💰 AI Financial Advisor Chatbot

An intelligent financial advisory chatbot powered by LangGraph with dual LLM support (Google Gemini & HuggingFace).

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📦 Table of Contents

• Features
• Screenshots
• Installation
• API Keys Setup
• Usage
• Example Queries
• Configuration
• Project Structure
• Tech Stack
• Troubleshooting
• Known Limitations
• License
• Contact

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🚀 Features

Specialized Financial Services:

• 🏆 Gold Investment Planning - Calculate affordable gold purchases based on your income and expenses
• 💰 Budget Analysis - Analyze your monthly budget using the 50-30-20 rule
• 📊 Investment Advice - Get personalized investment recommendations
• 📈 Financial Health Assessment - Track your savings rate and financial goals
• 🤖 General Financial Q&A - Ask any finance-related questions

Technical Features:

• 🔷 Dual LLM Support - Choose between Google Gemini (fast) or HuggingFace (unlimited)
• ⚡ Dynamic Responses - AI-generated, context-aware advice (not templates)
• 📋 Structured Outputs - Point-wise, easy-to-read financial analysis
• 🔄 Intelligent Routing - Automatically routes queries to specialized agents
• 💾 Session Persistence - Maintains conversation history
• 🌐 100% Free - No credit card required for either LLM provider

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⚠️ Disclaimer

IMPORTANT: This chatbot provides general financial information for educational purposes only.

• ❌ NOT a substitute for professional financial advice
• ❌ NOT certified financial planning
• ❌ NOT responsible for financial decisions made based on this tool

Always consult certified financial advisors before making important financial decisions.

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📷 Screenshots

Home Page

AI Provider Selection

Chatbot Functionality

Gold Investment Analysis

Budget Analysis

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📥 Installation

Prerequisites

• Python 3.10 or higher
• pip package manager
• Internet connection (for cloud LLMs)

Step-by-Step Setup:

# 1. Clone the repository
git clone https://github.com/ShashankGowni/Agentic_ChatBot.git
cd Agentic_ChatBot

# 2. Create virtual environment
python -m venv venv

# 3. Activate virtual environment
# Windows:
venv\Scripts\activate
# Mac/Linux:
source venv/bin/activate

# 4. Install dependencies
pip install -r requirements.txt

🔑 API Keys Setup

You need at least ONE of these API keys (both are 100% FREE):

• Option 1: Google Gemini (Recommended - Faster ⚡)
• Go to Google AI Studio
• Sign in with your Google account
• Click "Create API Key"
• Click "Create API key in new project"
• Copy the key (starts with AIzaSy...)

Free Tier:

• ✅ 60 requests per minute
• ✅ 1,500 requests per day
• ✅ No credit card required

Option 2: HuggingFace (Unlimited Requests 🚀)

Go to HuggingFace

• Sign up (completely free, no credit card)
• Go to Settings → Access Tokens
• Click "New token"
• Select "Read" access
• Copy the token (starts with hf_...)

Free Tier:

• ✅ Unlimited requests
• ✅ No credit card required

Set Up Your Keys:

Create a .env file in the project root directory (same level as app.py):

env

Google Gemini API Key (get from: https://aistudio.google.com/app/apikey)

GEMINI_API_KEY=your_gemini_api_key_here

HuggingFace API Token (get from: https://huggingface.co/settings/tokens)

HUGGINGFACE_API_TOKEN=your_huggingface_token_here

⚠️ Important:

• Never commit the .env file to GitHub (it's already in .gitignore)
• You can use either or both keys

💡 Usage

Run the Application:

streamlit run app.py

In Your Browser:

• Open: http://localhost:8501
• Select AI Provider: Choose between 🔷 Gemini (fast) or 🤗 HuggingFace (unlimited)
• Select Model: Pick from available models in the dropdown
• Enter API Key: Paste your key in the sidebar (or it auto-loads from .env)
• Choose Use Case:
• Financial Advisor
• Gold Investment Advisor
• Budget Analyzer
• Basic Chatbot
• Start Chatting! 💬

💬 Example Queries

Gold Investment Planning:

I want to buy gold. My salary is 60k and expenses are 35k. 
How much can I buy in 4 months?

Expected Response:

🏆 Gold Investment Analysis

💰 Your Financial Summary:
• Monthly Salary: ₹60,000
• Monthly Expenses: ₹35,000
• Monthly Savings: ₹25,000
...

Budget Analysis:

Analyze my budget: income 80k, expenses 60k

Expected Response:

📊 Budget Analysis Report

💵 Current Financial Status:
• Monthly Income: ₹80,000
• Monthly Expenses: ₹60,000
...

⚙️ Configuration

[DEFAULT]
PAGE_TITLE = Financial Advisor ChatBot

LLM_OPTIONS = gemini, huggingface

USECASE_OPTIONS = Financial Advisor, Gold Investment Advisor, Budget Analyzer, Basic Chatbot

# Available Gemini models (FREE)
Gemini_MODEL_OPTIONS = gemini-2.5-flash, gemini-flash-latest, gemini-2.0-flash

# Available HuggingFace models (FREE)
HuggingFace_MODEL_OPTIONS = mistralai/Mistral-7B-Instruct-v0.2, HuggingFaceH4/zephyr-7b-beta

📂 Project Structure:

Agentic_ChatBot/
├── app.py                          
├── requirements.txt                
├── .env                            
├── .env.example                    
├── .gitignore                      
├── README.md                       
├── images/                         
│   ├── HomePage_Screenshot.png
│   ├── ModelSelection_Screenshot.png
│   ├── ChatbotFunctionality_Screenshot.png
│   ├── GoldAnalysis_Screenshot.png
│   └── BudgetAnalysis_Screenshot.png
└── src/
    └── lang_graph_chatbot/
        ├── main.py                 # Core chatbot logic
        ├── LLMS/                   # LLM integrations
        │   ├── gemini_llm.py       # Google Gemini wrapper
        │   ├── huggingface.py      # HuggingFace wrapper
        │   ├── ollama.py           # (Optional) Ollama support
        │   └── groq_llm.py         # (Deprecated)
        ├── nodes/                  # Specialized AI agents
        │   ├── classifier_node.py          # Routes queries
        │   ├── gold_advisor_node.py        # Gold expert
        │   ├── budget_analyzer_node.py     # Budget expert
        │   ├── basic_chatbot_node.py       # General Q&A
        │   └── document_qa_node.py         # Document analysis
        ├── graph/                  # LangGraph workflow
        │   └── graph_builder.py    # Builds agent workflow
        ├── state/                  # State management
        │   └── state.py            # Conversation state
        ├── tools/                  # Financial calculators
        │   ├── gold_advisor.py
        │   ├── budget_calculator.py
        │   └── financial_analyzer.py
        └── ui/                     # Streamlit UI
            ├── uiconfig.ini        # UI configuration
            ├── uiconfigfile.py     # Config parser
            └── streamlitui/
                ├── loadui.py       # UI loader
                └── display_result.py

🛠️ Tech Stack

Frameworks & Libraries:

• Streamlit - Interactive web UI
• LangGraph - Agent orchestration and workflow
• LangChain - LLM integration framework
• Python 3.10+ - Core programming language

LLM Providers (Cloud-Based):

Google Gemini AI 🔷

• Models: gemini-2.5-flash, gemini-2.0-flash
• Speed: ⚡⚡⚡⚡⚡ Very Fast
• Limit: 1,500 requests/day (FREE)

HuggingFace Inference API 🤗

• Models: Mistral-7B-Instruct, Zephyr-7B-beta
• Speed: ⚡⚡⚡ Good
• Limit: Unlimited (FREE)

Key Dependencies:

• streamlit>=1.28.0
• langgraph>=0.0.20
• langchain>=0.1.0
• langchain-core>=0.1.0
• google-generativeai>=0.3.0
• huggingface-hub>=0.19.0
• python-dotenv>=1.0.0

🔧 Troubleshooting

Issue: API Key not working

• Verify key is correct in .env
• Check key has proper permissions

Issue: Rate limit exceeded

• Switch to HuggingFace (unlimited)
• Wait for rate limit reset

⚠️ Known Limitations

• Gemini: 1,500 requests/day limit
• HuggingFace: Slower response times
• Requires internet connection
• Does not provide certified financial advice

📧 Contact

Maintainer: Shashank Gowni

• 📧 Email: shashankgowni09@gmail.com
• 💼 LinkedIn: linkedin.com/in/shashankgowni
• 🐙 GitHub: @ShashankGowni

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📄 License

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

What this means:

• ✅ Free to use for personal and commercial projects
• ✅ Free to modify and distribute
• ✅ Just keep the copyright notice
• ❌ No warranty provided

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Acknowledgments

• Built with LangGraph
• Powered by Google Gemini AI and HuggingFace
• UI built with Streamlit

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🤝 Contributing

Contributions are welcome! To contribute:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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🌟 Show Your Support

If this project helped you, please ⭐ star the repository!

Made with ❤️ by Shashank Gowni