A comprehensive Streamlit-powered analytics dashboard for analyzing CTC (Crush, Tear, Curl) tea sales data across North and South India markets. This application provides AI-powered market insights, statistical analysis, and interactive visualizations for tea auction data.
- Interactive Data Visualization: Dynamic charts showing price trends, volume analysis, and market efficiency
- Market Comparison: Compare different tea markets (North/South India, Leaf/Dust varieties)
- Statistical Analysis: Comprehensive analysis including correlations, trends, and market positions
- Data Processing: Support for Excel (.xlsx, .xls) and CSV file formats with intelligent column mapping
- Market Narrative: AI-generated market analysis and insights
- Price Analysis: Detailed price trend analysis with forecasting
- Market Intelligence: Competitive analysis and strategic recommendations
- Automated Reporting: AI-powered insights generation using OpenAI GPT models
- Multi-tab Interface: Organized analysis across Position, Trends, Comparison, and Levels tabs
- Responsive Design: Mobile-optimized interface with touch-friendly controls
- Real-time Processing: Dynamic data filtering and analysis
- Export Capabilities: PDF report generation for detailed analysis
- Welcome Brewing Sequence: Animated introduction for first-time users with tea preparation stages
- Data Upload Animations: Tea steeping progress indicators during file processing
- AI Analysis Animations: Tea master tasting animations for market intelligence generation
- Visualization Brewing: Tea preparation animations while charts are being generated
- Correlation Blending: Tea blending animations for relationship analysis
- Progress Tracking: Multi-stage tea ceremony progress indicators with elapsed time
- Frontend: Streamlit 1.29.0
- Data Processing: Pandas 2.1.3, NumPy 1.26.2
- Visualization: Plotly 5.18.0, Plotly Express
- AI Integration: OpenAI 1.3.5
- File Processing: openpyxl, xlrd
- Report Generation: ReportLab
- Python: 3.11+
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Clone the repository:
git clone https://github.com/your-username/ctc-tea-sales-analytics.git cd ctc-tea-sales-analytics -
Install dependencies using pip:
pip install .Or install from pyproject.toml directly:
pip install -e . -
Set up environment variables (optional for AI features):
export OPENAI_API_KEY="your-openai-api-key"
Using pip with dependencies list:
pip install streamlit==1.29.0 pandas==2.1.3 plotly==5.18.0 openai==1.3.5 numpy==1.26.2 openpyxl xlrd reportlab plotly-expressFor development:
pip install -e ".[dev]"Local Development:
streamlit run main.py --server.port=5000 --server.address=0.0.0.0On Replit: Click the "Run" button or use the configured workflow.
The application expects Excel or CSV files with the following columns (accepts variations):
| Required Column | Accepted Variations |
|---|---|
| Centre | Centre, Center, Market Center, Location |
| Sale No | Sale No, Sale Number, Sale_No, SaleNo |
| Sales Price(Kg) | Price/Kg, Price (Kg), Sales Price, Price |
| Sold Qty (Ton) | Sold Quantity, Sold_Qty, Quantity Sold |
| Unsold Qty (Ton) | Unsold Quantity, Unsold_Qty, Quantity Unsold |
Markets should follow the format: {Region} CTC {Type}
- Regions: North India, South India
- Types: Leaf, Dust
Example: "North India CTC Leaf", "South India CTC Dust"
- Drag-and-drop file upload
- Intelligent column mapping with fuzzy matching
- Data validation and cleaning
- Support for multiple file formats
- Multi-select filters for regions and tea types
- Dynamic market filtering
- Real-time data updates
- Current market position metrics
- Price, volume, and efficiency analysis
- Historical context and percentile rankings
- Price trend visualization with trend lines
- Market efficiency tracking
- Rolling correlations and moving averages
- Cross-market comparisons
- Correlation heatmaps
- Competitive benchmarking
- Price distribution analysis
- Volume analysis with multiple aggregation methods
- Statistical summaries and insights
- Market Narrative: Comprehensive market analysis
- Price Analysis: Detailed price insights and forecasting
- Market Intelligence: Strategic recommendations
- Tea Messages: 50+ themed messages across 5 categories (upload, processing, ai_analysis, visualization, report)
- Tea Icons: 10 animated tea-related emojis (teapot, cup, leaves, steam, etc.)
- Progress Animations: Spinning teapot, tea leaf progress bars, steaming cup sequences
- Multi-step Progress: Tracks progress across multiple processing stages
- Elapsed Time Display: Shows processing duration for transparency
- Automatic Cleanup: Removes animation placeholders when complete
- Category-based Messages: Different message sets for different operations
- Chart Brewing: 4-stage visualization preparation with progress tracking
- Correlation Blending: Tea blending metaphor for relationship analysis
- Market Intelligence: Sophisticated tea tasting animation for AI analysis
- Traditional Ceremony: Classical tea preparation with reverent styling
- Modern Station: Contemporary brewing with digital aesthetics
- Artisan Crafting: Hand-crafted approach with premium styling
- App Initialization: Welcome brewing sequence on first visit
- Data Upload: Tea steeping animations during file processing
- Sample Data Loading: Quick brewing animation for instant data access
- AI Analysis: Tea master tasting sequence for all AI-powered insights
- Chart Generation: Visualization brewing for market position charts
- Correlation Analysis: Tea blending animation for relationship matrices
- Statistical Processing: Multi-stage progress tracking for complex calculations
- HTML/CSS Animations: Custom styled progress bars with gradient effects
- Dynamic Content: Real-time message updates with contextual tea terminology
- Responsive Design: Animations adapt to mobile and desktop viewports
- Performance Optimized: Lightweight animations that don't impact data processing
- State Management: Proper cleanup prevents UI conflicts and memory leaks
process_excel_data(): File processing and column mappingstandardize_market_category(): Market name standardizationcalculate_weighted_price(): Weighted average price calculationsanalyze_levels(): Market position analysisanalyze_trends(): Trend analysis and patternsanalyze_comparatives(): Cross-market comparisonscalculate_correlations(): Correlation matrix generationgenerate_ai_narrative(): AI-powered market insightsgenerate_price_analysis(): Price analysis with AIgenerate_market_insights(): Strategic market intelligenceOPENAI_API_KEY: Required for AI-powered features- Fork the repository
- Create a feature branch
- Make your changes
- Test thoroughly
- Submit a pull request
- Click the "Deploy" button in Replit
- The app will be available on your Replit domain
- Supports both development and production modes
- Python 3.11+ environment
- All dependencies installed
- Environment variables configured
- Port 5000 accessible
- File Upload Errors: Ensure your file has the required columns with accepted naming variations
- AI Features Not Working: Check that
OPENAI_API_KEYis properly set - Performance Issues: For large d 717D atasets, the app includes optimized batch processing
- Check the troubleshooting section
- Review the console logs for error details
- Ensure all dependencies are properly installed
- Built with Streamlit for the web interface
- Plotly for interactive visualizations
- OpenAI for AI-powered insights
- Pandas and NumPy for data processing
├── main.py # Main Streamlit application
├── utils.py # Data processing and analysis utilities
├── styles.py # Custom CSS styling
├── loading_animations.py # Tea-themed loading animation system
├── assets/ # Sample data and resources
│ └── default_data.csv # Sample tea market dataset
├── pyproject.toml # Project dependencies
├── .replit # Replit configuration
└── README.md # This file
The dashboard features a comprehensive tea-themed loading animation system that enhances user experience during data processing and analysis operations.
The app includes custom styling and mobile optimization. Configuration can be found in styles.py.
This application is optimized for Replit deployment:
For other platforms, ensure:
Run with debug logging:
streamlit run main.py --logger.level=debugThis project is licensed under the MIT License - see the LICENSE file for details.
For issues and questions:
Note: This application is designed for tea market analysis and can be adapted for other commodity markets with similar data structures.