https://github.com/bushra-butt-17/budgetbuddy-finance-chatbot

Budget Buddy is a finance chatbot built using Chainlit and the LLaMA language model. It analyzes PDF documents, such as bank statements and budget reports, to provide personalized financial advice and insights. The chatbot is integrated with Hugging Face for model management, offering an interactive way to manage personal finances.
https://github.com/bushra-butt-17/budgetbuddy-finance-chatbot

accelerate bitsandbytes chainlit chatbot ctransformers embeddings faiss-cpu faiss-vector-database huggingface-hub huggingface-transformers langchain langchain-community llama mini-llm pdf-parse pypdf sentence-transformers torch vector-store

Last synced: 24 days ago
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Budget Buddy is a finance chatbot built using Chainlit and the LLaMA language model. It analyzes PDF documents, such as bank statements and budget reports, to provide personalized financial advice and insights. The chatbot is integrated with Hugging Face for model management, offering an interactive way to manage personal finances.

• Host: GitHub
• URL: https://github.com/bushra-butt-17/budgetbuddy-finance-chatbot
• Owner: Bushra-Butt-17
• Created: 2024-11-25T14:41:55.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-12-10T14:31:38.000Z (4 months ago)
• Last Synced: 2025-02-05T15:14:21.736Z (3 months ago)
• Topics: accelerate, bitsandbytes, chainlit, chatbot, ctransformers, embeddings, faiss-cpu, faiss-vector-database, huggingface-hub, huggingface-transformers, langchain, langchain-community, llama, mini-llm, pdf-parse, pypdf, sentence-transformers, torch, vector-store
• Language: Python
• Homepage:
• Size: 19.3 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        


---

# Budget Buddy - Your Personal Finance Chatbot 💸🤖

**Budget Buddy** is a finance-focused chatbot designed to help you manage your personal finances with ease. It uses advanced natural language processing (NLP) technologies, including **Chainlit**, **LLaMA**, and **Hugging Face Sentence Transformers**, to analyze your financial documents, answer budget-related questions, and provide actionable insights.

---

## 📌 **What is a Chatbot?**

A **chatbot** is a software application designed to simulate human-like conversations using text or voice. Chatbots use NLP to understand and respond to user queries intelligently. Budget Buddy acts as a financial assistant, leveraging machine learning and NLP to interpret your uploaded financial data and provide meaningful advice.

---

## 📌 **How Budget Buddy Works**

Budget Buddy processes your uploaded financial documents (e.g., bank statements, budget reports) and provides insights by:  

1. **Embedding Generation:** Converts the content of your PDFs into meaningful numerical representations (embeddings) using **Hugging Face Sentence Transformers**.  

2. **Financial Data Storage:** Stores these embeddings in a **vector database** for quick retrieval and analysis.  

3. **Interactive Conversations:** Answers user questions using the generated embeddings and the **LLaMA language model** for natural and context-aware responses.  

---

## 🔍 **What are Sentence Transformers?**

**Sentence Transformers** are deep learning models designed to convert text into dense numerical vectors (embeddings). These vectors capture the semantic meaning of sentences, enabling advanced tasks like:

- Information retrieval.

- Question answering.

- Semantic similarity comparison.

Budget Buddy uses the `sentence-transformers/all-MiniLM-L6-v2` model, a lightweight and efficient transformer-based model, ideal for creating embeddings for financial data.

### **How It Works in Budget Buddy**:

1. Text is extracted from uploaded PDFs.

2. The extracted text is passed through the Sentence Transformer model to generate embeddings.

3. These embeddings are stored in a **vectorstore** for efficient and quick query matching.

---

## 🔗 **Role of Hugging Face**

**Hugging Face** is a leading platform for state-of-the-art NLP models and tools. In Budget Buddy, it plays a critical role by providing:

1. **Pretrained Models:** The `sentence-transformers/all-MiniLM-L6-v2` model is accessed via Hugging Face for creating embeddings.  

2. **Ease of Integration:** Hugging Face tools simplify integrating advanced NLP capabilities into the chatbot.  

3. **Customizability:** Allows fine-tuning and adapting pretrained models for specific financial use cases.

---

## 🚀 **Key Features**

### 1. **Upload Financial PDFs** 📄  

- Upload documents like bank statements, expense reports, and budgets.  

### 2. **PDF Analysis** 📊  

- Extract structured information, such as income, expenses, and transaction summaries.  

### 3. **Interactive Conversations** 💬  

- Ask questions like:  

  - *"What’s my total spending for October?"*  

  - *"How much did I save last month?"*  

  - *"What’s the largest expense in my uploaded PDF?"*  

### 4. **Personalized Financial Advice** 💡  

- Get tailored insights and suggestions for improving savings, managing budgets, and optimizing expenses.  

### 5. **Fast and Accurate Responses** ⚡  

- Leverages vector embeddings for efficient and context-aware query handling.

---

## 🛠️ **Project Structure**

Here’s how the project is organized:  

```

- .chainlit/            # Chainlit-specific configuration and settings

- .files/               # Temporary or supporting file storage

- data/                 # 📂 Upload your financial PDFs here!

- vectorstore/          # 🧠 Vector database storing financial embeddings

- venv/                 # 🛡️ Virtual environment for dependencies

- chainlit/             # Chainlit app configuration and scripts

- ingest/               # 📜 Scripts for processing PDFs into embeddings

- model/                # 🤖 Machine learning models (LLaMA & Hugging Face)

- README.md             # 📖 Project documentation

- requirements.txt      # 📋 Project dependencies

```

> **Note**: There are currently no test files in the project. Testing will involve manual verification of the chatbot’s responses using different financial PDFs.

---

## 📖 **How to Get Started**

### 1. **Clone the Repository** 🛠️

```bash

git clone https://github.com/Bushra-Butt-17/BudgetBuddy-Finance-Chatbot.git

cd BudgetBuddy

```

### 2. **Set Up the Virtual Environment** 🐍

```bash

python -m venv venv

source venv/bin/activate   # For Linux/MacOS

venv\Scripts\activate      # For Windows

```

### 3. **Install Dependencies** 📦

```bash

pip install -r requirements.txt

```

---

## 🤖 **How to Use Budget Buddy**

### Step 1: Upload Financial PDFs  

   - Save your PDF files (e.g., bank statements, budget reports) in the `data/` directory.  

### Step 2: Process PDFs 📜 ➡️ 🧠  

   - Run the `ingest_data.py` script to generate embeddings from the PDFs:  

     ```bash

     python ingest.py

     ```

   - Embeddings are stored in the `vectorstore/` directory for quick access.  

### Step 3: Run the Chatbot 💬  

   - Launch the chatbot using Chainlit:  

     ```bash

     chainlit run

     ```

   - Open your browser to interact with Budget Buddy, upload financial PDFs, and ask questions.

---

## 🌟 **Behind the Scenes**

### **Embedding Generation**  

The chatbot uses **Hugging Face Embeddings** to generate dense numerical representations (embeddings) of financial PDFs. These embeddings allow the chatbot to:  

- Extract and summarize critical information.  

- Retrieve relevant details when answering user questions.  

- Offer precise and personalized financial insights.

Here’s how embeddings are created in Budget Buddy:

```python

from langchain.embeddings import HuggingFaceEmbeddings

# Create embeddings for financial PDFs

embeddings = HuggingFaceEmbeddings(

    model_name='sentence-transformers/all-MiniLM-L6-v2',

    model_kwargs={'device': 'cpu'}  # Optimized for devices without GPU

)

```

### **Vectorstore**  

The embeddings are stored in a vector database (`vectorstore/`), enabling efficient similarity searches and instant retrieval of relevant data.

---

## 🚀 **Future Plans**

- **Automated Testing Framework:** Add unit and integration tests for validating chatbot responses.  

- **Data Visualization:** Visualize financial trends and summaries (e.g., income vs. expenses).  

- **Support for More File Formats:** Expand support to Excel and CSV files.  

- **Advanced NLP Models:** Fine-tune LLaMA for financial-specific tasks.  

---

## 🙌 **Contributing**

We welcome contributions to make Budget Buddy better! Feel free to fork the repository, make changes, and submit a pull request.  

---

## 📧 **Contact**

For questions, suggestions, or feedback, contact: **[email protected]**  

---

**Happy Budgeting!** 💸🎉

 ---

## **Tips for Best Results** 🎯

1. **Upload PDFs** to the `data/` folder for consistent processing.  

2. Always **run the `ingest_data.py` script** after adding new PDFs to update embeddings.  

3. Use a **virtual environment** to manage dependencies and prevent conflicts.  

---

## **Future Enhancements** 🔮

- 📈 Add support for dynamic dashboards.  

- 📥 Real-time PDF uploads directly via the chatbot.  

- 🌍 Integration of external APIs for currency conversion and stock market data.  

---

## **License** 📄

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.  

---

Get started today and take control of your finances with Budget Buddy! 💸