Product Classification System

A comprehensive product analysis and recommendation system that works with JSON data files. Features 12+ analysis engines, visual recommendations, Excel export, and interactive Python charts.

✨ Why This Repository?

🎯 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

🖼️ Visual Recommendations: Interactive matplotlib-based product comparisons with automatic image downloading and high-quality PNG export

📊 Professional Reports: Multi-sheet Excel exports with comprehensive data and interactive Python visualizations

🤖 No TensorFlow Required: Lightweight alternatives using scikit-learn and PIL for easy deployment

🏗️ Professional Architecture: Complete system documentation with Mermaid diagrams, component interactions, and data flow charts

⚡ Easy to Use: Simple configuration file setup - just add your JSON file paths and run!

🚀 Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Configure Your Product Lists

Edit examples/product_lists.txt to add your JSON file paths:

# Product Lists Configuration
# Just add your JSON file paths - everything else is auto-detected!

C:\Users\YourName\Downloads\styles\product1.json
C:\Users\YourName\Downloads\styles\product2.json

3. Run Analysis

# Analyze all products
python src/multi_product_analyzer.py --product-lists-config examples/product_lists.txt

# Create tabular charts with Python visualizations
python src/tabular_product_analyzer.py --product-lists-config examples/product_lists.txt

# Get product recommendations with visual display
python src/product_recommender_json.py --config-file examples/product_lists.txt --analyze
python src/product_recommender_json.py --config-file examples/product_lists.txt --recommend "PRODUCT_ID_HERE"

📁 Project Structure

ProductClassification/
├── src/                          # Main source code
│   ├── json_data_loader.py       # JSON data loading utility
│   ├── multi_product_analyzer.py # Multi-file product analyzer
│   ├── product_analyzer_from_file.py # Single file product analyzer
│   ├── product_analyzer_json.py  # JSON-based product analyzer
│   ├── tabular_product_analyzer.py # Tabular chart generator
│   ├── product_recommender_json.py # Visual product recommendation system
│   ├── image_classifier_json.py  # Image classification
│   ├── combined_classifier_json.py # Combined text + image classifier
│   ├── simple_text_classifier.py # Text classification
│   ├── basic_text_analyzer.py    # Basic text analysis
│   ├── categorize_words_json.py  # Word categorization
│   ├── images/                   # Cached product images and recommendation visualizations
│   │   ├── product images (7 files) # Downloaded product images
│   │   └── recommendations_*.png # Visual recommendation charts
│   ├── 10045_401097928176_analysis.txt # Generated analysis report
│   └── product_analysis_charts.xlsx # Generated Excel charts
├── examples/                     # Example files and configurations
│   ├── product_lists.txt        # Your product lists configuration
│   ├── example_product_lists.txt # Example configuration template
│   ├── run_analysis.py          # Example analysis script
│   ├── run_examples.bat         # Windows batch file for examples
│   ├── analysis_results.txt     # Example analysis output
│   ├── comprehensive_analysis.xlsx # Example Excel output
│   ├── comprehensive_analysis_updated.xlsx # Updated example
│   └── comprehensive_analysis_with_python_charts.xlsx # Python charts example
├── results/                      # Legacy analysis results
│   ├── categories.txt           # Category data
│   ├── image-classification-top500-errors.html # Image classification errors
│   └── text-classification-top500-errors.html # Text classification errors
├── architecture/                 # System architecture documentation
│   ├── system_architecture.md   # Main architecture overview
│   ├── component_diagram.md     # Component interactions
│   └── data_flow.md            # Data flow documentation
├── requirements.txt             # Python dependencies
├── config.env.example          # Environment configuration template
└── README.md                   # This documentation file

🎯 Main Features

📊 Product Analysis

Multi-file Analysis: Analyze multiple JSON files simultaneously
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
Inventory Analysis: Track SKU numbers, stock status, quantities, and locations

📈 Visualization & Reporting

Tabular Charts: Generate comprehensive Excel reports with multiple sheets
Python Charts: Interactive matplotlib visualizations including:
- Stock Status Distribution
- Price Range Analysis
- Brand Tier Distribution
- Sustainability Analysis (Yes/No counts)
- Inventory Categories
- Market Segment Analysis
Excel Export: Professional Excel files with formatted data

🎨 Product Recommendations

Visual Display: Show target product and 5 recommendations with images
Multiple Similarity Types: Text-only, image-only, or combined similarity
Detailed Explanations: Explain why products are similar
Image Processing: Download and analyze product images automatically

🤖 Classification Systems

Text Classification: Analyze product descriptions and names
Image Classification: Extract visual features from product images
Combined Analysis: Merge text and image features for comprehensive analysis
No TensorFlow Required: Lightweight alternatives using scikit-learn and PIL

🔧 Configuration

Environment Variables

Copy config.env.example to config.env and configure:

SHOPSTYLE_API_KEY=your_api_key_here
DATABASE_URL=sqlite:///crawl.db

Product Lists Configuration

The examples/product_lists.txt file supports:

Simple file path listing
Automatic list name generation
Auto-detection of categories and output files
Comment support with #

📋 Usage Examples

Analyze All Products

python src/multi_product_analyzer.py --product-lists-config examples/product_lists.txt

Generate Comprehensive Charts

python src/tabular_product_analyzer.py --product-lists-config examples/product_lists.txt --output-file my_analysis.xlsx

Get Product Recommendations

# First, analyze to see available products
python src/product_recommender_json.py --config-file examples/product_lists.txt --analyze

# Then get recommendations for a specific product
python src/product_recommender_json.py --config-file examples/product_lists.txt --recommend "productID" --top-k 5

Text Classification

python src/simple_text_classifier.py --json-file path/to/your/products.json

Image Classification

python src/image_classifier_json.py --json-file path/to/your/products.json

🎨 Visual Features

The recommendation system displays:

Target Product: Large, centered display with image and description
5 Recommendations: Side-by-side layout with similarity scores
Image Support: Automatic image downloading and display
Fallback Handling: Placeholder images for missing or broken images
Professional Layout: Clean, organized visual presentation

📊 Output Examples

Analysis Results

Excel Reports: Multi-sheet workbooks with comprehensive data (src/product_analysis_charts.xlsx)
Python Charts: Interactive visualizations displayed in separate windows
Text Reports: Detailed analysis summaries (src/{list_name}_analysis.txt)
Image Cache: Automatically cached product images (src/images/)

Recommendation Output

Visual Display: Matplotlib-based product comparison
Similarity Scores: Numerical similarity ratings
Explanations: Detailed reasoning for recommendations
Product Details: Names, descriptions, categories, and images
Saved Visualizations: PNG files automatically saved to src/images/ (e.g., recommendations_prod261180192.png)

🎯 Real Analysis Example

Here's an actual analysis output from the system analyzing 6 products:

🖼️ Visual Recommendation Examples

The product recommendation system generates visual comparisons and saves them as high-quality PNG files:

Live Demo

🎬 See it in action! The system creates professional visualizations like this:

Try it yourself: Run python src/product_recommender_json.py --config-file examples/product_lists.txt --recommend "productID" to generate your own visual recommendations!

Visual Layout

Top Row: Target product (large, centered display)
Bottom Row: 5 recommended products with similarity scores
High Resolution: 300 DPI PNG files for crisp display
Professional Layout: Clean, organized presentation with product details

File Naming Convention

Format: recommendations_{PRODUCT_ID}.png
Example: recommendations_prod261180192.png
Location: src/images/ directory
Automatic: Generated every time you run recommendations

📋 Sample Product Analysis:

🎯 PRODUCT: DRESSING FLORAL ITALIAN BRIEF

ID: prod285360089
Category: Women
Materials: cotton, polyester, nylon
Style: floral design
Price: mid-range ($120)
Brand: Lise Charmel
Sustainability: ❌ No (Score: 1/10)
Market: mass market, budget conscious
Quality: standard craftsmanship

🎯 PRODUCT: SK 3/4S BTNK LNG

ID: prod205250129
Category: Women
Materials: silk
Style: Daytime occasions
Price: luxury ($1038)
Brand: Eskandar
Sustainability: ✅ Yes (Score: 3/10, sustainable materials: silk)
Market: premium market
Quality: high craftsmanship

📊 Summary Statistics:

Total Products: 6
Sustainable Products: 1/6 (16.7%)
Average Sustainability Score: 2.0/10
Price Distribution: 4 premium, 1 mid-range, 1 luxury
Quality Distribution: 4 high quality, 2 unknown
Market Distribution: 4 premium market, 1 mass market, 1 luxury market
Care Requirements: 4 high maintenance, 2 unknown

🎨 Analysis Features Demonstrated:

✅ Sustainability Analysis: Identifies eco-friendly materials and practices ✅ Material Extraction: Detects primary materials (cotton, silk, polyester, nylon) ✅ Price Analysis: Categorizes products by price range and luxury level ✅ Brand Analysis: Identifies brand names and reputation scores ✅ Market Segmentation: Determines target demographics and market positioning ✅ Quality Assessment: Evaluates craftsmanship and construction quality ✅ Care Instructions: Provides maintenance recommendations ✅ Style Analysis: Identifies design elements and occasions ✅ Dimensional Analysis: Extracts size and weight information

🛠️ Dependencies

Core: numpy, pandas, matplotlib, seaborn
Image Processing: Pillow
Excel Export: openpyxl
HTTP Requests: requests
Database: sqlalchemy
Machine Learning: scikit-learn (optional)

📝 Notes

No TensorFlow Required: System uses lightweight alternatives
JSON-Based: Works directly with JSON product data files
Cross-Platform: Works on Windows, macOS, and Linux
Extensible: Easy to add new analysis features
Professional: Clean, organized codebase with proper documentation

🚀 Getting Started

Clone/Download this repository
Install dependencies: pip install -r requirements.txt
Configure product lists: Edit examples/product_lists.txt with your JSON file paths
Run analysis: python src/multi_product_analyzer.py --product-lists-config examples/product_lists.txt
View results:
- Check src/{list_name}_analysis.txt for detailed text reports
- Check src/product_analysis_charts.xlsx for comprehensive Excel data
- View Python charts displayed in separate windows
- Browse src/images/ for cached product images
- Check src/images/recommendations_*.png for visual recommendation charts

🎯 Quick Test

Run the example script: python examples/run_analysis.py or double-click examples/run_examples.bat

🏗️ Architecture Documentation

For detailed understanding of the system architecture:

System Architecture: Complete system overview with component diagrams
Component Interactions: Detailed component relationships and data flow
Data Flow: How data moves through the system from input to output

The architecture documentation includes:

Component Diagrams: Visual representation of all system components
Data Flow Charts: How data flows through the processing pipeline
Sequence Diagrams: Step-by-step interaction flows
Technical Details: Design patterns, scalability, and performance considerations

🌟 Star This Repository

If you found this project helpful, please give it a ⭐ star! It helps others discover this tool and motivates continued development.

🤝 Contributing

We welcome contributions! See our Contributing Guidelines for details.

🐛 Found a bug? Open an issue
💡 Have an idea? Suggest a feature
🔧 Want to contribute? Submit a PR

📞 Support

📖 Documentation: Check the Architecture Documentation for detailed system overview
💬 Discussions: Use GitHub Discussions for questions and ideas
🐛 Issues: Report bugs and request features via Issues

Made with ❤️ for the data science community

Enjoy analyzing your products! 🎉

Name		Name	Last commit message	Last commit date
Latest commit History 37 Commits
architecture		architecture
examples		examples
results		results
src		src
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
config.env.example		config.env.example
my_analysis.xlsx		my_analysis.xlsx
requirements.txt		requirements.txt

License

Clever-Boy/Productclassification

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