ChatterFlow: An AI Chatbot with Streamlit

Test the bot here: https://ruvenguna94-chatterflow-appchatterflow-id7wbv.streamlit.app/

Introduction

ChatterFlow is an intelligent chatbot powered by OpenAI and Streamlit, designed to provide an interactive and user-friendly experience. This chatbot leverages natural language processing (NLP) to generate human-like responses and can be easily customized for various use cases.

In this notebook, we will guide you through building a chatbot interface using Streamlit, allowing users to interact with the AI model seamlessly.

Key Features:

✅ Conversational AI: Interact with an AI-powered chatbot for seamless Q&A.
✅ Conversation History: Stores past interactions and allows users to delete them when needed.
✅ User-Friendly Interface: Built using Streamlit for an intuitive web experience.
✅ Ongoing Enhancements: Future versions will include improvements in performance, memory handling, and response accuracy.

Why Streamlit?

Streamlit is an open-source Python library that simplifies the creation of interactive web applications for machine learning and data science projects. It is widely used due to its ease of use and minimal coding requirements.

Key Features of Streamlit:

• Quick Prototyping: Build interactive UIs with just a few lines of Python code.
• Real-time Updates: Automatically updates components as users interact with the app.
• Simple Deployment: Easily share and deploy applications with Streamlit Community Cloud or other hosting platforms.
• Support for Machine Learning Models: Ideal for showcasing AI and data science projects in a web-based format.

With Streamlit, you can create a chatbot UI that runs directly in a web browser, eliminating the need for complex frontend development.

Other Ways to Build a Chatbot UI

While Streamlit is a great choice, there are other ways to build a chatbot interface depending on your needs:

1. Gradio – A simple and lightweight UI framework designed for AI applications.
2. Flask/Django with HTML & JavaScript – More customizable but requires backend and frontend development.
3. React with FastAPI – A modern, scalable approach for high-performance applications.
4. Dash (Plotly) – Best suited for data visualization and analytics-driven chatbots.
5. Tkinter/PyQt – Desktop-based GUI applications instead of web-based interfaces.

Each of these options comes with trade-offs in terms of complexity, performance, and ease of deployment. Streamlit, however, remains one of the fastest ways to get a chatbot up and running with minimal effort.

This chatbot is a work in progress, and I will be continuously improving its features. Some planned enhancements include:
🔹 Integration with web search capability
🔹 Expanded capabilities with LangChain 🔹 Leverage open source LLMs instead of OpenAI 🔹 Image generation 🔹 Document summarizer 🔹 Many more!

Getting Started

In the next sections, we will:
✅ Install necessary dependencies
✅ Set up Streamlit
✅ Integrate OpenAI’s API
✅ Build an interactive chatbot interface with conversation history management

Follow these steps to set up and run the chatbot:

Installation

Clone this repository and navigate into the project directory:

git clone https://github.com/ruvenguna94/ChatterFlow.git
cd ChatterFlow

Install the required dependencies:

pip install -r requirements.txt

Running the Chatbot

Run the Streamlit application:

streamlit run app.py

This will open a web-based interface where you can interact with the chatbot.

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Contributing

Contributions are welcome! If you have any suggestions or improvements, feel free to open an issue or submit a pull request.

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License

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