Let's build a chatbot with just Python using the Streamlit library, Ollama, and Microsoft Phi-3.
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.
allows you to run open-source large language models, locally You can find more info in the official Ollama docs.
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.
If you can't use pip then use conda instead to install the library. Ensure that you have install Ollama platform in to your computer, by visiting this link: Ollama.com
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
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.
1 - Clone the project from Github
git clone https://github.com/chrnthnkmutt/phi3_experiment.git
2 - run the application
streamlit run app.py
code along 1 - Create your app.py file
app.py
2 - Add imports
import streamlit as st
import ollama3 - 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 token6 - 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
Visit the file name phi3-vis-ocr.ipynb and phi3-vis-gen.ipynb for execution the file for testing multimodal performance of Phi-3 Vision. Recommended to run on Google Collaboratory