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https://github.com/markphamm/alcon-financial-assistant

Alcon Financial Analysis: An interactive dashboard showcasing dynamic financial metrics, KPIs, and competitor comparisons. Powered by a RAG-based chatbot for real-time insights from financial reports and industry data, enabling data-driven decisions and strategic market positioning for Alcon.
https://github.com/markphamm/alcon-financial-assistant

financial pygwalker rag streamlit

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Alcon Financial Analysis: An interactive dashboard showcasing dynamic financial metrics, KPIs, and competitor comparisons. Powered by a RAG-based chatbot for real-time insights from financial reports and industry data, enabling data-driven decisions and strategic market positioning for Alcon.

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README 

        
# Alcon Financial Competitors Analysis App

![Untitled](https://github.com/user-attachments/assets/3fb5c7d4-ce5b-46aa-8d89-28afb390555a)

Access our Streamlit app [here](https://alcon-financial-assistant.streamlit.app/)



## Overview

This Streamlit application provides a comprehensive financial analysis tool for Alcon and its competitors. It offers various features including financial data visualization, a chatbot for queries, and configuration management.



## Features

1. **Financial Data Visualization**

   - Income Statement Analysis (Annual and Quarterly)

     ![image](https://github.com/user-attachments/assets/01971893-86b8-4061-9138-8f57518d8979)

   - Balance Sheet Analysis (Annual and Quarterly)

     ![image](https://github.com/user-attachments/assets/8e0d49d9-c5e9-486f-98ef-b8375d0dcdc1)

   - Cash Flow Analysis (Annual and Quarterly)

     ![image](https://github.com/user-attachments/assets/32b3d48c-aa1c-41ff-a063-222ae9f0da2f)

   - Interactive charts using Plotly and Pygwalker

     ![image](https://github.com/user-attachments/assets/f183164c-2b8d-44ea-9f2f-cfb909867f92)



2. **RAG-powered Chatbot**

   ![image](https://github.com/user-attachments/assets/a8cd78bf-dd55-4986-bad7-a3fda456ecd0)

   - AI-powered assistant for financial queries

   - Utilizes OpenAI's GPT model and Chroma vector database for retrieval-augmented generation

   - Provides context-aware responses based on up-to-date financial data

  

   - **RAG Architecture**

   ![image](https://github.com/user-attachments/assets/82fe2c8f-ee92-4b63-81b4-f3f185d97d88)



4. **Configuration Management**

   - Add or remove ticker symbols for analysis



   

     ![image](https://github.com/user-attachments/assets/b53d0722-e8f3-4690-b82a-121414015fd3)

   - Run ETL (Extract, Transform, Load) pipeline

  

     

     ![image](https://github.com/user-attachments/assets/ea618c7b-c98a-4dde-bf20-2098ded081e6)

   - Populate Vector Database for enhanced chatbot performance

  

     

     ![image](https://github.com/user-attachments/assets/558fb045-e678-4734-997c-94ab353d2282)



## Installation



1. Clone the repository:

   ```

   git clone [repository-url]

   ```



2. Navigate to the project directory:

   ```

   cd [project-directory]

   ```



3. Create a virtual environment:

   ```

   python -m venv .venv

   ```



4. Activate the virtual environment:

   - On Windows:

     ```

     .venv\Scripts\activate

     ```

   - On macOS and Linux:

     ```

     source .venv/bin/activate

     ```



5. Install required dependencies:

   ```

   pip install -r requirements.txt

   ```



6. Set up environment variables:

   - Create a `.env` file in the root directory

   - Add your OpenAI API key:

     ```

     OPENAI_API_KEY=your_api_key_here

     ```



## Usage



1. Run the Streamlit app:

   ```

   streamlit run app.py

   ```



2. Navigate through different sections using the sidebar:

   - Income Statement

   - Balance Sheet

   - Cash Flow

   - Chatbot

   - Configs



3. In the visualization sections, you can:

   - Select specific companies for comparison

   - Choose between annual and quarterly data

   - Create custom charts using Plotly or Pygwalker



4. Use the Chatbot for financial queries related to the loaded data



5. In the Configs section, you can:

   - Add or remove ticker symbols

   - Run the ETL pipeline to update data

   - Populate the vector database for improved chatbot responses



## Data Sources



The application uses financial data stored in CSV files:

- `annually_income_statement.csv`

- `annually_balance_sheet.csv`

- `annually_cash_flow.csv`

- `quarterly_income_statement.csv`

- `quarterly_balance_sheet.csv`

- `quarterly_cash_flow.csv`



## Dependencies



- Streamlit

- OpenAI

- LangChain

- Pandas

- Plotly

- Pygwalker

- Chroma



## Customization



You can modify the `config.py` file to change the list of tracked tickers and update the color theme for visualizations.



## Note



Ensure that you have the necessary permissions and comply with the terms of service for all data sources and APIs used in this application.