Phi3_experiment

Intelligent Chatbot with Phi-3 SLM and Streamlit Library

Let's build a chatbot with just Python using the Streamlit library, Ollama, and Microsoft Phi-3.

Streamlit:

turns data scripts into shareable web apps in minutes. All in pure Python. No front‑end experience required. You can find more info in the official Streamlit docs.

Ollama:

allows you to run open-source large language models, locally You can find more info in the official Ollama docs.

Phi-3 Mini:

is a 3.8B parameters, lightweight, state-of-the-art open model by Microsoft. You can find more info in the official Phi-3 Mini docs.

Steps

If you can't use pip then use conda instead to install the library

1 - Create a new conda environment

conda create --name envStreamPhi

2 - Activate the environment

conda activate envStreamPhi

3 - Clone StreamLit template

git clone https://github.com/streamlit/streamlit.git
conda install streamlit

4 - Install ollama & pull the phi-3 model

pip install ollama
ollama pull phi3

5 - Pull the Embeddings model:

ollama pull nomic-embed-text

6 - Test installation

streamlit hello

Build the AI assistant

In order to build the AI assistant, you have 2 choices : clone the repo and get all the code from the get-go or coding along with me.

I - First option :

1 - Clone the project from Github

git clone https://github.com/chrnthnkmutt/phi3_experiment.git

2 - run the application

streamlit run app.py

II - Second option:

code along 1 - Create your app.py file

app.py

2 - Add imports

import streamlit as st
import ollama

3 - Add the defacto message

if "messages" not in st.session_state:
    st.session_state["messages"] = [{"role": "assistant", "content": "Hello tehre, how can I help you, today?"}]

4 - Add the message history

for msg in st.session_state.messages:
    if msg["role"] == "user":
        st.chat_message(msg["role"], avatar="🧑‍💻").write(msg["content"])
    else:
        st.chat_message(msg["role"], avatar="🤖").write(msg["content"])

5 - Configure model

def generate_response():
    response = ollama.chat(model='phi3', stream=True, messages=st.session_state.messages)
    for partial_resp in response:
        token = partial_resp["message"]["content"]
        st.session_state["full_message"] += token
        yield token

6 - Configure the prompt

if prompt := st.chat_input():
    st.session_state.messages.append({"role": "user", "content": prompt})
    st.chat_message("user", avatar="🧑‍💻").write(prompt)
    st.session_state["full_message"] = ""
    st.chat_message("assistant", avatar="🤖").write_stream(generate_response)
    st.session_state.messages.append({"role": "assistant", "content": st.session_state["full_message"]})   

7 - all the codebase of app.py

import streamlit as st
import ollama

st.title("💬 Phi3 Chatbot")

if "messages" not in st.session_state:
    st.session_state["messages"] = [{"role": "assistant", "content": "Hello tehre, how can I help you, today?"}]

### Write Message History
for msg in st.session_state.messages:
    if msg["role"] == "user":
        st.chat_message(msg["role"], avatar="🧑‍💻").write(msg["content"])
    else:
        st.chat_message(msg["role"], avatar="🤖").write(msg["content"])

## Configure the model
def generate_response():
    response = ollama.chat(model='phi3', stream=True, messages=st.session_state.messages)
    for partial_resp in response:
        token = partial_resp["message"]["content"]
        st.session_state["full_message"] += token
        yield token

if prompt := st.chat_input():
    st.session_state.messages.append({"role": "user", "content": prompt})
    st.chat_message("user", avatar="🧑‍💻").write(prompt)
    st.session_state["full_message"] = ""
    st.chat_message("assistant", avatar="🤖").write_stream(generate_response)
    st.session_state.messages.append({"role": "assistant", "content": st.session_state["full_message"]})   

Run the Streamlit app

streamlit run app.py