gemini-chatbot-streamlit

Gemini Chatbot with Streamlit 🤖

Hey👋 welcome to my conversational AI chatbot using Python, Streamlit, and Google's Gemini API. What started as a simple chat interface evolved into something I'm pretty proud of - a clean, modular application that actually remembers what you talked about earlier in the conversation.

I spent a lot of time thinking about code organization here. Instead of cramming everything into one massive file, I broke it down into focused modules that each handle their own thing. It makes the code so much easier to understand and work with.

🚀 Try It Live!

🌐 Live Demo - Try the Chatbot Now!

Click the link above to interact with the chatbot directly in your browser - no setup required!

What This Thing Does

• Clean Chat Interface: Built with Streamlit, so it looks modern and works great
• Smart Conversations: The bot actually remembers what you said 10 messages ago
• Easy Setup: Just drop your API key in a .env file and you're good to go
• Flexible Models: Want to try different Gemini models? Just change one line
• Error Handling: When things go wrong, you get helpful messages instead of crashes
• Session Management: Your chat history sticks around until you decide to clear it

🏗️ How I Organized Everything

The File Structure

I wanted each file to have one job and do it well:

• main.py - The conductor. It orchestrates everything but doesn't do the heavy lifting
• config.py - Handles all the environment setup and API configuration
• chatbot.py - The brain. Pure conversation logic with no UI clutter
• ui.py - Everything visual. All the Streamlit magic happens here

Why I Built It This Way

• Single Responsibility - Each module has one clear job
• Clean Dependencies - No unnecessary imports cluttering things up
• Easy Testing - I can test each piece independently
• Room to Grow - Adding new features won't break existing code

🛠️ What You'll Need

• Python 3.8+ - The foundation
• Streamlit - For the web interface
• Google Generative AI - Powers the chatbot
• python-dotenv - Manages environment variables

🚀 Getting Started

Option 1: Try the Live Demo (Easiest)

Just click the Live Demo Link above - no installation needed!

Option 2: Run Locally

1. Prerequisites

You'll need Python 3.8 or higher and a Google API key for Gemini. You can get one from Google AI Studio - it's free to start with.

2. Installation

# Install the required packages
pip install -r requirements.txt

3. Environment Setup

Create a .env file in the project folder:

GOOGLE_API_KEY=your_google_api_key_here
GEMINI_MODEL=gemini-2.0-flash

Model Options:

• gemini-2.0-flash (newest, what I recommend)
• gemini-1.5-pro (more capable, bit slower)
• gemini-1.5-flash (fast and efficient)

4. Fire It Up

streamlit run main.py

Your browser should open to http://localhost:8501 and you're ready to chat!

📁 Project Structure

gemini-chatbot-streamlit/
├── main.py              # 🎯 Application entry point
├── config.py            # ⚙️ Environment & API configuration
├── chatbot.py           # 🤖 Chat logic & conversation memory
├── ui.py                # 🎨 User interface components
├── requirements.txt     # 📦 Python dependencies
├── README.md            # 📖 Project documentation
└── .env                 # 🔐 Environment variables (create this)

Module Responsibilities

Module	Purpose	Dependencies
main.py	Entry point, orchestrates app flow	config, chatbot, ui
config.py	Environment setup, API configuration	os, dotenv, google.generativeai
chatbot.py	Chat logic, conversation memory	None (pure Python logic)
ui.py	Streamlit interface components	streamlit

🎯 Usage Guide

Starting the Application

1. Run streamlit run main.py (or try the live demo)
2. The app automatically:
   • Loads your .env configuration
   • Initializes the Gemini AI model
   • Sets up the chat interface

Chatting with the Bot

1. Type your message in the input field at the bottom
2. Press Enter to send your message
3. AI responds with context from your conversation history
4. Continue chatting - the bot remembers your entire conversation

Managing Conversations

• Clear Chat: Click the 🗑️ button to start a new conversation
• Session Persistence: Your chat history persists until you refresh or clear
• Error Recovery: Any errors are displayed with helpful messages

📦 Dependencies

Here's what gets installed when you run pip install -r requirements.txt:

• python-dotenv - Loads environment variables from .env files
• google-generativeai - Google's official Gemini API client
• streamlit - Web framework for building the chat interface

⚙️ Configuration Options

Switching Models

Want to try a different Gemini model? Just update your .env file:

GEMINI_MODEL=gemini-1.5-pro  # Switch to the Pro model

API Key Management

• Keep your API key in the .env file (never commit this to git!)
• Make sure your key has Gemini API access enabled
• Check your quota if you're hitting rate limits

Memory Management

The chatbot keeps track of your conversation by:

• Remembering the last 10 messages (5 back-and-forth exchanges)
• Building context for each response to maintain conversation flow
• Automatically handling long conversations by keeping the most recent stuff

🐛 When Things Go Wrong

"Error loading environment"

# Double-check your .env file has:
GOOGLE_API_KEY=your_actual_key_here
GEMINI_MODEL=gemini-2.0-flash

"Error setting up AI model"

• Make sure your Google API key is valid
• Check that Gemini API is enabled in your Google Cloud project
• Verify you haven't hit your API quota

"Module not found errors"

# Install everything fresh
pip install -r requirements.txt

"Streamlit won't start"

# Check if Streamlit is installed properly
streamlit --version
# Reinstall if needed
pip install streamlit --upgrade

"No response from AI"

• Check your internet connection
• Verify API key permissions
• Try switching to a different model in your .env file

🧪 Development Notes

Why This Structure Works

• Easy Testing: Each module can be tested independently
• Clean Development: Work on UI, logic, or config separately
• Team Collaboration: Multiple people can work on different modules
• Future Extensions: Easy to add new features without breaking existing code

Extending the Application

• Add new UI components: Modify ui.py
• Enhance chat logic: Update chatbot.py
• Add new configuration options: Extend config.py
• Change application flow: Modify main.py

📚 References & Resources

• Streamlit Documentation
• Google Generative AI (Gemini) API
• python-dotenv Documentation
• Google AI Studio (API Key)

🙏 Acknowledgements

This program was created for educational purposes as part of the Computer Information and Decision Management Business Intelligence and Decision Support Systems course at West Texas A&M University, under the guidance of Dr. Cheng (Carl) Zhang.

Also special thanks to the open-source community and the creators of Streamlit, Google Generative AI, and the Python ecosystem!

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📝 License

This project is for educational and demonstration purposes, showcasing best practices in Python application development and AI integration.