len(st.session_state.recommendations), + } + ) + st.session_state.search_history = st.session_state.search_history[-5:] + st.rerun() + else: + st.warning( + "No books found matching your description. Try being more specific or use different keywords!" + ) + st.session_state.recommendations = [] + + elif search_button: + st.warning("Please describe what kind of book you're looking for!") + + if st.session_state.recommendations: + filtered = st.session_state.recommendations + min_rating = st.session_state.get("min_rating", 0.0) + selected_genres = st.session_state.get("selected_genres", []) + + if min_rating > 0.0: + filtered = [ + rec + for rec in filtered + if rec.get("rating") and pd.notna(rec["rating"]) and float(rec.get("rating", 0)) >= min_rating + ] + + if selected_genres: + filtered = [ + rec + for rec in filtered + if rec.get("genres") and any(g in rec.get("genres", "") for g in selected_genres) + ] + + st.markdown( + f'
Found {len(filtered)} Perfect Books For You
', + unsafe_allow_html=True, + ) + + if not filtered: + st.info("No books match your current filters. Try adjusting them!") + else: + visible_recs = filtered[:12] + with custom_spinner("Loading book covers..."): + covers_dict = load_book_covers_batch(visible_recs) + + for row_idx in range(0, min(len(filtered), 12), 4): + cols = st.columns(4, gap="medium") + + for col_idx, rec in enumerate(filtered[row_idx : row_idx + 4]): + with cols[col_idx]: + unique_idx = row_idx + col_idx + cover_url = covers_dict.get(rec["title"], config.FALLBACK_COVER_URL) + render_book_card(rec, unique_idx, cover_url, query_text=query) + + with tab2: + st.markdown('
', unsafe_allow_html=True) + st.markdown("### Browse Books by Collection") + + clusters_arr, cluster_names, book_data_with_clusters = load_cluster_data() + + cluster_options = [ + f"{name} ({np.sum(clusters_arr == cluster_id)} books)" for cluster_id, name in cluster_names.items() + ] + + selected_cluster_option = st.selectbox("Select a Collection", options=cluster_options, index=0) + + selected_cluster_id = int(list(cluster_names.keys())[cluster_options.index(selected_cluster_option)]) + + cluster_books_df = book_data_with_clusters[book_data_with_clusters["cluster_id"] == selected_cluster_id] + + st.markdown( + f'
Books in {cluster_names[selected_cluster_id]}
', + unsafe_allow_html=True, + ) + + if not cluster_books_df.empty: + cluster_recs = cluster_books_df.to_dict(orient="records") + + visible_recs = cluster_recs[:12] + with custom_spinner("Loading book covers..."): + covers_dict = load_book_covers_batch(visible_recs) + + for row_idx in range(0, min(len(cluster_recs), 12), 4): + cols = st.columns(4, gap="medium") + + for col_idx, rec in enumerate(cluster_recs[row_idx : row_idx + 4]): + with cols[col_idx]: + unique_idx = f"cluster_{selected_cluster_id}_{row_idx + col_idx}" + cover_url = covers_dict.get(rec["title"], config.FALLBACK_COVER_URL) + render_book_card({**rec, "similarity": 1.0}, unique_idx, cover_url) + + else: + st.info("No books found in this collection.") + st.markdown("
", unsafe_allow_html=True) + + except Exception as e: + st.error(f"Something went wrong: {str(e)}") + logger.error(f"Application error: {e}", exc_info=True) + + +if __name__ == "__main__": + main() diff --git a/src/book_recommender/apps/main_appV2.py b/src/book_recommender/apps/main_appV2.py new file mode 100644 index 0000000000000000000000000000000000000000..222e6d7f40532bb79f77d0d76635b6e2290593dd --- /dev/null +++ b/src/book_recommender/apps/main_appV2.py @@ -0,0 +1,441 @@ +import json +import logging +import os +import uuid +from datetime import datetime +from typing import Optional + +import numpy as np +import pandas as pd +import streamlit as st +import sys + +# --- PATH SETUP --- +# Add the project root to the Python path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))) + +# --- MODULE IMPORTS --- +# (Assumed to exist based on your previous code) +import src.book_recommender.core.config as config +from src.book_recommender.core.exceptions import DataNotFoundError +from src.book_recommender.core.logging_config import configure_logging +from src.book_recommender.data.processor import clean_and_prepare_data +from src.book_recommender.ml.clustering import cluster_books, get_cluster_names +from src.book_recommender.ml.embedder import generate_embedding_for_query +from src.book_recommender.ml.explainability import explain_recommendation +from src.book_recommender.ml.feedback import save_feedback +from src.book_recommender.ml.recommender import BookRecommender +from src.book_recommender.utils import get_cover_url_multi_source, load_book_covers_batch + +# --- CONFIGURATION --- +configure_logging(log_file="app.log", log_level=os.getenv("LOG_LEVEL", "INFO")) +logger = logging.getLogger(__name__) + +st.set_page_config( + page_title="DeepShelf Demo", + page_icon="📚", + layout="wide", + initial_sidebar_state="collapsed" +) + +# --- MODERN CSS (V2) --- +st.markdown( + """ + + """, + unsafe_allow_html=True, +) + +# --- BACKEND FUNCTIONS (Cached) --- + +@st.cache_resource(show_spinner=False) +def load_recommender() -> BookRecommender: + """Load recommender efficiently.""" + files_exist = ( + os.path.exists(config.PROCESSED_DATA_PATH) + and os.path.exists(config.EMBEDDINGS_PATH) + and os.path.exists(config.EMBEDDING_METADATA_PATH) + ) + + model_changed = False + if files_exist: + with open(config.EMBEDDING_METADATA_PATH, "r", encoding="utf-8") as f: + metadata = json.load(f) + if metadata.get("model_name") != config.EMBEDDING_MODEL: + model_changed = True + + if files_exist and not model_changed: + book_data = pd.read_parquet(config.PROCESSED_DATA_PATH) + embeddings = np.load(config.EMBEDDINGS_PATH) + else: + # Fallback to generation if files missing (usually dev env) + from src.book_recommender.ml.embedder import generate_embeddings + if not os.path.exists(config.RAW_DATA_PATH): + raise DataNotFoundError(f"Raw data file not found at: {config.RAW_DATA_PATH}") + + book_data = clean_and_prepare_data(str(config.RAW_DATA_PATH), str(config.PROCESSED_DATA_PATH)) + embeddings = generate_embeddings(book_data, model_name=config.EMBEDDING_MODEL, show_progress_bar=False) + np.save(config.EMBEDDINGS_PATH, embeddings) + + metadata = {"model_name": config.EMBEDDING_MODEL} + with open(config.EMBEDDING_METADATA_PATH, "w", encoding="utf-8") as f: + json.dump(metadata, f) + + return BookRecommender(book_data=book_data, embeddings=embeddings) + +@st.cache_resource(show_spinner=False) +def load_cluster_data() -> tuple[np.ndarray, dict, pd.DataFrame]: + """Load cluster data for the 'Browse' tab.""" + recommender = load_recommender() + book_data_df = recommender.book_data.copy() + embeddings_arr = recommender.embeddings + clusters_arr, _ = cluster_books(embeddings_arr, n_clusters=config.NUM_CLUSTERS) + book_data_df["cluster_id"] = clusters_arr + names = get_cluster_names(book_data_df, clusters_arr) + return clusters_arr, names, book_data_df + +# --- UI COMPONENT FUNCTIONS --- + +def render_hero(): + """Renders the top Hero section.""" + st.markdown(""" +
+

📚 DeepShelf Demo

+

Describe what you're craving. We'll handle the rest.

+
+ """, unsafe_allow_html=True) + +def render_book_card_content(rec, cover_url): + """ + Renders the HTML content of the card (Image + Text). + Buttons are handled outside by Streamlit to maintain functionality. + """ + + # Process Genres (Limit to 2 for space) + genres_html = "" + if rec.get("genres") and isinstance(rec.get("genres"), str): + genres = [g.strip() for g in rec["genres"].split(",")[:2]] + for g in genres: + genres_html += f'{g.title()}' + + # Process Similarity + match_badge = "" + if "similarity" in rec: + pct = int(rec["similarity"] * 100) + match_badge = f'
{pct}% Match
' + + # Rating + rating_display = "" + if rec.get("rating") and pd.notna(rec["rating"]): + try: + r = float(rec["rating"]) + rating_display = f'★ {r:.1f}' + except: pass + + # HTML Structure + html = f""" +
+
+ +
+ {match_badge} +
{rec['title']}
+
by {rec.get('authors', 'Unknown')}
+
+
{rating_display}
+
+
+ {genres_html} +
+
+ """ + st.markdown(html, unsafe_allow_html=True) + +@st.dialog("📖 Book Details") +def show_book_details(book, query_text: Optional[str] = None): + """Modal for book details.""" + + # Header + st.subheader(book['title']) + st.caption(f"by {book.get('authors', 'Unknown Author')}") + + col1, col2 = st.columns([1, 2], gap="large") + + with col1: + st.image(get_cover_url_multi_source(book["title"], book.get("authors", "")), use_container_width=True) + + # Metadata pills + if book.get("rating"): + st.metric("Rating", f"{float(book['rating']):.1f}/5") + + with col2: + # AI Explanation + if query_text and "similarity" in book: + with st.spinner("Analyzing match..."): + explanation = explain_recommendation( + query_text=query_text, + recommended_book=book, + similarity_score=book["similarity"] + ) + st.markdown(f"**Why this matches:**") + st.info(explanation["summary"]) + + # Description + st.markdown("**Synopsis:**") + st.write(book.get("description", "No description available.")) + + st.divider() + + # External Links + c1, c2 = st.columns(2) + c1.link_button("Google Search", f"https://www.google.com/search?q={book['title']}+{book.get('authors', '')}", use_container_width=True) + c2.link_button("Goodreads", f"https://www.goodreads.com/search?q={book['title']}", use_container_width=True) + + +# --- MAIN APP LOGIC --- + +def main(): + # Session State Init + if "query" not in st.session_state: st.session_state.query = "" + if "recommendations" not in st.session_state: st.session_state.recommendations = [] + if "session_id" not in st.session_state: st.session_state.session_id = str(uuid.uuid4()) + + try: + recommender = load_recommender() + + # --- STICKY HERO & SEARCH --- + # We put this in a container to act as our sticky header + with st.container(): + render_hero() + + # Search Input Area + c1, c2, c3 = st.columns([1, 6, 1]) + with c2: + # Suggestion Chips + s1, s2, s3 = st.columns(3) + if s1.button("🧙‍♂️ Fantasy Dragons", use_container_width=True): st.session_state.query = "Epic fantasy with dragons and magic" + if s2.button("🕵️‍♀️ Murder Mystery", use_container_width=True): st.session_state.query = "Whodunnit mystery thriller" + if s3.button("🚀 Sci-Fi Space", use_container_width=True): st.session_state.query = "Hard science fiction in space" + + query_input = st.text_area( + "Search", + value=st.session_state.query, + placeholder="Describe the vibe: 'A melancholic book about growing up in the 90s...'", + height=80, + label_visibility="collapsed" + ) + + search_clicked = st.button("✨ Find Recommendations", type="primary", use_container_width=True) + + # --- LOGIC HANDLER --- + if search_clicked and query_input: + st.session_state.query = query_input # Sync state + with st.spinner("Reading thousands of books..."): + query_embedding = generate_embedding_for_query(query_input) + st.session_state.recommendations = recommender.get_recommendations_from_vector( + query_embedding, top_k=12, similarity_threshold=0.20 + ) + + st.divider() + + # --- RESULTS GRID --- + if st.session_state.recommendations: + st.markdown(f"### Found {len(st.session_state.recommendations)} Matches") + + # Fetch covers in bulk + visible_recs = st.session_state.recommendations + with st.spinner("Fetching covers..."): + covers_dict = load_book_covers_batch(visible_recs) + + # Responsive Grid Logic + # We iterate through the results and create rows of 4 + num_cols = 4 + rows = [visible_recs[i:i + num_cols] for i in range(0, len(visible_recs), num_cols)] + + for row in rows: + cols = st.columns(num_cols) + for idx, (col, rec) in enumerate(zip(cols, row)): + with col: + # 1. Render the HTML visual card + cover = covers_dict.get(rec["title"], config.FALLBACK_COVER_URL) + render_book_card_content(rec, cover) + + # 2. Render the Streamlit interactive buttons BELOW the HTML card + # Because of the fixed height in CSS, these will align nicely + b1, b2 = st.columns([1, 3]) + with b1: + # Like button (icon only) + st.button("👍", key=f"like_{rec['title']}", help="More like this") + with b2: + if st.button("View Details", key=f"det_{rec['title']}", use_container_width=True): + show_book_details(rec, st.session_state.query) + + st.markdown("
", unsafe_allow_html=True) + + elif search_clicked: + st.warning("No books found matching that description. Try being broader!") + + except Exception as e: + st.error(f"App Error: {str(e)}") + logger.error(f"Crash: {e}", exc_info=True) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/src/book_recommender/core/__init__.py b/src/book_recommender/core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/book_recommender/core/config.py b/src/book_recommender/core/config.py new file mode 100644 index 0000000000000000000000000000000000000000..d18a9868f0703a0814d187a502354e777ed1fc58 --- /dev/null +++ b/src/book_recommender/core/config.py @@ -0,0 +1,46 @@ +"""Centralized configuration for the book recommender application.""" + +import os +from pathlib import Path + +from dotenv import load_dotenv + +load_dotenv() + +BASE_DIR = Path(__file__).resolve().parent.parent.parent.parent + +DATA_DIR = BASE_DIR / "data" +# Ensure data directory exists +DATA_DIR.mkdir(parents=True, exist_ok=True) + +RAW_DATA_DIR = DATA_DIR / "raw" +PROCESSED_DATA_DIR = DATA_DIR / "processed" + +RAW_DATA_PATH = RAW_DATA_DIR / "books_prepared.csv" +PROCESSED_DATA_PATH = PROCESSED_DATA_DIR / "books_cleaned.parquet" +EMBEDDINGS_PATH = PROCESSED_DATA_DIR / "book_embeddings.npy" +EMBEDDING_METADATA_PATH = PROCESSED_DATA_DIR / "embedding_metadata.json" +CLUSTERS_CACHE_PATH = PROCESSED_DATA_DIR / "cluster_cache.pkl" + +EMBEDDING_MODEL = os.getenv("EMBEDDING_MODEL", "all-MiniLM-L6-v2") + +EMBEDDING_DIMENSION = 384 + +EMBEDDING_DEVICE = os.getenv("EMBEDDING_DEVICE", "cpu") + +DEFAULT_BATCH_SIZE = 64 + +DEFAULT_TOP_K = 10 +MIN_SIMILARITY_THRESHOLD = 0.3 +NUM_CLUSTERS = int(os.getenv("NUM_CLUSTERS", "50")) + + +APP_VERSION = "0.1.0" +FALLBACK_COVER_URL = "https://placehold.co/200x300/667eea/white?text=No+Cover" + + +# --- Data/Model Versioning (Future Consideration) +# For production systems, consider implementing a robust data and model +# versioning system (e.g., DVC - Data Version Control) to track changes +# to processed data and generated embeddings. For this MVP, manual +# management or timestamping of files is suggested if versioning is critical. diff --git a/src/book_recommender/core/exceptions.py b/src/book_recommender/core/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..9c9d6e98919ad27dd18c093bb58a7ff4bed9f76e --- /dev/null +++ b/src/book_recommender/core/exceptions.py @@ -0,0 +1,25 @@ +"""Custom exception types for the book recommender application.""" + + +class BookRecommenderError(Exception): + """Base class for exceptions in this application.""" + + pass + + +class DataNotFoundError(BookRecommenderError, FileNotFoundError): + """Raised when a required data file (e.g., CSV, pickle) is not found.""" + + pass + + +class FileProcessingError(BookRecommenderError): + """Raised when there's an error during file processing (e.g., CSV parsing).""" + + pass + + +class ModelLoadError(BookRecommenderError): + """Raised when the sentence-transformer model cannot be loaded.""" + + pass diff --git a/src/book_recommender/core/logging_config.py b/src/book_recommender/core/logging_config.py new file mode 100644 index 0000000000000000000000000000000000000000..06ef1841a1ee65c21dfd6e3a533fab53ad7ce5fe --- /dev/null +++ b/src/book_recommender/core/logging_config.py @@ -0,0 +1,58 @@ +import logging +import os +from logging.handlers import RotatingFileHandler + +_logging_configured = False + + +def configure_logging( + log_file: str = "app.log", + log_level: str = "INFO", + max_bytes: int = 5 * 1024 * 1024, + backup_count: int = 5, + log_dir: str = "logs", +): + """Configure application-wide logging with clean format.""" + global _logging_configured + + os.makedirs(log_dir, exist_ok=True) + log_path = os.path.join(log_dir, log_file) + + root_logger = logging.getLogger() + root_logger.setLevel(log_level) + + if root_logger.handlers: + for handler in root_logger.handlers: + root_logger.removeHandler(handler) + + formatter = logging.Formatter("%(asctime)s | %(levelname)-8s | %(name)-30s | %(message)s", datefmt="%H:%M:%S") + + console_handler = logging.StreamHandler() + console_handler.setFormatter(formatter) + console_handler.setLevel(log_level) + root_logger.addHandler(console_handler) + + file_handler = RotatingFileHandler(log_path, maxBytes=max_bytes, backupCount=backup_count, encoding="utf-8") + file_handler.setFormatter(formatter) + root_logger.addHandler(file_handler) + + logging.getLogger("urllib3").setLevel(logging.WARNING) + logging.getLogger("sentence_transformers").setLevel(logging.WARNING) + logging.getLogger("transformers").setLevel(logging.WARNING) + logging.getLogger("torch").setLevel(logging.WARNING) + logging.getLogger("huggingface_hub").setLevel(logging.WARNING) + logging.getLogger("streamlit").setLevel(logging.ERROR) + logging.getLogger("watchfiles").setLevel(logging.WARNING) + + if not _logging_configured: + root_logger.info(f"Logging configured | Level: {log_level} | File: {log_path}") + _logging_configured = True + + +if __name__ == "__main__": + configure_logging(log_file="test_app.log", log_level="DEBUG") + logger = logging.getLogger(__name__) + logger.debug("This is a debug message from logging_config.py") + logger.info("This is an info message from logging_config.py") + logger.warning("This is a warning message from logging_config.py") + logger.error("This is an error message from logging_config.py") diff --git a/src/book_recommender/data/__init__.py b/src/book_recommender/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/src/book_recommender/data/processor.py b/src/book_recommender/data/processor.py new file mode 100644 index 0000000000000000000000000000000000000000..1fa4b3d2eb7c9cc05a61c7bd64bbec11357a1362 --- /dev/null +++ b/src/book_recommender/data/processor.py @@ -0,0 +1,195 @@ +import ast +import logging +import os +import sys + +# Add the project root to the Python path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))) + +import pandas as pd + +from src.book_recommender.core.exceptions import DataNotFoundError, FileProcessingError +from src.book_recommender.utils import ensure_dir_exists + +logger = logging.getLogger(__name__) + + +def process_dataframe(df: pd.DataFrame) -> pd.DataFrame: + """ + Cleans, deduplicates, and engineers features on a raw book DataFrame. + + This function performs the following main operations: + 1. Handles missing columns by initializing them as empty strings. + 2. Fills any NaN values in text columns with empty strings. + 3. Converts all text columns to string type for safety. + 4. Creates lowercase versions of 'title' and 'authors' for matching, + while preserving the original casing for display. + 5. Deduplicates the DataFrame based on the lowercased title. + 6. Removes rows with empty titles. + 7. Engineers the 'combined_text' feature for embeddings, applying a + weighting strategy to give more importance to the title and author. + + Args: + df (pd.DataFrame): The raw book data, typically from a CSV. + + Returns: + pd.DataFrame: The processed DataFrame ready for embedding. + + Raises: + ValueError: If the DataFrame is empty after processing. + """ + logger.info("Starting data cleaning and preparation...") + logger.info(f"Found columns in CSV: {df.columns.tolist()}") + + expected_cols = ["title", "authors", "genres", "description", "tags"] + for col in expected_cols: + if col not in df.columns: + df[col] = "" + logger.warning(f"Column '{col}' not found in CSV. Initializing as empty.") + + df[expected_cols] = df[expected_cols].fillna("") + + for col in expected_cols: + df[col] = df[col].astype(str) + + if "book_id" in df.columns: + df["id"] = df["book_id"].astype(str) + logger.info("Using 'book_id' as the unique identifier.") + else: + df["id"] = df.index.astype(str) + logger.warning("No 'book_id' column found. Generated 'id' from DataFrame index.") + + for col in ["genres", "tags"]: + if col in df.columns: + df[col] = df[col].apply( + lambda x: ", ".join(ast.literal_eval(x)) if (x.startswith("[") and x.endswith("]")) else x + ) + + df["title_lower"] = df["title"].str.strip().str.lower() + df["authors_lower"] = df["authors"].str.strip().str.lower() + + # Clean description text (remove HTML tags and artifacts) + # This regex removes
, , tags and their closing counterparts + import re + def clean_html_text(text): + if not isinstance(text, str): + return text + text = re.sub(r'', '\n', text, flags=re.IGNORECASE) # Replace
with newline + text = re.sub(r'<[^>]+>', '', text) # Remove other HTML tags + return text.strip() + + df["description"] = df["description"].apply(clean_html_text) + + # Lowercase genres/tags for normalization, but keep description/title case for display + for col in ["genres", "tags"]: + df[col] = df[col].str.strip().str.lower() + + original_rows = len(df) + df.drop_duplicates(subset=["title_lower"], keep="first", inplace=True) + new_rows = len(df) + if new_rows < original_rows: + logger.info(f"Removed {original_rows - new_rows} duplicate books based on title.") + + original_rows = len(df) + df.dropna(subset=["title_lower"], inplace=True) + df = df[df["title_lower"] != ""].copy() + if len(df) < original_rows: + logger.warning(f"Dropped {original_rows - len(df)} rows with missing titles.") + + if df.empty: + logger.error("DataFrame is empty after cleaning. No valid book data to process.") + raise ValueError( + "No valid books found after processing. The dataset might be empty or contain only invalid entries." + ) + + logger.info("Creating 'combined_text' for embeddings with weighted fields...") + # Use lowercased description ONLY for embedding generation, not for storage + df["combined_text"] = ( + (df["title_lower"] + " ") * 3 + + "by " + + df["authors_lower"] + + ". " + + "genres: " + + df["genres"] + + ". " + + "description: " + + df["description"].str.lower() + + ". " + + "tags: " + + df["tags"] + ) + return df + + +def clean_and_prepare_data(raw_path: str, processed_path: str) -> pd.DataFrame: + """ + Orchestrator function that loads raw data, processes it, and saves the result. + + This function chains the data processing steps: + 1. Loads the raw CSV data from `raw_path`. + 2. Calls `process_dataframe` to perform all cleaning and feature engineering. + 3. Saves the cleaned DataFrame to a Parquet file at `processed_path`. + + Args: + raw_path (str): The file path for the raw CSV data. + processed_path (str): The file path to save the processed Parquet file. + + Returns: + pd.DataFrame: The fully processed DataFrame. + + Raises: + DataNotFoundError: If the file at `raw_path` is not found. + FileProcessingError: If the CSV file cannot be parsed. + """ + if not os.path.exists(raw_path): + logger.error(f"Raw data file not found at: {raw_path}") + raise DataNotFoundError(f"Raw data file not found at: {raw_path}") + + try: + logger.info(f"Loading raw data from {raw_path}...") + raw_df = pd.read_csv(raw_path) + logger.info(f"Loaded {len(raw_df)} rows.") + except (pd.errors.ParserError, UnicodeDecodeError) as e: + logger.error(f"Failed to parse CSV from {raw_path}: {e}") + raise FileProcessingError(f"Failed to parse CSV from {raw_path}: {e}") + except Exception as e: + logger.error(f"An unexpected error occurred while loading CSV from {raw_path}: {e}") + raise + + processed_df = process_dataframe(raw_df) + + try: + ensure_dir_exists(processed_path) + logger.info(f"Saving processed data to {processed_path}...") + processed_df.to_parquet(processed_path, index=False) + logger.info(f"Successfully saved {len(processed_df)} processed rows.") + except Exception as e: + logger.error(f"Failed to save processed data to {processed_path}: {e}") + raise + + return processed_df + + +if __name__ == "__main__": + import argparse + + import book_recommender.core.config as config + + if os.getenv("LOG_LEVEL"): + logging.basicConfig(level=os.getenv("LOG_LEVEL")) + else: + logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") + + parser = argparse.ArgumentParser(description="Clean and prepare book data.") + parser.add_argument("--raw-path", type=str, default=config.RAW_DATA_PATH, help="Path to the raw CSV data file.") + parser.add_argument( + "--processed-path", + type=str, + default=config.PROCESSED_DATA_PATH, + help="Path to save the processed Parquet file.", + ) + args = parser.parse_args() + + logger.info("--- Starting Data Processing Standalone Script ---") + clean_and_prepare_data(raw_path=args.raw_path, processed_path=args.processed_path) + logger.info("--- Data Processing Finished ---") diff --git a/src/book_recommender/ml/__init__.py b/src/book_recommender/ml/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1dee30dc9e06a6e14195e23603810beb31440bdb --- /dev/null +++ b/src/book_recommender/ml/__init__.py @@ -0,0 +1,23 @@ +"""Machine learning modules for BookFinder-AI""" +import os +import sys + +# Add the project root to the Python path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))) + +from src.book_recommender.ml.clustering import cluster_books, get_cluster_names +from src.book_recommender.ml.embedder import generate_embedding_for_query, generate_embeddings +from src.book_recommender.ml.explainability import explain_recommendation +from src.book_recommender.ml.feedback import get_all_feedback, save_feedback +from src.book_recommender.ml.recommender import BookRecommender + +__all__ = [ + "BookRecommender", + "generate_embedding_for_query", + "generate_embeddings", + "cluster_books", + "get_cluster_names", + "explain_recommendation", + "save_feedback", + "get_all_feedback", +] diff --git a/src/book_recommender/ml/clustering.py b/src/book_recommender/ml/clustering.py new file mode 100644 index 0000000000000000000000000000000000000000..2ec8426e26f39a7ad6263cee38e1f47329ecdbff --- /dev/null +++ b/src/book_recommender/ml/clustering.py @@ -0,0 +1,69 @@ +import logging + +import numpy as np +import pandas as pd +from sklearn.cluster import KMeans + +logger = logging.getLogger(__name__) + + +def cluster_books(embeddings: np.ndarray, n_clusters: int = 20, random_state: int = 42) -> tuple[np.ndarray, KMeans]: + """ + Cluster books using K-Means. + + Args: + embeddings (np.ndarray): A 2D NumPy array of book embeddings. + n_clusters (int): The number of clusters to form. + random_state (int): Seed for reproducibility. + + Returns: + tuple[np.ndarray, KMeans]: A tuple containing: + - np.ndarray: An array of cluster labels for each book. + - KMeans: The fitted KMeans model. + """ + logger.info(f"Starting K-Means clustering with {n_clusters} clusters...") + kmeans = KMeans(n_clusters=n_clusters, random_state=random_state, n_init=10) + clusters = kmeans.fit_predict(embeddings) + logger.info("K-Means clustering completed.") + return clusters, kmeans + + +def get_cluster_names(book_data: pd.DataFrame, clusters: np.ndarray) -> dict[int, str]: + """ + Generate descriptive names for clusters based on the most common genres. + + Args: + book_data (pd.DataFrame): DataFrame containing book metadata, including a 'genres' column. + clusters (np.ndarray): An array of cluster labels for each book. + + Returns: + dict[int, str]: A dictionary mapping cluster IDs to their descriptive names. + """ + logger.info("Generating descriptive names for clusters...") + cluster_names = {} + + temp_genres_list = book_data["genres"].apply( + lambda x: [g.strip() for g in x.split(",") if g.strip()] if isinstance(x, str) else [] + ) + + for cluster_id in range(max(clusters) + 1): + cluster_books_indices = np.where(clusters == cluster_id)[0] + if len(cluster_books_indices) == 0: + cluster_names[cluster_id] = f"Empty Cluster {cluster_id}" + logger.warning(f"Cluster {cluster_id} is empty.") + continue + + genres_in_cluster = temp_genres_list.iloc[cluster_books_indices] + + all_genres = [genre for sublist in genres_in_cluster for genre in sublist] + + if all_genres: + genre_counts = pd.Series(all_genres).value_counts() + top_genre = genre_counts.index[0] + cluster_names[cluster_id] = f"{top_genre.title()} Collection" + else: + cluster_names[cluster_id] = f"Miscellaneous Cluster {cluster_id}" + logger.debug(f"Cluster {cluster_id} named: {cluster_names[cluster_id]}") + + logger.info("Cluster names generated.") + return cluster_names diff --git a/src/book_recommender/ml/embedder.py b/src/book_recommender/ml/embedder.py new file mode 100644 index 0000000000000000000000000000000000000000..7c1fb3fcc140f333ce3bc74aaa8d65104c5d41dd --- /dev/null +++ b/src/book_recommender/ml/embedder.py @@ -0,0 +1,222 @@ +# src/embedder.py + +import logging +import os +import sys +from functools import lru_cache +from typing import Optional +from pathlib import Path + +# Add the project root to the Python path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))) + +import numpy as np +import pandas as pd +from sentence_transformers import SentenceTransformer + +import src.book_recommender.core.config as config +from src.book_recommender.core.exceptions import DataNotFoundError, ModelLoadError + +logger = logging.getLogger(__name__) + + +@lru_cache(maxsize=1) +def load_model(model_name: str = config.EMBEDDING_MODEL) -> SentenceTransformer: + """ + Loads and caches the sentence-transformer model. + + Checks for a local cache at 'data/processed/model_cache'. + If not found or incomplete, downloads and saves it. + + Args: + model_name (str): The name of the model to load. + + Returns: + SentenceTransformer: The loaded model instance. + + Raises: + ModelLoadError: If the model cannot be loaded. + """ + cache_path = config.PROCESSED_DATA_DIR / "model_cache" + + try: + # Robust check: A valid model directory must contain 'modules.json' + if cache_path.exists() and (cache_path / "modules.json").exists(): + logger.info(f"Loading model from local project cache: {cache_path}") + model = SentenceTransformer(str(cache_path), device=config.EMBEDDING_DEVICE) + logger.info("Model loaded successfully from cache.") + return model + + logger.info(f"Model not found in project cache (or incomplete). Downloading {model_name}...") + model = SentenceTransformer(model_name, device=config.EMBEDDING_DEVICE) + + logger.info(f"Saving model to project cache: {cache_path}") + # Ensure parent directory exists + cache_path.parent.mkdir(parents=True, exist_ok=True) + model.save(str(cache_path)) + logger.info("Model saved to cache.") + + return model + except Exception as e: + logger.error(f"Failed to load sentence-transformer model '{model_name}': {e}") + raise ModelLoadError(f"Failed to load sentence-transformer model '{model_name}': {e}") + + +def generate_embeddings( + df: pd.DataFrame, + model_name: str = config.EMBEDDING_MODEL, + show_progress_bar: bool = True, + batch_size: int = config.DEFAULT_BATCH_SIZE, +) -> np.ndarray: + """ + Generates sentence embeddings for the 'combined_text' of a DataFrame. + + This function loads a specified sentence-transformer model and uses it to + encode the `combined_text` column of the provided DataFrame into + high-dimensional vectors (embeddings). + + Args: + df (pd.DataFrame): The DataFrame containing book data with a 'combined_text' column. + model_name (str): The name of the sentence-transformer model to use. + show_progress_bar (bool): Whether to display a progress bar during encoding. + batch_size (int): The batch size for the encoding process to manage memory. + + Returns: + np.ndarray: A 2D NumPy array containing the generated embeddings. + """ + model = load_model(model_name) + + logger.info(f"Generating embeddings for {len(df)} books...") + try: + embeddings = model.encode( + df["combined_text"].tolist(), show_progress_bar=show_progress_bar, batch_size=batch_size + ) + logger.info(f"Embeddings generated with shape: {embeddings.shape}") + except Exception as e: + logger.error(f"Failed to generate embeddings: {e}") + raise + + return np.asarray(embeddings) + + +def generate_embedding_for_query( + query: str, model_name: str = config.EMBEDDING_MODEL, model: Optional[SentenceTransformer] = None +) -> np.ndarray: + """ + Generates a sentence embedding for a single text query. + + Args: + query (str): The text query to embed. + model_name (str): The name of the sentence-transformer model to use (if model not provided). + model (SentenceTransformer, optional): A pre-loaded model instance to use. + + Returns: + np.ndarray: A 1D NumPy array representing the query embedding. + """ + if model is None: + model = load_model(model_name) + + logger.info(f"Generating embedding for query: '{query[:50]}...'") + embedding = model.encode(query, show_progress_bar=False) + return np.asarray(embedding) + + +if __name__ == "__main__": + import argparse + import json + import sys + + sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + from src.book_recommender.core import config as config_main + from src.book_recommender.core.exceptions import DataNotFoundError + from src.book_recommender.utils import ensure_dir_exists as ensure_dir_exists_main + + # When running as a script, basic logging is not configured by default. + # To see log output, set the LOG_LEVEL environment variable, + # e.g., `export LOG_LEVEL=INFO` or run with `python -m logging ...` + if os.getenv("LOG_LEVEL"): + logging.basicConfig(level=os.getenv("LOG_LEVEL")) + else: + # Provide a default configuration if the script is run directly + # and no environment variable is set, to ensure messages are not lost. + logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") + + parser = argparse.ArgumentParser(description="Generate and save book embeddings.") + parser.add_argument( + "--processed-path", + type=str, + default=config_main.PROCESSED_DATA_PATH, + help="Path to the processed Parquet data file.", + ) + parser.add_argument( + "--embeddings-path", + type=str, + default=config_main.EMBEDDINGS_PATH, + help="Path to save the embeddings .npy file.", + ) + parser.add_argument( + "--metadata-path", + type=str, + default=config_main.EMBEDDING_METADATA_PATH, + help="Path to save the embedding metadata JSON file.", + ) + parser.add_argument( + "--model-name", + type=str, + default=config_main.EMBEDDING_MODEL, + help="Name of the sentence-transformer model to use.", + ) + parser.add_argument( + "--no-progress-bar", + action="store_false", + dest="show_progress_bar", + help="Disable the progress bar during embedding generation.", + ) + parser.add_argument( + "--batch-size", type=int, default=config_main.DEFAULT_BATCH_SIZE, help="The batch size to use for encoding." + ) + args = parser.parse_args() + + logger.info("--- Starting Embedding Generation Standalone Script ---") + + if not os.path.exists(args.processed_path): + logger.error(f"Processed data file not found at: {args.processed_path}") + raise DataNotFoundError(f"Processed data file not found at: {args.processed_path}") + + logger.info(f"Loading processed data from {args.processed_path}...") + processed_df = pd.read_parquet(args.processed_path) + + embeddings_array = generate_embeddings( + df=processed_df, + model_name=args.model_name, + show_progress_bar=args.show_progress_bar, + batch_size=args.batch_size, + ) + + try: + ensure_dir_exists_main(args.embeddings_path) + logger.info(f"Saving embeddings to {args.embeddings_path}...") + np.save(args.embeddings_path, embeddings_array) + logger.info("Embeddings saved successfully.") + + metadata = { + "model_name": args.model_name, + "embedding_dimension": config_main.EMBEDDING_DIMENSION, + "num_books": len(processed_df), + "batch_size": args.batch_size, + "created_at": pd.Timestamp.now().isoformat(), + } + ensure_dir_exists_main(args.metadata_path) + + # Save a simple metadata file to indicate the embedding version/date + with open(args.metadata_path, "w", encoding="utf-8") as f: + import json + json.dump(metadata, f, indent=4) + + logger.info(f"Metadata saved to {args.metadata_path}") + + except Exception as e: + logger.error(f"Failed to save embeddings or metadata: {e}") + raise + + logger.info("--- Embedding Generation Finished ---") diff --git a/src/book_recommender/ml/explainability.py b/src/book_recommender/ml/explainability.py new file mode 100644 index 0000000000000000000000000000000000000000..f13b827e883045207dc58acdb66fdf52763c1073 --- /dev/null +++ b/src/book_recommender/ml/explainability.py @@ -0,0 +1,148 @@ +import logging +import os +from typing import Any, Dict, List, Union + +try: + from groq import Groq +except ImportError: + Groq = None + +logger = logging.getLogger(__name__) + + +def _normalize_to_list(field_value: Union[str, List[str], None]) -> List[str]: + """Normalize a string (comma-separated) or list of strings into a list of strings.""" + if not field_value: + return [] + if isinstance(field_value, list): + return [str(item).strip() for item in field_value] + if isinstance(field_value, str): + return [item.strip() for item in field_value.split(",") if item.strip()] + return [] + + +def _generate_rule_based_summary( + query_text: str, recommended_book: Dict[str, Any], contribution_scores: Dict[str, float] +) -> str: + """Fallback: Generates a simple rule-based summary.""" + summary = f"You got this recommendation because it's a good match for your interest in '{query_text}'. " + + matching_features = [] + if contribution_scores.get("genres", 0) > 0.1: + book_genres_list = _normalize_to_list(recommended_book.get("genres")) + if book_genres_list: + matching_features.append(f"it shares genres such as {', '.join(book_genres_list[:3])}") + + if contribution_scores.get("description_keywords", 0) > 0.1: + matching_features.append("has matching keywords in the description") + + if contribution_scores.get("authors", 0) > 0.1: + book_authors_list = _normalize_to_list(recommended_book.get("authors")) + if book_authors_list and book_authors_list != ["Unknown Author"]: + matching_features.append(f"is by author(s) {', '.join(book_authors_list)}") + + if matching_features: + summary += "Specifically, " + ", and ".join(matching_features) + "." + else: + summary += "Its content aligns well with your query." + + return summary + + +def _generate_llm_summary(query_text: str, recommended_book: Dict[str, Any]) -> str | None: + """Generates a personalized explanation using Groq (Llama 3).""" + api_key = os.getenv("GROQ_API_KEY") + if not api_key or not Groq: + return None + + try: + client = Groq(api_key=api_key) + + # System Prompt: Sets the persona and constraints + system_content = ( + "You are a helpful book assistant explaining recommendations to a reader. " + "Your goal is to explain the connection between their search and this book. " + "Address the reader directly as 'You'. " + "NEVER refer to 'the user' or 'the user's request' in the third person. " + "Be insightful, concise, and warm." + ) + + # User Prompt: Provides the data and specific task + user_content = f""" + Reader's Search: "{query_text}" + + Book Metadata: + - Title: {recommended_book.get('title')} + - Author: {recommended_book.get('authors')} + - Synopsis: {recommended_book.get('description')} + + Task: Write a 2-sentence explanation of why this book fits the search. + + Guidelines: + 1. Start directly with "You might like this because..." or "This matches your search for..." + 2. Explicitly link specific themes in the synopsis to the search terms. + 3. Do NOT summarize the plot unless it proves the match. + 4. STRICTLY FORBIDDEN: Do not use the word "user". + """ + + completion = client.chat.completions.create( + model="llama-3.1-8b-instant", + messages=[ + {"role": "system", "content": system_content}, + {"role": "user", "content": user_content} + ], + temperature=0.6, # Slightly lower for more focused results + max_tokens=120, + top_p=1, + stop=None, + stream=False, + ) + + return completion.choices[0].message.content.strip() + except Exception as e: + logger.error(f"Groq API failed: {e}") + return None + + +def get_contribution_scores(query_text: str, recommended_book: Dict[str, Any]) -> Dict[str, float]: + """Calculates hypothetical contribution scores for different features.""" + scores = {"genres": 0.0, "description_keywords": 0.0, "authors": 0.0} + # Simple logic kept for metadata details + query_words = set(query_text.lower().split()) + book_desc = recommended_book.get("description", "").lower() + + if any(w in book_desc for w in query_words): + scores["description_keywords"] = 0.8 + + return scores + + +def explain_recommendation( + query_text: str, recommended_book: Dict[str, Any], similarity_score: float +) -> Dict[str, Any]: + """ + Generates an explanation for why a particular book was recommended. + Tries LLM first, falls back to rules. + """ + logger.info(f"Generating explanation for '{recommended_book.get('title')}'") + + # 1. Try LLM + summary = _generate_llm_summary(query_text, recommended_book) + + # 2. Fallback to Rule-Based + contribution_scores = get_contribution_scores(query_text, recommended_book) + if not summary: + summary = _generate_rule_based_summary(query_text, recommended_book, contribution_scores) + + confidence = "HIGH" if similarity_score > 0.7 else "MEDIUM" + + return { + "match_score": round(similarity_score * 100), + "confidence": confidence, + "summary": summary, + "details": { + "genres_contribution": round(contribution_scores["genres"] * 100), + "description_keywords_contribution": round(contribution_scores["description_keywords"] * 100), + "authors_contribution": round(contribution_scores["authors"] * 100), + }, + } diff --git a/src/book_recommender/ml/feedback.py b/src/book_recommender/ml/feedback.py new file mode 100644 index 0000000000000000000000000000000000000000..b18a8302aff8cd53b45434d622711ed647d3374a --- /dev/null +++ b/src/book_recommender/ml/feedback.py @@ -0,0 +1,95 @@ +import json +import logging +import os +from datetime import datetime +from typing import Any, Dict, List, Optional + +from book_recommender.utils import ensure_dir_exists + +logger = logging.getLogger(__name__) + +FEEDBACK_DIR = "data/feedback" +FEEDBACK_FILE = os.path.join(FEEDBACK_DIR, "user_feedback.jsonl") + + +def save_feedback( + query: str, book_details: Dict[str, Any], feedback_type: str, session_id: Optional[str] = None +) -> None: + """ + Saves user feedback to a JSONL file. + + Args: + query (str): The original query text that led to the recommendation. + book_details (Dict[str, Any]): Details of the book for which feedback is given. + Should include 'id', 'title', 'authors'. + feedback_type (str): Type of feedback, e.g., "positive", "negative". + session_id (Optional[str]): Unique identifier for the user session. + """ + ensure_dir_exists(FEEDBACK_FILE) + + feedback_entry = { + "timestamp": datetime.now().isoformat(), + "query": query, + "book_id": book_details.get("id"), + "book_title": book_details.get("title"), + "book_authors": book_details.get("authors"), + "feedback": feedback_type, + "session_id": session_id, + } + + try: + with open(FEEDBACK_FILE, "a", encoding="utf-8") as f: + f.write(json.dumps(feedback_entry) + "\n") + logger.info(f"Feedback saved: {feedback_type} for '{book_details.get('title')}'") + except Exception as e: + logger.error(f"Failed to save feedback to {FEEDBACK_FILE}: {e}") + + +def get_all_feedback() -> List[Dict[str, Any]]: + """ + Loads all saved user feedback from the JSONL file. + + Returns: + List[Dict[str, Any]]: A list of feedback entries. + """ + if not os.path.exists(FEEDBACK_FILE): + return [] + + feedback_data = [] + try: + with open(FEEDBACK_FILE, "r", encoding="utf-8") as f: + for line in f: + feedback_data.append(json.loads(line)) + except Exception as e: + logger.error(f"Failed to load feedback from {FEEDBACK_FILE}: {e}") + + return feedback_data + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s") + logger.info("--- Testing feedback system ---") + + sample_book_1 = { + "id": "book1", + "title": "The Great Novel", + "authors": ["Author A"], + "description": "...", + "genres": "Fiction", + } + sample_book_2 = { + "id": "book2", + "title": "Another Story", + "authors": ["Author B"], + "description": "...", + "genres": "Fantasy", + } + + save_feedback("sci-fi book", sample_book_1, "positive", "session123") + save_feedback("fantasy epic", sample_book_2, "negative", "session123") + save_feedback("classic literature", sample_book_1, "positive", "session456") + + all_feedback = get_all_feedback() + print(f"\nTotal feedback entries: {len(all_feedback)}") + for entry in all_feedback: + print(entry) diff --git a/src/book_recommender/ml/recommender.py b/src/book_recommender/ml/recommender.py new file mode 100644 index 0000000000000000000000000000000000000000..330b685f21efbc55b4fec3f484de78d08ee63b9d --- /dev/null +++ b/src/book_recommender/ml/recommender.py @@ -0,0 +1,185 @@ +import logging +import os +import sys +from typing import Dict, List, Optional + +# Add the project root to the Python path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../.."))) + +import faiss +import numpy as np +import pandas as pd + +import src.book_recommender.core.config as config + +logger = logging.getLogger(__name__) + + +class BookRecommender: + """ + A content-based book recommender system that uses a FAISS index for + efficient similarity search. + + This class encapsulates the logic for building a searchable index of book + embeddings and retrieving recommendations based on semantic similarity. + """ + + def __init__(self, book_data: pd.DataFrame, embeddings: np.ndarray): + """ + Initializes the recommender, builds the FAISS index, and prepares data. + + Args: + book_data (pd.DataFrame): DataFrame containing book metadata. + Must include 'title_lower' column for indexing. + embeddings (np.ndarray): A 2D NumPy array of book embeddings. + """ + if len(book_data) != len(embeddings): + raise ValueError("Mismatch between number of books and number of embeddings.") + + self.book_data = book_data + self.embeddings = embeddings.astype("float32") + + faiss.normalize_L2(self.embeddings) + + self.index = faiss.IndexFlatL2(config.EMBEDDING_DIMENSION) + self.index.add(self.embeddings) + + self.title_to_index = pd.Series(self.book_data.index, index=self.book_data["title_lower"]).to_dict() + logger.info(f"Recommender initialized with FAISS index containing {self.index.ntotal} vectors.") + + def get_recommendations_from_vector( + self, + vector: np.ndarray, + top_k: int = config.DEFAULT_TOP_K, + similarity_threshold: float = config.MIN_SIMILARITY_THRESHOLD, + ignore_index: Optional[int] = None, + ) -> List[Dict]: + """ + Finds and returns top_k book recommendations for a given embedding vector. + + Args: + vector (np.ndarray): The embedding vector to find recommendations for. + top_k (int): The number of recommendations to return. + similarity_threshold (float): The minimum similarity score. + ignore_index (int, optional): An index to ignore in the results (e.g., the query book itself). + + Returns: + A list of dictionaries, each containing details of a recommended book. + """ + if vector.ndim == 1: + vector = vector.reshape(1, -1) + + faiss.normalize_L2(vector) + + distances, indices = self.index.search(vector, top_k + (1 if ignore_index is not None else 0)) + + # Filter out the ignore_index if present + valid_mask = indices[0] != ignore_index + valid_indices = indices[0][valid_mask] + valid_distances = distances[0][valid_mask] + + # Calculate similarity scores + # cosine_sim = 1 - (L2_dist^2) / 2 + similarity_scores = 1 - (valid_distances**2) / 2 + + # Filter by threshold + threshold_mask = similarity_scores >= similarity_threshold + final_indices = valid_indices[threshold_mask] + final_scores = similarity_scores[threshold_mask] + + if len(final_indices) == 0: + return [] + + # Batch retrieve book data + # We use iloc[final_indices] to get all rows at once + recommended_books_df = self.book_data.iloc[final_indices] + + recommendations = [] + # Iterate over the subset DataFrame and the corresponding scores + for (idx, row), score in zip(recommended_books_df.iterrows(), final_scores): + recommendations.append( + { + "id": row["id"], + "title": row["title"], + "authors": row.get("authors", "N/A"), + "description": row.get("description", ""), + "genres": row.get("genres", ""), + "tags": row.get("tags", ""), + "rating": row.get("rating", "N/A"), + "cover_image_url": row.get("cover_image_url", None), + "similarity": float(score), + } + ) + + return recommendations + + def get_recommendations( + self, + title: str, + top_k: int = 5, + similarity_threshold: float = config.MIN_SIMILARITY_THRESHOLD, + ) -> List[Dict]: + """ + Finds and returns top_k book recommendations for a given title using FAISS. + + Args: + title (str): The title of the book to get recommendations for. + top_k (int): The number of recommendations to return. + similarity_threshold (float): The minimum similarity score for a book + to be considered a recommendation. + + Returns: + A list of dictionaries, where each dictionary contains the details + of a recommended book (title, authors, similarity, etc.). + Returns an empty list if the title is not found or no books meet + the similarity threshold. + """ + book_index = self.title_to_index.get(title.lower()) + + if book_index is None: + logger.warning(f"Title '{title}' not found in the dataset.") + return [] + + book_vector = self.embeddings[book_index] + + recommendations = self.get_recommendations_from_vector( + book_vector, top_k, similarity_threshold, ignore_index=book_index + ) + + logger.info(f"Found {len(recommendations)} recommendations for '{title}'.") + return recommendations + + +if __name__ == "__main__": + import sys + + sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + from src.book_recommender.core import config as config_main + + logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") + + if not (os.path.exists(config_main.PROCESSED_DATA_PATH) and os.path.exists(config_main.EMBEDDINGS_PATH)): + print("Processed data or embeddings not found.") + print("Please run 'python src/data_processor.py' and 'python src/embedder.py' first.") + else: + logger.info(f"Loading book metadata from {config_main.PROCESSED_DATA_PATH}...") + book_data_df = pd.read_parquet(config_main.PROCESSED_DATA_PATH) + + logger.info(f"Loading book embeddings from {config_main.EMBEDDINGS_PATH}...") + embeddings_arr = np.load(config_main.EMBEDDINGS_PATH) + + recommender = BookRecommender(book_data=book_data_df, embeddings=embeddings_arr) + + book_titles = recommender.book_data["title"].tolist() + if book_titles: + test_title = book_titles[0] + print(f"--- Getting recommendations for: '{test_title}' ---") + recs = recommender.get_recommendations(test_title, top_k=5) + + if recs: + for i, rec in enumerate(recs): + print(f"{i+1}. {rec['title']} by {rec['authors']} (Similarity: {rec['similarity']:.2f})") + else: + print("Could not find any recommendations.") + else: + print("No book titles found in the dataset.") diff --git a/src/book_recommender/services/personalizer.py b/src/book_recommender/services/personalizer.py new file mode 100644 index 0000000000000000000000000000000000000000..bc7aa6c6664aa302d666c713a1093711d7a43251 --- /dev/null +++ b/src/book_recommender/services/personalizer.py @@ -0,0 +1,56 @@ +import requests +import os +from typing import List, Dict, Optional + +class PersonalizationService: + def __init__(self): + # Use env var for Docker compatibility, default to localhost + # The Service B (Personalizer) runs on Port 8001 + self.base_url = os.getenv("PERSONALIZER_URL", "http://localhost:8001") + self.timeout = 60.0 # Increased timeout for CPU inference + + def get_recommendations(self, user_history: List[str], top_k: int = 5) -> List[Dict]: + """ + Call the external personalization API. + Returns a list of dicts: {'title': str, 'score': float, 'genres': str} + """ + if not user_history: + return [] + + try: + payload = { + "user_history": user_history, + "top_k": top_k + } + response = requests.post( + f"{self.base_url}/personalize/recommend", + json=payload, + timeout=self.timeout + ) + + if response.status_code == 200: + return response.json() + else: + # Log error but don't crash + print(f"Personalizer Error: {response.status_code}") + return [] + + except requests.exceptions.RequestException: + # Circuit Breaker pattern: If service is down, return empty list + # so the main app doesn't crash. + print("Personalizer Service Unreachable") + return [] + + def semantic_search(self, query: str) -> List[Dict]: + """ + Search for books by plot/vibe. + """ + try: + response = requests.post( + f"{self.base_url}/search", + json={"query": query, "top_k": 10}, + timeout=self.timeout + ) + return response.json() if response.status_code == 200 else [] + except: + return [] diff --git a/src/book_recommender/utils.py b/src/book_recommender/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..941ba67d381ae24239e8248b1fc38c0bff7969a2 --- /dev/null +++ b/src/book_recommender/utils.py @@ -0,0 +1,184 @@ +import logging +import os +import urllib.parse +from concurrent.futures import ThreadPoolExecutor +from functools import lru_cache +from typing import Optional +import difflib + +import requests + + +logger = logging.getLogger(__name__) + +PLACEHOLDER_IMAGES = [ + "https://images.unsplash.com/photo-1543002588-bfa74002ed7e?w=300&h=450&fit=crop", + "https://images.unsplash.com/photo-1512820790803-83ca734da794?w=300&h=450&fit=crop", + "https://images.unsplash.com/photo-1495446815901-a7297e633e8d?w=300&h=450&fit=crop", +] + + +def _strings_are_similar(s1: str, s2: str, threshold: float = 0.6) -> bool: + """Check if two strings are similar using sequence matching or containment.""" + if not s1 or not s2: + return False + s1, s2 = s1.lower(), s2.lower() + # Check for containment (handles substrings like "Harry Potter" in "Harry Potter and the...") + if s1 in s2 or s2 in s1: + return True + return difflib.SequenceMatcher(None, s1, s2).ratio() > threshold + + +def ensure_dir_exists(file_path: str): + """Ensures that the directory for a given file path exists.""" + try: + output_dir = os.path.dirname(file_path) + if output_dir: + os.makedirs(output_dir, exist_ok=True) + except OSError as e: + logger.error(f"Error creating directory for {file_path}: {e}") + raise + + +@lru_cache(maxsize=256) +def get_cover_url_multi_source(title: str, author: str) -> str: + """ + Fetch book cover from multiple sources with fallback chain. + + Priority order: + 1. Google Books API (best quality, most reliable) + 2. Open Library API + 3. Beautiful placeholder from Unsplash + """ + cover = _get_cover_from_google_books(title, author) + if cover: + return cover + + cover = _get_cover_from_openlibrary(title, author) + if cover: + return cover + + # Return None so the frontend can render a generated gradient cover + return None + + +def _get_cover_from_google_books(title: str, author: str) -> Optional[str]: + """Fetch cover from Google Books API.""" + try: + query = f"{title} {author}".strip() + encoded_query = urllib.parse.quote(query) + + base_url = "https://www.googleapis.com/books/v1/volumes" + url = f"{base_url}?q={encoded_query}&maxResults=1" + + # Add API key if available to avoid rate limiting + api_key = os.getenv("GOOGLE_BOOKS_API_KEY") + if api_key: + url += f"&key={api_key}" + + response = requests.get(url, timeout=5) + response.raise_for_status() + data = response.json() + + if data.get("totalItems", 0) > 0: + items = data.get("items", []) + if items and "volumeInfo" in items[0]: + volume_info = items[0]["volumeInfo"] + + # Validate match to avoid false positives + found_title = volume_info.get("title", "") + if not _strings_are_similar(title, found_title): + logger.info(f"Google Books mismatch: queried '{title}', got '{found_title}'. Skipping.") + return None + + image_links = volume_info.get("imageLinks", {}) + + for size in ["large", "medium", "small", "thumbnail", "smallThumbnail"]: + if size in image_links: + cover_url: str = image_links[size] + cover_url = cover_url.replace("http://", "https://") + logger.info(f"Found Google Books cover for '{title}'") + return cover_url + + return None + except Exception as e: + logger.debug(f"Google Books API failed for '{title}': {e}") + return None + + +def _get_cover_from_openlibrary(title: str, author: str) -> Optional[str]: + """Fetch cover from Open Library API.""" + try: + search_url = ( + f"https://openlibrary.org/search.json?title={urllib.parse.quote(title)}&author={urllib.parse.quote(author)}" + ) + + response = requests.get(search_url, timeout=5) + response.raise_for_status() + data = response.json() + + if data.get("numFound", 0) > 0: + docs = data.get("docs", []) + if docs: + for doc in docs: + if "isbn" in doc and doc["isbn"]: + isbn = doc["isbn"][0] + cover_url = f"https://covers.openlibrary.org/b/isbn/{isbn}-L.jpg" + logger.info(f"Found Open Library cover for '{title}'") + return cover_url + + return None + except Exception as e: + logger.debug(f"Open Library API failed for '{title}': {e}") + return None + + +def load_book_covers_batch(books): + """Pre-fetch covers in batch, using existing URLs if available.""" + results = {} + books_to_fetch = [] + + for book in books: + # Check if we already have a valid URL from our enriched dataset + existing_url = book.get("cover_image_url") + if existing_url and isinstance(existing_url, str) and len(existing_url) > 10: + results[book["title"]] = existing_url + else: + books_to_fetch.append(book) + + if not books_to_fetch: + return results + + with ThreadPoolExecutor(max_workers=10) as executor: + futures = { + executor.submit(get_cover_url_multi_source, book["title"], book.get("authors", "")): book for book in books_to_fetch + } + + for future in futures: + book = futures[future] + try: + results[book["title"]] = future.result() + except Exception as e: + logger.error(f"Error loading cover for {book['title']}: {e}") + results[book["title"]] = PLACEHOLDER_IMAGES[0] + + return results + + +def fetch_book_cover(title: str, author: str) -> Optional[str]: + """ + Fetch book cover from multiple sources with fallback chain. + + Priority order: + 1. Google Books API (best quality, most reliable) + 2. Open Library API + 3. Placeholder image + + Args: + title (str): Book title + author (str): Book author + + Returns: + Optional[str]: URL to book cover or None if not found + """ + return get_cover_url_multi_source(title, author) diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..72525fe0c0027706ac5d654cfc5d3a15fd51a9c4 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,14 @@ +# tests/conftest.py +import logging + +import pytest + + +@pytest.fixture(autouse=True) +def caplog_for_tests(caplog): + """ + Fixture to capture logs during tests and set a default logging level. + This prevents log messages from cluttering test output unless a test fails. + """ + caplog.set_level(logging.INFO) + yield diff --git a/tests/test_api_endpoints.py b/tests/test_api_endpoints.py new file mode 100644 index 0000000000000000000000000000000000000000..0bd1e89be276f0600dd6e42ae132211d7054cad1 --- /dev/null +++ b/tests/test_api_endpoints.py @@ -0,0 +1,260 @@ +import os +import unittest +from unittest.mock import MagicMock + +import numpy as np +import pandas as pd + +# FastAPI related imports +from fastapi.testclient import TestClient + +import src.book_recommender.core.config as config +from src.book_recommender.api.dependencies import ( # Import actual dependencies to override + get_clusters_data, + get_recommender, + get_sentence_transformer_model, +) +from src.book_recommender.api.main import app as fastapi_app # Import the FastAPI app instance +from src.book_recommender.ml.recommender import BookRecommender + + +class TestFastAPIEndpoints(unittest.TestCase): + + dummy_processed_data = pd.DataFrame( + { + "id": ["1", "2", "3", "4", "5"], + "title": ["Test Book 1", "Test Book 2", "Related Test Book 1", "Unrelated Book", "Sci-Fi Classic"], + "authors": ["Author A", "Author B", "Author A", "Author C", "Author D"], + "genres": ["Fiction", "Science Fiction", "Fiction", "Fantasy", "Science Fiction"], + "description": [ + "Description for book 1", + "Description for book 2 with sci-fi themes", + "Another book by Author A", + "A magical adventure", + "Deep space exploration", + ], + "title_lower": ["test book 1", "test book 2", "related test book 1", "unrelated book", "sci-fi classic"], + "authors_lower": ["author a", "author b", "author a", "author c", "author d"], + "combined_text": [ + "test book 1 by author a. genres: fiction. " "description: description for book 1. tags: ", + "test book 2 by author b. genres: science fiction. " + "description: description for book 2 with sci-fi themes. tags: ", + "related test book 1 by author a. genres: fiction. " "description: another book by author a. tags: ", + "unrelated book by author c. genres: fantasy. " "description: a magical adventure. tags: ", + "sci-fi classic by author d. genres: science fiction. " "description: deep space exploration. tags: ", + ], + "cluster_id": [0, 1, 0, 2, 1], + } + ) + dummy_embeddings = np.random.rand(len(dummy_processed_data), config.EMBEDDING_DIMENSION).astype("float32") + + dummy_cluster_names = {0: "Fiction Collection", 1: "Sci-Fi Collection", 2: "Fantasy Collection"} + + def setUp(self): + super().setUp() + + # Clear lru_cache for dependencies + get_recommender.cache_clear() + get_sentence_transformer_model.cache_clear() + get_clusters_data.cache_clear() + + # 1. Create mock objects for dependencies + self.mock_model = MagicMock() + self.mock_recommender_instance = MagicMock( + spec=BookRecommender + ) # Note: this is the instance returned by get_recommender + self.mock_clusters_data_value = ( # Note: this is the actual value returned by get_clusters_data + self.dummy_processed_data["cluster_id"].values, + self.dummy_cluster_names, + self.dummy_processed_data, + ) + + # 2. Configure mock behaviors + self.mock_model.encode.side_effect = lambda x, **kwargs: ( + np.array([np.random.rand(config.EMBEDDING_DIMENSION)]).astype("float32") + if isinstance(x, str) + else np.random.rand(len(x), config.EMBEDDING_DIMENSION).astype("float32") + ) + + self.mock_recommender_instance.book_data = self.dummy_processed_data + self.mock_recommender_instance.embeddings = self.dummy_embeddings + self.mock_recommender_instance.get_recommendations.return_value = [ + { + "id": "3", + "title": "Related Test Book 1", + "authors": "Author A", + "description": "Another book by Author A", + "genres": "Fiction", + "similarity": 0.8, + }, + { + "id": "1", + "title": "Test Book 1", + "authors": "Author A", + "description": "Description for book 1", + "genres": "Fiction", + "similarity": 0.7, + }, + ] + self.mock_recommender_instance.get_recommendations_from_vector.return_value = [ + { + "id": "5", + "title": "Sci-Fi Classic", + "authors": "Author D", + "description": "Deep space exploration", + "genres": "Science Fiction", + "similarity": 0.9, + }, + { + "id": "2", + "title": "Test Book 2", + "authors": "Author B", + "description": "Description for book 2 with sci-fi themes", + "genres": "Science Fiction", + "similarity": 0.8, + }, + ] + + # 3. Override dependencies for the FastAPI app + fastapi_app.dependency_overrides[get_sentence_transformer_model] = lambda: self.mock_model + fastapi_app.dependency_overrides[get_recommender] = ( + lambda: self.mock_recommender_instance + ) # Return the instance + fastapi_app.dependency_overrides[get_clusters_data] = ( + lambda: self.mock_clusters_data_value + ) # Return the actual value + + # self.client needs to be created *after* the dependencies are mocked + self.client = TestClient(fastapi_app) + + # Ensure feedback file is clean before each test + feedback_file_path = os.path.join(config.BASE_DIR, "data", "feedback", "user_feedback.jsonl") + if os.path.exists(feedback_file_path): + os.remove(feedback_file_path) + + def tearDown(self): + # Clear dependency overrides after each test + fastapi_app.dependency_overrides = {} + super().tearDown() + + def test_health_check(self): + response = self.client.get("/health") + self.assertEqual(response.status_code, 200) + self.assertEqual( + response.json(), {"status": "OK", "message": "BookFinder API is healthy and core services are loaded."} + ) + + def test_recommend_by_query(self): + response = self.client.post("/recommend/query", json={"query": "sci-fi books", "top_k": 2}) + self.assertEqual(response.status_code, 200) + recommendations = response.json() + self.assertEqual(len(recommendations), 2) + self.assertEqual(recommendations[0]["book"]["title"], "Sci-Fi Classic") + self.assertEqual(recommendations[1]["book"]["title"], "Test Book 2") + + def test_recommend_by_title(self): + response = self.client.post("/recommend/title", json={"title": "Test Book 1", "top_k": 2}) + self.assertEqual(response.status_code, 200) + recommendations = response.json() + self.assertEqual(len(recommendations), 2) + self.assertEqual(recommendations[0]["book"]["title"], "Related Test Book 1") + self.assertEqual(recommendations[1]["book"]["title"], "Test Book 1") + + def test_list_books(self): + response = self.client.get("/books?page=1&page_size=3") + self.assertEqual(response.status_code, 200) + data = response.json() + self.assertEqual(data["total"], 5) + self.assertEqual(len(data["books"]), 3) + self.assertEqual(data["books"][0]["title"], "Test Book 1") + + def test_search_books(self): + response = self.client.get("/books/search?query=author A&page=1&page_size=5") + self.assertEqual(response.status_code, 200) + data = response.json() + self.assertEqual(data["total"], 2) + self.assertEqual(len(data["books"]), 2) + self.assertEqual(data["books"][0]["title"], "Test Book 1") + self.assertEqual(data["books"][1]["title"], "Related Test Book 1") + + def test_get_stats(self): + response = self.client.get("/stats") + self.assertEqual(response.status_code, 200) + data = response.json() + self.assertEqual(data["total_books"], 5) + self.assertIn("fiction", data["genres_count"]) + self.assertIn("author a", data["authors_count"]) + + def test_list_clusters(self): + response = self.client.get("/clusters") + self.assertEqual(response.status_code, 200) + clusters = response.json() + self.assertEqual(len(clusters), 3) + self.assertEqual(clusters[0]["name"], "Fiction Collection") + self.assertEqual(clusters[0]["size"], 2) # Test Book 1, Related Test Book 1 + self.assertEqual(len(clusters[0]["top_books"]), 2) # Should sample up to 3, but there are only 2 + + def test_get_books_in_cluster(self): + response = self.client.get("/clusters/0?page=1&page_size=10") + self.assertEqual(response.status_code, 200) + data = response.json() + self.assertEqual(data["total"], 2) + self.assertEqual(len(data["books"]), 2) + self.assertEqual(data["books"][0]["title"], "Test Book 1") + + def test_get_cluster_sample(self): + response = self.client.get("/clusters/1/sample?sample_size=1") + self.assertEqual(response.status_code, 200) + books = response.json() + self.assertEqual(len(books), 1) + self.assertEqual(books[0]["genres"], ["Science Fiction"]) # Only Sci-Fi books in cluster 1 + + def test_explain_recommendation_endpoint(self): + sample_book = { + "id": "1", + "title": "Test Book 1", + "authors": ["Author A"], + "description": "Description for book 1", + "genres": ["Fiction"], + "cover_image_url": None, + } + response = self.client.post( + "/explain", + json={"query_text": "A book about fiction", "recommended_book": sample_book, "similarity_score": 0.75}, + ) + self.assertEqual(response.status_code, 200) + explanation = response.json() + self.assertIn("match_score", explanation) + self.assertIn("confidence", explanation) + self.assertIn("summary", explanation) + self.assertIn("details", explanation) + + def test_submit_feedback_and_get_stats(self): + # Submit feedback + feedback_payload = { + "query": "fantasy adventure", + "book_id": "4", # Unrelated Book + "feedback_type": "positive", + "session_id": "test_session_1", + } + response = self.client.post("/feedback", json=feedback_payload) + self.assertEqual(response.status_code, 204) # No Content + + feedback_payload_2 = { + "query": "sci-fi classic", + "book_id": "5", # Sci-Fi Classic + "feedback_type": "negative", + "session_id": "test_session_1", + } + response = self.client.post("/feedback", json=feedback_payload_2) + self.assertEqual(response.status_code, 204) # No Content + + # Get stats + response = self.client.get("/feedback/stats") + self.assertEqual(response.status_code, 200) + stats = response.json() + self.assertEqual(stats["total_feedback"], 2) + self.assertEqual(stats["positive_feedback"], 1) + self.assertEqual(stats["negative_feedback"], 1) + self.assertIn("Unrelated Book", stats["feedback_by_book_title"]) + self.assertIn("fantasy adventure", stats["feedback_by_query"]) diff --git a/tests/test_app.py b/tests/test_app.py new file mode 100644 index 0000000000000000000000000000000000000000..bbad9ac0f8e5347260c6c9d182cb4b42cda6d695 --- /dev/null +++ b/tests/test_app.py @@ -0,0 +1,50 @@ +# tests/test_app.py +""" +Tests for the Streamlit application UI. + +Note: UI testing for Streamlit apps typically requires a dedicated library +like 'streamlit-testing-library'. These tests are placeholders to demonstrate +where UI tests would go and how they might be structured. +""" + +import unittest + + +class TestStreamlitApp(unittest.TestCase): + + def test_app_imports(self): + """ + A very basic test to ensure the app.py module can be imported + without syntax errors or immediate import errors. + """ + try: + from src.book_recommender.apps.main_app import main + + self.assertTrue(callable(main)) + except ImportError as e: + self.fail(f"Failed to import the Streamlit app: {e}") + + def test_header_renders(self): + """ + Placeholder for a test that would check if the header renders. + Using a real testing library, you would: + 1. Render the app. + 2. Query for the 'h1' element. + 3. Assert that its text content is correct. + """ + self.assertTrue(True) + + def test_recommendation_button_works(self): + """ + Placeholder for a test that would simulate a button click. + Using a real testing library, you would: + 1. Render the app. + 2. Enter text into the text input. + 3. Simulate a click on the 'Recommend' button. + 4. Assert that the recommendation output appears. + """ + self.assertTrue(True) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_data_processor.py b/tests/test_data_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..de066a61fc8d7b1451d4bb30fd5d327462e66452 --- /dev/null +++ b/tests/test_data_processor.py @@ -0,0 +1,85 @@ +# tests/test_data_processor.py + +import os +import tempfile +import unittest + +import pandas as pd + +from src.book_recommender.core.exceptions import DataNotFoundError, FileProcessingError +from src.book_recommender.data.processor import clean_and_prepare_data + + +class TestDataProcessor(unittest.TestCase): + + def setUp(self): + # Create a temporary directory for test artifacts + self.test_dir = tempfile.TemporaryDirectory() + self.raw_path = os.path.join(self.test_dir.name, "test_books.csv") + self.processed_path = os.path.join(self.test_dir.name, "test_books_cleaned.parquet") + + # Create a dummy CSV file + self.sample_data = { + "title": ["Book A", "Book B", None, "Book D"], + "authors": ["Author 1", "Author 2", "Author 3", "Author 4"], + "genres": ["['Fiction']", "['Sci-Fi', 'Fantasy']", "['History']", float("nan")], + "description": ["Desc A", "Desc B", "Desc C", "Desc D"], + "tags": ["['tag1']", "['tag2']", "['tag3']", "['tag4']"], + } + pd.DataFrame(self.sample_data).to_csv(self.raw_path, index=False) + + def tearDown(self): + # Clean up the temporary directory + self.test_dir.cleanup() + + def test_clean_and_prepare_data(self): + # Run the function to be tested + processed_df = clean_and_prepare_data(self.raw_path, self.processed_path) + + # 1. Test that the output file is created + self.assertTrue(os.path.exists(self.processed_path)) + + # 2. Test that rows with no title are dropped + self.assertEqual(len(processed_df), 3) + self.assertNotIn(None, processed_df["title_lower"]) + + # 3. Test that NaN values are filled and list-strings are parsed + self.assertFalse(processed_df["genres"].isnull().any()) + self.assertEqual(processed_df.iloc[0]["genres"], "fiction") + + # 4. Test that 'combined_text' column is created and is correct + self.assertIn("combined_text", processed_df.columns) + + # Check the content for the first valid book + expected_text = "book a book a book a by author 1. genres: fiction. description: desc a. tags: tag1" + self.assertEqual(processed_df.iloc[0]["combined_text"], expected_text) + + def test_missing_raw_data_file(self): + """Test that DataNotFoundError is raised if the raw data file is missing.""" + with self.assertRaises(DataNotFoundError) as cm: + clean_and_prepare_data(raw_path="non_existent_file.csv", processed_path=self.processed_path) + self.assertIsInstance(cm.exception, DataNotFoundError) + + def test_empty_dataframe(self): + """Test that a ValueError is raised if the dataframe is empty after cleaning.""" + # Create an empty csv + empty_df = pd.DataFrame(columns=["title", "authors", "genres", "description", "tags"]) + empty_df.to_csv(self.raw_path, index=False) + + with self.assertRaises(ValueError) as cm: + clean_and_prepare_data(raw_path=self.raw_path, processed_path=self.processed_path) + self.assertIsInstance(cm.exception, ValueError) + + def test_malformed_csv(self): + """Test that FileProcessingError is raised for a malformed CSV.""" + # Create a malformed CSV file + with open(self.raw_path, "w", encoding="utf-8") as f: + f.write('title,authors\n"Book","Author"\n"Another Book') # Missing closing quote + + with self.assertRaises(FileProcessingError) as cm: + clean_and_prepare_data(raw_path=self.raw_path, processed_path=self.processed_path) + self.assertIsInstance(cm.exception, FileProcessingError) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_embedder.py b/tests/test_embedder.py new file mode 100644 index 0000000000000000000000000000000000000000..b580e5dd239a5697e4ca36714f615ddd8b4431ca --- /dev/null +++ b/tests/test_embedder.py @@ -0,0 +1,61 @@ +# tests/test_embedder.py + +import os +import tempfile +import unittest + +import numpy as np # Changed from pickle +import pandas as pd + +import src.book_recommender.core.config as config +from src.book_recommender.data.processor import clean_and_prepare_data +from src.book_recommender.ml.embedder import generate_embeddings + + +class TestEmbedder(unittest.TestCase): + + def setUp(self): + self.test_dir = tempfile.TemporaryDirectory() + self.raw_path = os.path.join(self.test_dir.name, "test_books.csv") + self.processed_path = os.path.join(self.test_dir.name, "test_books_cleaned.parquet") # Changed to .parquet + self.embeddings_path = os.path.join(self.test_dir.name, "test_embeddings.npy") # Changed to .npy + + # Create dummy data and process it + sample_data = { + "title": ["Book A", "Book B", "Book C"], + "authors": ["Author A", "Author B", "Author C"], + "genres": ["Fiction", "Sci-Fi", "History"], + "description": ["Desc A", "Desc B", "Desc C"], + "tags": ["tag1", "tag2", "tag3"], + } + pd.DataFrame(sample_data).to_csv(self.raw_path, index=False) + clean_and_prepare_data(self.raw_path, self.processed_path) + + def tearDown(self): + self.test_dir.cleanup() + + def test_generate_embeddings(self): + # Load the processed DataFrame first + processed_df = pd.read_parquet(self.processed_path) # Load DataFrame + + # Run the function with correct signature + embeddings = generate_embeddings(processed_df, show_progress_bar=False) # Pass DataFrame + + # Save embeddings + np.save(self.embeddings_path, embeddings) + + # 1. Test that the output file is created + self.assertTrue(os.path.exists(self.embeddings_path)) + + # 2. Test that the embeddings shape is correct + expected_rows = 3 + expected_dims = config.EMBEDDING_DIMENSION + self.assertEqual(embeddings.shape, (expected_rows, expected_dims)) + + # 3. Test that the saved file can be loaded and has the same shape + loaded_embeddings = np.load(self.embeddings_path) # Use np.load instead of pickle + self.assertEqual(loaded_embeddings.shape, (expected_rows, expected_dims)) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_integration.py b/tests/test_integration.py new file mode 100644 index 0000000000000000000000000000000000000000..0a265e818d8be495e30c3a797aae88fcd106763c --- /dev/null +++ b/tests/test_integration.py @@ -0,0 +1,88 @@ +# tests/test_integration.py + +import os +import tempfile +import unittest + +import pandas as pd + +# FastAPI related imports +import src.book_recommender.core.config as config +from src.book_recommender.data.processor import process_dataframe +from src.book_recommender.ml.embedder import generate_embeddings +from src.book_recommender.ml.recommender import BookRecommender + + +class TestIntegration(unittest.TestCase): + + def setUp(self): + self.test_dir = tempfile.TemporaryDirectory() + + self.raw_csv_path = os.path.join(self.test_dir.name, "test_books.csv") + + # Create a dummy CSV file for the test + sample_data = { + "title": ["The Sun Also Rises", "A Farewell to Arms", "For Whom the Bell Tolls", "The Old Man and the Sea"], + "authors": ["Ernest Hemingway", "Ernest Hemingway", "Ernest Hemingway", "Ernest Hemingway"], + "genres": ["Fiction", "War, Fiction", "War, Fiction", "Fiction"], + "description": [ + "A story of American and British expatriates in Paris.", + "A love story during World War I.", + "An American in the Spanish Civil War.", + "An old fisherman struggles with a giant marlin.", + ], + "tags": ["lost generation", "war", "spain", "cuba"], + } + pd.DataFrame(sample_data).to_csv(self.raw_csv_path, index=False) + + def tearDown(self): + self.test_dir.cleanup() + + def test_full_pipeline(self): + """ + Tests the full data processing and recommendation pipeline end-to-end. + """ + # --- 1. Load Raw Data --- + raw_df = pd.read_csv(self.raw_csv_path) + self.assertEqual(len(raw_df), 4) + + # --- 2. Process DataFrame --- + processed_df = process_dataframe(raw_df) + self.assertEqual(len(processed_df), 4) + self.assertIn("combined_text", processed_df.columns) + + # --- 3. Generate Embeddings --- + embeddings = generate_embeddings(df=processed_df, model_name=config.EMBEDDING_MODEL) + self.assertEqual(embeddings.shape, (4, config.EMBEDDING_DIMENSION)) + + # --- 4. Initialize Recommender --- + recommender = BookRecommender(book_data=processed_df, embeddings=embeddings) + self.assertIsNotNone(recommender) + + # --- 5. Get Recommendations --- + # "A Farewell to Arms" and "For Whom the Bell Tolls" are both war novels by the same author, + # so they should be highly similar. + recommendations = recommender.get_recommendations("A Farewell to Arms", top_k=1) + + # --- 6. Assert Results --- + self.assertEqual(len(recommendations), 1) + + top_recommendation = recommendations[0] + + # The exact top book can vary with model updates, so we check for reasonableness. + # It should be another Hemingway novel from our list. + expected_titles = ["The Sun Also Rises", "For Whom the Bell Tolls", "The Old Man and the Sea"] + self.assertIn(top_recommendation["title"], expected_titles) + + # The similarity should be high, indicating a strong match. + # NOTE: The threshold is set lower (e.g., > 0.3) because with normalized + # text and the 'all-MiniLM-L6-v2' model, scores for even closely + # related documents might not be extremely high. This ensures the test + # is robust to minor model variations. + self.assertTrue( + top_recommendation["similarity"] > 0.3, f"Similarity score {top_recommendation['similarity']} is not > 0.3" + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_personalization_integration.py b/tests/test_personalization_integration.py new file mode 100644 index 0000000000000000000000000000000000000000..0ea8fd4603054c2c728287c1336e9ee76fdd6e4b --- /dev/null +++ b/tests/test_personalization_integration.py @@ -0,0 +1,99 @@ +import unittest +from unittest.mock import MagicMock, patch +import pandas as pd +from fastapi.testclient import TestClient +import sys +import os + +# Add project root to path +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) + +from src.book_recommender.api.main import app, get_recommender +from src.book_recommender.services.personalizer import PersonalizationService +from src.book_recommender.ml.recommender import BookRecommender + +class TestPersonalizationIntegration(unittest.TestCase): + + def setUp(self): + # Mock Recommender Data + self.mock_recommender = MagicMock(spec=BookRecommender) + self.mock_recommender.book_data = pd.DataFrame({ + "id": ["1", "2"], + "title": ["Test Book A", "Test Book B"], + "authors": ["Author A", "Author B"], + "description": ["Desc A", "Desc B"], + "genres": ["Genre A", "Genre B"], + "cover_image_url": ["http://img.com/a.jpg", None] + }) + + # Mock Personalization Service + self.mock_personalizer = MagicMock(spec=PersonalizationService) + + # Override dependencies + app.dependency_overrides[get_recommender] = lambda: self.mock_recommender + + # Patch the personalizer instance in the api module + self.patcher = patch("src.book_recommender.api.main.personalizer", self.mock_personalizer) + self.patcher.start() + + self.client = TestClient(app) + + def tearDown(self): + app.dependency_overrides = {} + self.patcher.stop() + + def test_personalize_endpoint_success(self): + # Setup mock return value from personalization service + self.mock_personalizer.get_recommendations.return_value = [ + {"title": "Test Book A", "score": 0.9, "genres": "Genre A"}, + {"title": "Test Book B", "score": 0.8, "genres": "Genre B"} + ] + + payload = { + "user_history": ["Some Old Book"], + "top_k": 2 + } + + response = self.client.post("/recommend/personalize", json=payload) + + self.assertEqual(response.status_code, 200) + results = response.json() + + self.assertEqual(len(results), 2) + self.assertEqual(results[0]["book"]["title"], "Test Book A") + self.assertEqual(results[0]["similarity_score"], 0.9) + self.assertEqual(results[1]["book"]["title"], "Test Book B") + + # Check if cover image logic worked (Book B had None, but we didn't mock load_book_covers_batch so it might stay None or fail if called) + # Actually, load_book_covers_batch is imported in main.py. We might need to mock it if we want to test cover fetching. + # But for basic integration, this is enough. + + def test_personalize_endpoint_empty_response(self): + self.mock_personalizer.get_recommendations.return_value = [] + + payload = { + "user_history": ["Some Old Book"], + "top_k": 2 + } + + response = self.client.post("/recommend/personalize", json=payload) + + self.assertEqual(response.status_code, 200) + self.assertEqual(response.json(), []) + + def test_personalize_endpoint_service_failure(self): + # Simulate service returning None or raising exception (though wrapper usually catches it) + # The wrapper in main checks "if not semantic_recs: return []" + self.mock_personalizer.get_recommendations.return_value = [] + + payload = { + "user_history": ["Some Old Book"], + "top_k": 2 + } + + response = self.client.post("/recommend/personalize", json=payload) + self.assertEqual(response.status_code, 200) + self.assertEqual(response.json(), []) + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_recommender.py b/tests/test_recommender.py new file mode 100644 index 0000000000000000000000000000000000000000..c5a5dc0aa33f05fe0088b5f9b0454dd9ff963e4b --- /dev/null +++ b/tests/test_recommender.py @@ -0,0 +1,92 @@ +# tests/test_recommender.py + +import unittest + +import numpy as np +import pandas as pd + +import src.book_recommender.core.config as config # Import config for EMBEDDING_DIMENSION +from src.book_recommender.ml.recommender import BookRecommender + + +class TestRecommender(unittest.TestCase): + + def setUp(self): + # Create dummy data directly in memory + self.book_data = pd.DataFrame( + { + "id": ["1", "2", "3", "4", "5"], + "title": ["Alpha Book", "Beta Book", "Gamma Book", "Delta Book", "Epsilon Book"], + "title_lower": ["alpha book", "beta book", "gamma book", "delta book", "epsilon book"], + "authors": ["Auth1", "Auth2", "Auth3", "Auth1", "Auth5"], + "authors_lower": ["auth1", "auth2", "auth3", "auth1", "auth5"], + "description": ["Desc1", "Desc2", "Desc3", "Desc1", "Desc5"], + "genres": ["Fiction", "Sci-Fi", "History", "Fiction", "Fantasy"], + "tags": ["tag1", "tag2", "tag3", "tag4", "tag5"], + "rating": [4.5, 4.0, 4.8, 4.3, 4.6], + "combined_text": ["alpha", "beta", "gamma", "delta", "epsilon"], + } + ) + + # Create dummy embeddings (simple, not from a real model) + # Make 'alpha book' very similar to 'delta book' + # Make 'beta book' very similar to 'epsilon book' + self.embeddings = np.array( + [ + [0.9, 0.1, 0.1, 0.1, 0.1], # alpha + [0.1, 0.9, 0.1, 0.1, 0.1], # beta + [0.1, 0.1, 0.9, 0.1, 0.1], # gamma + [0.85, 0.15, 0.1, 0.1, 0.1], # delta (similar to alpha) + [0.1, 0.8, 0.1, 0.1, 0.1], # epsilon (similar to beta) + ] + ) + # Pad embeddings to match expected dimension if necessary + if self.embeddings.shape[1] < config.EMBEDDING_DIMENSION: + padding = np.zeros((self.embeddings.shape[0], config.EMBEDDING_DIMENSION - self.embeddings.shape[1])) + self.embeddings = np.hstack((self.embeddings, padding)) + + # Initialize the recommender with in-memory data + self.recommender = BookRecommender(book_data=self.book_data, embeddings=self.embeddings) + + def test_get_recommendations(self): + # Test with a book that exists + recs = self.recommender.get_recommendations("alpha book", top_k=1, similarity_threshold=0.0) + + # 1. Should return 1 recommendation + self.assertEqual(len(recs), 1) + + # 2. The input book itself should not be in the recommendations + rec_titles = [r["title"] for r in recs] + self.assertNotIn("alpha book", rec_titles) + + # 3. The most similar book ('delta book') should be first + self.assertEqual(recs[0]["title"], "Delta Book") + + # 4. Check the structure of the output + self.assertIsInstance(recs[0], dict) + self.assertIn("title", recs[0]) + self.assertIn("authors", recs[0]) + self.assertIn("description", recs[0]) + self.assertIn("genres", recs[0]) + self.assertIn("tags", recs[0]) + self.assertIn("rating", recs[0]) + self.assertIn("similarity", recs[0]) + + def test_get_recommendations_non_existent_title(self): + # Test with a book title that does not exist + recs = self.recommender.get_recommendations("non existent book") + self.assertEqual(len(recs), 0) + + def test_recommender_accuracy(self): + """ + Tests if the recommender returns expected similar books based on dummy embeddings. + """ + # Test for 'beta book', expecting 'epsilon book' as the most similar + recs = self.recommender.get_recommendations("beta book", top_k=1) + self.assertEqual(len(recs), 1) + self.assertEqual(recs[0]["title"], "Epsilon Book") + self.assertTrue(recs[0]["similarity"] > 0.7) # Expect a high similarity for a known similar book + + +if 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