Leveraging LLM’S for AI-Driven Demand Prediction

Welcome to the Inventory Management System. This application leverages large language models (LLMs) for AI-driven demand prediction and provides comprehensive tools for data analysis, AI assistance, product analysis, and sentiment analysis.

Features

📊 Data Analysis Dashboard

Access comprehensive inventory analysis tools including:

• Data validation and preview
• Exploratory Data Analysis
• Time Series Analysis
• Demand Forecasting

🤖 AI Assistant

Interact with our AI-powered chatbot to:

• Get demand predictions
• Analyze specific parts
• Understand inventory trends
• Compare forecasting models

📝 Product Analysis

Perform detailed analysis of product reviews to:

• Understand customer feedback
• Identify advantages and disadvantages of products
• Track product performance
• Generate insights

😊 Sentiment Analysis

Measure the sentiment of specific vendors to:

• Understand vendor performance
• Compare sentiment across suppliers
• Track satisfaction trends
• Identify improvement areas

Installation

1. Clone the repository:

   git clone https://github.com/your-repo/inventory-management-system.git
   cd inventory-management-system

2. Create a virtual environment and activate it:

   python -m venv venv
   .\venv\Scripts\activate  # On Windows
   source venv/bin/activate  # On macOS/Linux

3. Install the required packages:

   pip install -r requirements.txt

4. Download the SpaCy model:

   python -m spacy download en_core_web_sm

Usage

1. Run the Streamlit application:

   streamlit run app.py

2. Open your web browser and go to http://localhost:8501 to access the application.

File Structure

• app.py: Main application file that sets up the Streamlit interface and navigation.
• 1_Data_Analysis.py: Data analysis dashboard.
• 2_AI_Assistant.py: AI assistant for demand prediction and analysis.
• 3_Product_Analysis.py: Product analysis based on customer reviews.
• 4_Sentiment_Analysis.py: Sentiment analysis of specific vendors.
• style.py: Common styling for the application.

Contributing

We welcome contributions to improve the Inventory Management System. Please fork the repository and submit a pull request with your changes.

License

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

Acknowledgements

• Streamlit
• SpaCy
• Hugging Face Transformers
• VADER Sentiment Analysis