{"id":48819229,"url":"https://github.com/clever-boy/productclassification","last_synced_at":"2026-04-14T14:01:29.327Z","repository":{"id":316001148,"uuid":"1061530007","full_name":"Clever-Boy/Productclassification","owner":"Clever-Boy","description":"Comprehensive product analysis and recommendation system with JSON data processing, visual analytics, and machine learning.","archived":false,"fork":false,"pushed_at":"2025-09-22T04:27:11.000Z","size":1254,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-09-22T06:13:28.930Z","etag":null,"topics":["data-visualization","json-processing","machine-learning","product-analysis","python","recommendation-system"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Clever-Boy.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-09-22T03:39:46.000Z","updated_at":"2025-09-22T04:27:14.000Z","dependencies_parsed_at":"2025-09-22T17:46:07.217Z","dependency_job_id":null,"html_url":"https://github.com/Clever-Boy/Productclassification","commit_stats":null,"previous_names":["clever-boy/productclassification"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/Clever-Boy/Productclassification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Clever-Boy%2FProductclassification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Clever-Boy%2FProductclassification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Clever-Boy%2FProductclassification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Clever-Boy%2FProductclassification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Clever-Boy","download_url":"https://codeload.github.com/Clever-Boy/Productclassification/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Clever-Boy%2FProductclassification/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31799411,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-14T11:13:53.975Z","status":"ssl_error","status_checked_at":"2026-04-14T11:13:53.299Z","response_time":153,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-visualization","json-processing","machine-learning","product-analysis","python","recommendation-system"],"created_at":"2026-04-14T14:01:28.457Z","updated_at":"2026-04-14T14:01:29.310Z","avatar_url":"https://github.com/Clever-Boy.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Product Classification System\n\n[![Python](https://img.shields.io/badge/Python-3.8+-blue.svg)](https://python.org)\n[![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)\n[![Stars](https://img.shields.io/github/stars/Clever-Boy/Productclassification.svg)](https://github.com/Clever-Boy/Productclassification/stargazers)\n[![Forks](https://img.shields.io/github/forks/Clever-Boy/Productclassification.svg)](https://github.com/Clever-Boy/Productclassification/network)\n[![Issues](https://img.shields.io/github/issues/Clever-Boy/Productclassification.svg)](https://github.com/Clever-Boy/Productclassification/issues)\n\nA comprehensive product analysis and recommendation system that works with JSON data files. Features 12+ analysis engines, visual recommendations, Excel export, and interactive Python charts.\n\n## ✨ **Why This Repository?**\n\n🎯 **Comprehensive Analysis**: Extract 15+ product attributes including sustainability, materials, style, pricing, brand analysis, dimensions, care instructions, target market, seasonal trends, quality assessment, and usage recommendations\n\n🖼️ **Visual Recommendations**: Interactive matplotlib-based product comparisons with automatic image downloading and high-quality PNG export\n\n📊 **Professional Reports**: Multi-sheet Excel exports with comprehensive data and interactive Python visualizations\n\n🤖 **No TensorFlow Required**: Lightweight alternatives using scikit-learn and PIL for easy deployment\n\n🏗️ **Professional Architecture**: Complete system documentation with Mermaid diagrams, component interactions, and data flow charts\n\n⚡ **Easy to Use**: Simple configuration file setup - just add your JSON file paths and run!\n\n## 🚀 Quick Start\n\n### 1. Install Dependencies\n```bash\npip install -r requirements.txt\n```\n\n### 2. Configure Your Product Lists\nEdit `examples/product_lists.txt` to add your JSON file paths:\n```\n# Product Lists Configuration\n# Just add your JSON file paths - everything else is auto-detected!\n\nC:\\Users\\YourName\\Downloads\\styles\\product1.json\nC:\\Users\\YourName\\Downloads\\styles\\product2.json\n```\n\n### 3. Run Analysis\n```bash\n# Analyze all products\npython src/multi_product_analyzer.py --product-lists-config examples/product_lists.txt\n\n# Create tabular charts with Python visualizations\npython src/tabular_product_analyzer.py --product-lists-config examples/product_lists.txt\n\n# Get product recommendations with visual display\npython src/product_recommender_json.py --config-file examples/product_lists.txt --analyze\npython src/product_recommender_json.py --config-file examples/product_lists.txt --recommend \"PRODUCT_ID_HERE\"\n```\n\n## 📁 Project Structure\n\n```\nProductClassification/\n├── src/                          # Main source code\n│   ├── json_data_loader.py       # JSON data loading utility\n│   ├── multi_product_analyzer.py # Multi-file product analyzer\n│   ├── product_analyzer_from_file.py # Single file product analyzer\n│   ├── product_analyzer_json.py  # JSON-based product analyzer\n│   ├── tabular_product_analyzer.py # Tabular chart generator\n│   ├── product_recommender_json.py # Visual product recommendation system\n│   ├── image_classifier_json.py  # Image classification\n│   ├── combined_classifier_json.py # Combined text + image classifier\n│   ├── simple_text_classifier.py # Text classification\n│   ├── basic_text_analyzer.py    # Basic text analysis\n│   ├── categorize_words_json.py  # Word categorization\n│   ├── images/                   # Cached product images and recommendation visualizations\n│   │   ├── product images (7 files) # Downloaded product images\n│   │   └── recommendations_*.png # Visual recommendation charts\n│   ├── 10045_401097928176_analysis.txt # Generated analysis report\n│   └── product_analysis_charts.xlsx # Generated Excel charts\n├── examples/                     # Example files and configurations\n│   ├── product_lists.txt        # Your product lists configuration\n│   ├── example_product_lists.txt # Example configuration template\n│   ├── run_analysis.py          # Example analysis script\n│   ├── run_examples.bat         # Windows batch file for examples\n│   ├── analysis_results.txt     # Example analysis output\n│   ├── comprehensive_analysis.xlsx # Example Excel output\n│   ├── comprehensive_analysis_updated.xlsx # Updated example\n│   └── comprehensive_analysis_with_python_charts.xlsx # Python charts example\n├── results/                      # Legacy analysis results\n│   ├── categories.txt           # Category data\n│   ├── image-classification-top500-errors.html # Image classification errors\n│   └── text-classification-top500-errors.html # Text classification errors\n├── architecture/                 # System architecture documentation\n│   ├── system_architecture.md   # Main architecture overview\n│   ├── component_diagram.md     # Component interactions\n│   └── data_flow.md            # Data flow documentation\n├── requirements.txt             # Python dependencies\n├── config.env.example          # Environment configuration template\n└── README.md                   # This documentation file\n```\n\n## 🎯 Main Features\n\n### 📊 **Product Analysis**\n- **Multi-file Analysis**: Analyze multiple JSON files simultaneously\n- **Comprehensive Metrics**: Extract 15+ product attributes including sustainability, materials, style, pricing, brand analysis, dimensions, care instructions, target market, seasonal trends, quality assessment, and usage recommendations\n- **Inventory Analysis**: Track SKU numbers, stock status, quantities, and locations\n\n### 📈 **Visualization \u0026 Reporting**\n- **Tabular Charts**: Generate comprehensive Excel reports with multiple sheets\n- **Python Charts**: Interactive matplotlib visualizations including:\n  - Stock Status Distribution\n  - Price Range Analysis\n  - Brand Tier Distribution\n  - Sustainability Analysis (Yes/No counts)\n  - Inventory Categories\n  - Market Segment Analysis\n- **Excel Export**: Professional Excel files with formatted data\n\n### 🎨 **Product Recommendations**\n- **Visual Display**: Show target product and 5 recommendations with images\n- **Multiple Similarity Types**: Text-only, image-only, or combined similarity\n- **Detailed Explanations**: Explain why products are similar\n- **Image Processing**: Download and analyze product images automatically\n\n### 🤖 **Classification Systems**\n- **Text Classification**: Analyze product descriptions and names\n- **Image Classification**: Extract visual features from product images\n- **Combined Analysis**: Merge text and image features for comprehensive analysis\n- **No TensorFlow Required**: Lightweight alternatives using scikit-learn and PIL\n\n## 🔧 Configuration\n\n### Environment Variables\nCopy `config.env.example` to `config.env` and configure:\n```\nSHOPSTYLE_API_KEY=your_api_key_here\nDATABASE_URL=sqlite:///crawl.db\n```\n\n### Product Lists Configuration\nThe `examples/product_lists.txt` file supports:\n- Simple file path listing\n- Automatic list name generation\n- Auto-detection of categories and output files\n- Comment support with `#`\n\n## 📋 Usage Examples\n\n### Analyze All Products\n```bash\npython src/multi_product_analyzer.py --product-lists-config examples/product_lists.txt\n```\n\n### Generate Comprehensive Charts\n```bash\npython src/tabular_product_analyzer.py --product-lists-config examples/product_lists.txt --output-file my_analysis.xlsx\n```\n\n### Get Product Recommendations\n```bash\n# First, analyze to see available products\npython src/product_recommender_json.py --config-file examples/product_lists.txt --analyze\n\n# Then get recommendations for a specific product\npython src/product_recommender_json.py --config-file examples/product_lists.txt --recommend \"productID\" --top-k 5\n```\n\n### Text Classification\n```bash\npython src/simple_text_classifier.py --json-file path/to/your/products.json\n```\n\n### Image Classification\n```bash\npython src/image_classifier_json.py --json-file path/to/your/products.json\n```\n\n## 🎨 Visual Features\n\nThe recommendation system displays:\n- **Target Product**: Large, centered display with image and description\n- **5 Recommendations**: Side-by-side layout with similarity scores\n- **Image Support**: Automatic image downloading and display\n- **Fallback Handling**: Placeholder images for missing or broken images\n- **Professional Layout**: Clean, organized visual presentation\n\n## 📊 Output Examples\n\n### Analysis Results\n- **Excel Reports**: Multi-sheet workbooks with comprehensive data (`src/product_analysis_charts.xlsx`)\n- **Python Charts**: Interactive visualizations displayed in separate windows\n- **Text Reports**: Detailed analysis summaries (`src/{list_name}_analysis.txt`)\n- **Image Cache**: Automatically cached product images (`src/images/`)\n\n### Recommendation Output\n- **Visual Display**: Matplotlib-based product comparison\n- **Similarity Scores**: Numerical similarity ratings\n- **Explanations**: Detailed reasoning for recommendations\n- **Product Details**: Names, descriptions, categories, and images\n- **Saved Visualizations**: PNG files automatically saved to `src/images/` (e.g., `recommendations_prod261180192.png`)\n\n## 🎯 Real Analysis Example\n\nHere's an actual analysis output from the system analyzing 6 products:\n\n## 🖼️ Visual Recommendation Examples\n\nThe product recommendation system generates visual comparisons and saves them as high-quality PNG files:\n\n### **Live Demo**\n🎬 **See it in action!** The system creates professional visualizations like this:\n\n![Product Recommendations Example](src/images/recommendations_prod250457851.png)\n\n\u003e **Try it yourself**: Run `python src/product_recommender_json.py --config-file examples/product_lists.txt --recommend \"productID\"` to generate your own visual recommendations!\n\n### **Visual Layout**\n- **Top Row**: Target product (large, centered display)\n- **Bottom Row**: 5 recommended products with similarity scores\n- **High Resolution**: 300 DPI PNG files for crisp display\n- **Professional Layout**: Clean, organized presentation with product details\n\n### **File Naming Convention**\n- Format: `recommendations_{PRODUCT_ID}.png`\n- Example: `recommendations_prod261180192.png`\n- Location: `src/images/` directory\n- Automatic: Generated every time you run recommendations\n\n### 📋 **Sample Product Analysis:**\n\n**🎯 PRODUCT: DRESSING FLORAL ITALIAN BRIEF**\n- **ID**: prod285360089\n- **Category**: Women\n- **Materials**: cotton, polyester, nylon\n- **Style**: floral design\n- **Price**: mid-range ($120)\n- **Brand**: Lise Charmel\n- **Sustainability**: ❌ No (Score: 1/10)\n- **Market**: mass market, budget conscious\n- **Quality**: standard craftsmanship\n\n**🎯 PRODUCT: SK 3/4S BTNK LNG**\n- **ID**: prod205250129\n- **Category**: Women\n- **Materials**: silk\n- **Style**: Daytime occasions\n- **Price**: luxury ($1038)\n- **Brand**: Eskandar\n- **Sustainability**: ✅ Yes (Score: 3/10, sustainable materials: silk)\n- **Market**: premium market\n- **Quality**: high craftsmanship\n\n### 📊 **Summary Statistics:**\n- **Total Products**: 6\n- **Sustainable Products**: 1/6 (16.7%)\n- **Average Sustainability Score**: 2.0/10\n- **Price Distribution**: 4 premium, 1 mid-range, 1 luxury\n- **Quality Distribution**: 4 high quality, 2 unknown\n- **Market Distribution**: 4 premium market, 1 mass market, 1 luxury market\n- **Care Requirements**: 4 high maintenance, 2 unknown\n\n### 🎨 **Analysis Features Demonstrated:**\n✅ **Sustainability Analysis**: Identifies eco-friendly materials and practices\n✅ **Material Extraction**: Detects primary materials (cotton, silk, polyester, nylon)\n✅ **Price Analysis**: Categorizes products by price range and luxury level\n✅ **Brand Analysis**: Identifies brand names and reputation scores\n✅ **Market Segmentation**: Determines target demographics and market positioning\n✅ **Quality Assessment**: Evaluates craftsmanship and construction quality\n✅ **Care Instructions**: Provides maintenance recommendations\n✅ **Style Analysis**: Identifies design elements and occasions\n✅ **Dimensional Analysis**: Extracts size and weight information\n\n## 🛠️ Dependencies\n\n- **Core**: `numpy`, `pandas`, `matplotlib`, `seaborn`\n- **Image Processing**: `Pillow`\n- **Excel Export**: `openpyxl`\n- **HTTP Requests**: `requests`\n- **Database**: `sqlalchemy`\n- **Machine Learning**: `scikit-learn` (optional)\n\n## 📝 Notes\n\n- **No TensorFlow Required**: System uses lightweight alternatives\n- **JSON-Based**: Works directly with JSON product data files\n- **Cross-Platform**: Works on Windows, macOS, and Linux\n- **Extensible**: Easy to add new analysis features\n- **Professional**: Clean, organized codebase with proper documentation\n\n## 🚀 Getting Started\n\n1. **Clone/Download** this repository\n2. **Install dependencies**: `pip install -r requirements.txt`\n3. **Configure product lists**: Edit `examples/product_lists.txt` with your JSON file paths\n4. **Run analysis**: `python src/multi_product_analyzer.py --product-lists-config examples/product_lists.txt`\n5. **View results**: \n   - Check `src/{list_name}_analysis.txt` for detailed text reports\n   - Check `src/product_analysis_charts.xlsx` for comprehensive Excel data\n   - View Python charts displayed in separate windows\n   - Browse `src/images/` for cached product images\n   - Check `src/images/recommendations_*.png` for visual recommendation charts\n\n### 🎯 **Quick Test**\nRun the example script: `python examples/run_analysis.py` or double-click `examples/run_examples.bat`\n\n## 🏗️ Architecture Documentation\n\nFor detailed understanding of the system architecture:\n\n- **[System Architecture](architecture/system_architecture.md)**: Complete system overview with component diagrams\n- **[Component Interactions](architecture/component_diagram.md)**: Detailed component relationships and data flow\n- **[Data Flow](architecture/data_flow.md)**: How data moves through the system from input to output\n\nThe architecture documentation includes:\n- **Component Diagrams**: Visual representation of all system components\n- **Data Flow Charts**: How data flows through the processing pipeline\n- **Sequence Diagrams**: Step-by-step interaction flows\n- **Technical Details**: Design patterns, scalability, and performance considerations\n\n## 🌟 **Star This Repository**\n\nIf you found this project helpful, please give it a ⭐ star! It helps others discover this tool and motivates continued development.\n\n## 🤝 **Contributing**\n\nWe welcome contributions! See our [Contributing Guidelines](CONTRIBUTING.md) for details.\n\n- 🐛 **Found a bug?** [Open an issue](https://github.com/Clever-Boy/Productclassification/issues)\n- 💡 **Have an idea?** [Suggest a feature](https://github.com/Clever-Boy/Productclassification/issues)\n- 🔧 **Want to contribute?** [Submit a PR](https://github.com/Clever-Boy/Productclassification/pulls)\n\n## 📞 **Support**\n\n- 📖 **Documentation**: Check the [Architecture Documentation](architecture/) for detailed system overview\n- 💬 **Discussions**: Use GitHub Discussions for questions and ideas\n- 🐛 **Issues**: Report bugs and request features via Issues\n\n---\n\n**Made with ❤️ for the data science community**\n\nEnjoy analyzing your products! 🎉\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fclever-boy%2Fproductclassification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fclever-boy%2Fproductclassification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fclever-boy%2Fproductclassification/lists"}