Building a Chatbot with Ollama, LLaMA 3.2, Python, and Streamlit

In the ever-evolving world of AI and natural language processing (NLP), creating your own chatbot has become more accessible than ever. Thanks to powerful tools like Ollama, open-source language models like LLaMA, and Python libraries such as Streamlit, building a chatbot with a web interface is now within reach for developers of all skill levels. In this blog, I’ll walk you through how I built a chatbot using these technologies.

What is Ollama and LLaMA?

Ollama is a lightweight and efficient framework that allows you to run large language models (LLMs) locally on your machine. It simplifies the process of downloading, managing, and interacting with models like LLaMA.

LLaMA (Large Language Model Meta AI) is a family of state-of-the-art language models developed by Meta. LLaMA 3.2 is one of the latest iterations, offering impressive capabilities in understanding and generating human-like text. By leveraging Ollama, you can easily pull and run LLaMA models on your local machine.

Why Streamlit?

Streamlit is a fantastic Python library that enables you to create web applications with minimal effort. It’s perfect for building interactive dashboards, data visualizations, and, in this case, a chatbot interface. With just a few lines of code, you can create a sleek and responsive web app.

Steps to Build the Chatbot

Here’s a high-level overview of the steps I followed to create the chatbot:

Visit the official Ollama website: ollama.com and Click on the download button corresponding to your operating system.
Pull the LLaMA 3.2 Model
After installing Ollama on my PC, I pulled the LLaMA 3.2 model using the following command:
```
 ollama pull llama3.2
```
This downloaded the model and made it ready for local use.
Set Up the Python Environment
I created a Python virtual environment and installed the necessary dependencies:
```
 pip install ollama streamlit
```

Write the Chatbot Logic
Using Python, I wrote a script to interact with the LLaMA model via Ollama. Here’s a simplified version of the code:

 import streamlit as st
 import ollama

 # Function to generate a response from the Llama 3.2 model
 def generate(prompt):
     response = ollama.chat(model='llama3.2', messages=[
                             {
                                 'role': 'user',   # Setting the role of the message
                                 'content': prompt, # User's input prompt
                             }
     ])
     return response['message']['content']  # Extracting the generated response content

 # Streamlit app title
 st.title("Chat bot")
 st.write("Ask your question, and I'll have your answer ready in no time.")

 # Text area for user input
 user_prompt = st.text_area("Enter your question:")

 # When the "Generate Response" button is clicked
 if st.button("Click to get the answer"):
     if user_prompt.strip() != "":  # Check if the prompt is not empty
         with st.spinner("Response in progress..."):  # Show a spinner while generating the response
             try:
                 response = generate(user_prompt)  # Generate the response
                 st.success("Response completed!")  # Show success message
                 st.text_area("Final Response:", value=response, height=200)  # Display the response in a text area
             except Exception as e:
                 st.error(f"Error: {str(e)}")  # Show error message if there's an exception
     else:
         st.warning("Enter a question.")  # Show a warning if the prompt is empty

Run the Streamlit App
To launch the chatbot interface, I ran the following command:
```
 streamlit run llama_chatbot.py
```
This opened a web interface in my browser where I could interact with the chatbot.

Key Features of the Chatbot

Local Execution: Since Ollama runs the LLaMA model locally, there’s no need to rely on external APIs, ensuring privacy and reducing latency.
Interactive Web Interface: Streamlit provides a clean and intuitive interface for users to interact with the chatbot.
Customizable: The chatbot can be easily extended to include additional features, such as context management, multi-turn conversations, or integration with other tools.

Challenges and Learnings

While the process was relatively straightforward, I encountered a few challenges:

Hardware Requirements: Running large models like LLaMA 3.2 locally requires a decent amount of RAM and GPU resources. If your machine struggles, consider using a smaller model or optimizing the setup.

Git hub:

https://github.com/vipinputhanveetil/ollama-llama-local-chatbot

Conclusion

Building a chatbot with Ollama, LLaMA 3.2, Python, and Streamlit was a rewarding experience. It demonstrated how accessible AI technologies have become, enabling developers to create powerful applications with minimal effort.