Chatbot LLM: Install Open Source on Windows Easily
In this blog, I'll walk you through building and deploying your personalized chatbot using an open-source language model (LLM) called Open LLaMA.
In this blog, I'll walk you through building and deploying your personalized chatbot using an open-source language model (LLM) called Open LLaMA. We will start by installing everything on a Windows environment and then deploy it with Ngrok for remote access.
To follow along, you can refer to my [GitHub repository](https://github.com/chelvadataer/personalized-bot.git), which contains all the code and resources.
Prerequisites
Before we dive in, ensure you have the following installed:
- Python 3.7 or above
- [Git](https://git-scm.com/download/win)
- [Ngrok](https://ngrok.com/download) (for public URL to local development)
- [Ollama](https://ollama.com) installed
If you don't have these installed, make sure to install them first. Let's proceed with installing the necessary Python libraries and setting up the chatbot.
Step 1: Cloning the GitHub Repository
The first step is to clone the repository that contains our chatbot code:
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Clone the repository
git clone https://github.com/chelvadataer/personalized-bot.git
cd personalized-bot
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This repository contains a script that uses [Streamlit](https://streamlit.io) to create the chatbot's UI and Ollama to generate responses using the LLaMA model.
Step 2: Setting Up Your Environment
We will now set up a Python virtual environment and install the required libraries. Run the following commands in your terminal:
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Create a virtual environment
python -m venv chatbot-env
Activate the virtual environment
For Windows
chatbot-env\Scripts\activate
Install required dependencies
pip install -r requirements.txt
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This will install all the dependencies listed in the `requirements.txt` file, including Streamlit.
Step 3: Running the Chatbot Locally
Now that the environment is set up, we can run our chatbot locally. The script in the repository is written in Python and uses Streamlit to create a simple web interface. To run the chatbot, use the following command:
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streamlit run chatbot.py
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Python Script Overview
Here's an overview of the Python script you can find in the repository:
python Code
The script takes user input, sends it to the Ollama model via subprocess, and then displays the generated response on the web interface.
Step 4: Exposing Your Chatbot to the Internet Using Ngrok
Streamlit runs locally by default, which means it is only accessible from your machine. To make the chatbot accessible remotely, we'll use Ngrok.
Downloading and Configuring Ngrok
If you haven’t already done so, download [Ngrok](https://ngrok.com/download). Once downloaded, follow these steps:
- Start Ngrok to expose the local Streamlit server to the public internet.
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# Start ngrok to create a public URL for your Streamlit app
ngrok http 8501
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- You will see a forwarding address that looks something like `http://abcd1234.ngrok.io`. You can use this URL to access your chatbot from anywhere in the world.
PS C:\Users\Chelvaprabaharan> ngrok http 8501
Step 5: Testing Your Chatbot
Now that Ngrok is running, you can share the forwarding URL with your friends or use it to test the chatbot from a different device. The chatbot UI should be intuitive, with a friendly design and a welcoming greeting message for users.
Adding Images and Branding
For a more professional-looking chatbot, consider adding some images and branding. Replace the placeholder logo.svg with your actual company logo or an image of your choice.
Python
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# Add logo to the app
st.image("logo.svg", width=100)
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You can also modify the chatbot's colors and layout by using the various parameters provided by Streamlit's UI components.
Conclusion
In this blog, we've learned how to set up and run a personalized chatbot using Open LLaMA, with a user-friendly UI built with Streamlit and deployment using Ngrok. This project is great for those who want to build an interactive chatbot and make it accessible from anywhere. Feel free to modify and improve it further!
For the full code, additional images, and examples, visit my [GitHub repository](https://github.com/chelvadataer/personalized-bot.git).
Happy coding!
Next Steps
- Try personalizing the chatbot to respond in different styles.
- Add more features, such as voice input or integrating a knowledge base.
- Deploy it on a cloud server to make it more robust.
What do you think about these additions? Let me know if you need any help with extending the chatbot!
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