moc_automation

DataGenie

DataGenie is a powerful web application designed to automate two key processes:

1. Report Generation: Automatically generate PDF reports from CSV files with predefined structures, text summaries, tables, and charts.
2. Interactive Chatbot: Query and interact with personalized data, such as PDFs, using advanced AI features like memory retention and optimized search.

Table of Contents

• Features
• Prerequisites
• Installation
• Configuration
• Usage
  • Report Generator
  • Chatbot
• Customization

Features

Report Generator

• Automated Report Creation: Generate comprehensive reports based on predefined structures.
• Text Summarization: Automatically summarize key topics within your data.
• Visualizations: Integrate tables and charts seamlessly into your reports.
• Editable Outputs: Download and edit reports in Word format before finalizing.

Chatbot

• Data Interaction: Upload reports and interact with them in real time.
• Memory Retention: Maintain the latest three states of interaction for continuity.
• Advanced Search: Leverage RAG query optimizers for enhanced search capabilities.
• User-Friendly Interface: Engage with data through a streamlined and intuitive UI.

Prerequisites

• Python 3.9 (or above)
• Virtual Environment (optional but recommended)
• Azure OpenAI credentials

Installation

To get started with DataGenie, follow the steps below:

1. Clone the Repository:

   git clone <repository-url>

2. Create and Activate Python Virtual Environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venvScriptsactivate`

3. Install Dependencies:

   pip install -r requirements.txt

Configuration

Azure OpenAI Setup

You need to update the .env file under the genai_utilits directory with your Azure OpenAI credentials. You can obtain these credentials from here.

Example configuration for .env:

OPENAI_API_KEY=<your_azure_openai_api_key>
OPENAI_API_TYPE=<your_azure_openai_type e.g azure>
OPENAI_API_VERSION=<your_azure_openai_version>
OPENAI_ENDPOINT=<your_azure_openai_endpoint>

Usage

After completing the configuration, start the Streamlit application:

streamlit run app.py

The Streamlit app will be accessible in your web browser, where you can upload files for report generation and chatbot.

Report Generator

1. Upload CSV File:

   • Upload your export-import data CSV file. A sample file (dec_com_export.csv) is available in the .assets folder.

2. Generate Report:

   • The tool will generate a PDF and Word file stored in the generated_report folder.

3. Predefined Structure of the Report:

   • Overall export trends
   • Top five export commodities
   • Export growth rates
   • Sector-wise analysis
   • Commodities comparison

Here is a demonstration video of DataGenie Report Generator in action:

DataGenie_ReportGenerator.mp4

Chatbot

1. Upload Report:

   • Upload a PDF report (e.g., Introduction To New Gen Technology.pdf located in the .assets folder).

2. Chat with the Data:

   • The process involves fetching data from the PDF, initializing VectorStore, preparing data for embeddings, and starting the chat. The latest three memory states are retained for continuity.

Here is a demonstration video of DataGenie ChatBot in action:

freecompress-DataGenie-ChatBot.mp4

Customization

1. Modify Chat Avatars:

   • To change the avatars in the chatbot, add new images to the .assets directory.

2. UI Customization:

   • UI customization options are available in the config.toml file located in the .streamlit directory. Modify the appearance and settings of the Streamlit app as needed.