Financial Bot

Overview

This project aims to create a financial bot capable of analyzing profit and loss (P&L) statements and other financial documents. The bot allows users to upload PDFs and ask questions related to the contents of the document, providing insights based on the financial data extracted.

Features

• PDF Analysis: The bot extracts and analyzes financial terms and statements from a PDF document.
• User Interaction: Users can ask questions related to the document's financial data, and the bot will respond with relevant information.
• Fast Response: The bot uses the Gemini-1.5-flash model, which caches information and provides faster response times.

Requirements

• Python 3.8 or higher
• Streamlit for web deployment
• GitHub repository access

Installation & Setup

1. Clone the Repository

Clone the repository from GitHub to your local machine:

git clone https://github.com/nj7782/Pl-bot.git

2. Install Dependencies

Navigate to the project directory and install the required libraries:

pip install -r requirements.txt

3. Run the Application Locally

To run the bot locally, use Streamlit to start the web interface:

streamlit run app.py

This will launch the app on your browser.

4. Upload a PDF

• Once the app is running, you'll be prompted to upload a PDF document containing financial data.
• The bot will analyze the document and process the relevant financial terms.

5. Ask Questions

• After uploading the document, you can ask the bot questions related to its content.
• Example questions include:
  • "What is the total revenue?"
  • "What is the net profit?"

6. Review the Results

• The bot will generate and return answers based on the data extracted from the uploaded document.

Model Used

• Gemini-1.5-flash: This model is used for efficient processing. It caches the extracted information and generates output in a minimal time.

Troubleshooting

• No Response: If the bot doesn't respond, ensure that the PDF is correctly uploaded and contains relevant financial data.
• Performance Issues: If the app is slow, try running it on a machine with better resources or reduce the document size.