Let's build a chatbot with just Python using the Streamlit library, Ollama, and Microsoft Phi-3. Streamlit 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 Ollama allows you to run open-source large language models, locally. You can find more info in the official Ollama docs. Phi-3 Mini 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 Create a new conda environment conda create --name envStreamPhi Activate the environment conda activate envStreamPhi Install ollama Download Ollama from https://ollama.com/download pull the phi-3 model The model we will be using is located here: ollama pull phi3 Pull the Embeddings model: ollama pull nomic-embed-text 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 code along with me. I - First option: Clone the project from GitHub git clone https://github.com/JalelTounsi/oLLaMaStreamPhi.git Run the Application streamlit run app.py II - Second Option: Code Along Create your app.py file Add Imports import streamlit as st
import ollama 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?"}] 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"]) 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 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"]}) 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 Bonus Point Feel free to check this video to understand the code, follow along, and run the AI assistant locally on your computer: AI assistant using Streamlit, Phi3, and Ollama Let's build a chatbot with just Python using the Streamlit library, Ollama , and Microsoft Phi-3 . Streamlit Ollama Microsoft Phi-3 Streamlit 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 . official Streamlit docs Ollama Ollama allows you to run open-source large language models, locally. You can find more info in the official Ollama docs . official Ollama Ollama docs Phi-3 Mini 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 . Phi-3 Mini Phi-3 Mini docs Steps Create a new conda environment conda create --name envStreamPhi conda create --name envStreamPhi Activate the environment conda activate envStreamPhi conda activate envStreamPhi Install ollama Download Ollama from https://ollama.com/download https://ollama.com/download pull the phi-3 model The model we will be using is located here : here ollama pull phi3 ollama pull phi3 Pull the Embeddings model: ollama pull nomic-embed-text ollama pull nomic-embed-text Test installation streamlit hello 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 code along with me. I - First option: Clone the project from GitHub git clone https://github.com/JalelTounsi/oLLaMaStreamPhi.git git clone https://github.com/JalelTounsi/oLLaMaStreamPhi.git Run the Application streamlit run app.py streamlit run app.py II - Second Option: Code Along Create your app.py file app.py Add Imports import streamlit as st
import ollama import streamlit as st
import ollama 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?"}] if "messages" not in st.session_state:
    st.session_state["messages"] = [{"role": "assistant", "content": "Hello tehre, how can I help you, today?"}] 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"]) 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 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 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 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"]}) 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"]}) All the Codebase of app.py 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"]}) 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 streamlit run app.py Bonus Point Feel free to check this video to understand the code, follow along, and run the AI assistant locally on your computer: AI assistant using Streamlit, Phi3, and Ollama AI assistant using Streamlit, Phi3, and Ollama

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