StockVision AI - Agentic Retail Optimization Platform

StockVision AI is an AI-powered agentic platform designed to optimize retail operations, including demand forecasting, inventory monitoring, and pricing strategies. Using a multi-agent architecture and powered by Ollama's LLaMA 3.2 model, StockVision AI enables retailers to make data-driven decisions by forecasting demand, adjusting stock levels, and optimizing prices based on historical trends and competition.

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Features

• 🧠 Multi-Agent System:

  • Demand Forecasting Agent: Predicts future product demand using historical sales data.
  • Inventory Monitoring Agent: Tracks stock levels, detects low stock or overstock situations, and suggests reorder actions.
  • Pricing Optimization Agent: Adjusts product pricing based on competitor analysis, elasticity, and sales volume.

• 📊 Real-Time Dashboards:

  • Visualize demand forecasts, stock levels, and pricing optimization strategies through interactive charts.

• 🧠 LLM-Powered Reasoning:

  • Uses locally hosted LLaMA 3.2 via Ollama for intelligent, real-time decision-making.

• 🔄 Custom Dataset Upload:

  • Upload CSVs for demand, inventory, and pricing data to tailor the predictions and optimizations.

• 📈 Interactive Chat Interface:

  • Chat with the system to ask about stock levels, forecasted demand, and optimized pricing.

• 🛠 Responsible AI:

  • Transparent decision-making, with clear reasoning paths and human-in-the-loop control.

• 🧠 Flexible Integration:

  • Compatible with external data sources or APIs for seamless integration into existing retail systems.

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Prerequisites

• Python: Version 3.10 or 3.11 (recommended for compatibility).
• Ollama: A local instance running the LLaMA 3.2 model for natural language processing.
• Dependencies: Listed in the Installation section.
• Optional: External data integration for demand, inventory, and pricing (or use sample datasets).

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Components

Data Sources

demand_forecasting.csv: Historical sales data for demand prediction inventory_monitoring.csv: Current inventory levels and historical stock movements pricing_optimization.csv: Price points, competitor data, and sales performance metrics

Processing Modules

forecast_updated2.py: Implements time series forecasting models inventory_updated2.py: Handles inventory analytics and optimization pricing_updated2.py: Contains pricing strategy algorithms retail_dashboard_m3.py: Main user interface integrating all components

Requirements

Core app framework

streamlit==1.32.2

Data handling

pandas==2.2.2 numpy==1.26.4

Visualization

altair==5.2.0

Date and time utilities

pytz==2024.1

System interaction

No need to install subprocess, os, re, json, datetime, time, base64, warnings — these are part of Python's standard library

Optional: for CSV/string file handling (used via StringIO)

No external dependency needed for io.StringIO

Performance and UI enhancements

streamlit-extras==0.3.5

Environment variables and config (if applicable)

python-dotenv==1.0.1

HTTP/LLM model communication (Ollama integration via REST)

requests==2.31.0

Caching

streamlit-caching==0.2.0

Installation

Clone the Repository:

git clone https://github.com/SnehaDeepikaP/StockVisionAI.git
cd StockVisionAI

Install dependencies:

pip install -r requirements.txt

Run the dashboard:

streamlit run retail_dashboard_m3.py

Testing

Test predefined queries (e.g., “Show me upcoming stockouts” or “What is the forecast for next month?”).

Upload sample datasets for demand, inventory, and pricing to see how the agents react.

Use the interactive chat to simulate real-time queries and check for accurate responses.

Test the integration with external APIs or data sources.

Project StructureStockVisionAI/

│
├── Datasets/
│   ├── demand_forecasting.csv      # Historical sales data
│   ├── inventory_monitoring.csv    # Inventory levels and movements
│   └── pricing_optimization.csv    # Price points and performance metrics
│
├── forecast_updated2.py            # Demand forecasting module
├── inventory_updated2.py           # Inventory management module
├── pricing_updated2.py             # Pricing strategy algorithms
├── retail_dashboard_m3.py          # Main Streamlit dashboard
├── LICENSE                         # License information
└── README.md                       # Project documentation

Contributing

Contributions are welcome! Please follow these steps:

1.Fork the repository.

2.Create a new branch (git checkout -b feature/your-feature).

3.Make your changes and commit (git commit -m "Add your feature").

4.Push to the branch (git push origin feature/your-feature).

5.Open a pull request.

6.Please ensure your code follows PEP 8 style guidelines and includes appropriate tests.

License

This project is licensed under the MIT License. See the LICENSE file for details.