Deploy an AI Model using Streamlit Library

Streamlit is an open-source Python library designed to make it easy for developers and data scientists to turn Python scripts into fully functional web applications without requiring any front-end development skills. It allows us to quickly prototype and deploy interactive AI-powered apps directly from our local machine or the cloud.

Implementation

Let's implement our model:

Step 1: Install dependencies

We will install the required dependencies for our model such as streamlit, google-generativeai.

pip install streamlit python-dotenv google-genai

Step 2: Set Up API Key

We need to create a environment file named .env in project directory to store our API Key.

GEMINI_API_KEY="your_api_key_here"

Step 3: Build the Model

Now we will build our model:

• Environment Setup: The .env file stores the API key securely, loaded with dotenv.
• Model Initialization: The Gemini model "models/gemini-2.5-flash" is loaded using Google’s GenAI SDK.
• Session Management: st.session_state ensures chat history persists during interaction.
• Real-Time Interaction: Users type queries and responses are fetched dynamically from Gemini.
• Auto Refresh: st.rerun() refreshes the app interface after each user message.

import streamlit as st
from google import genai
from dotenv import load_dotenv
import os

load_dotenv()
API_KEY = os.getenv("GEMINI_API_KEY")

if not API_KEY:
    st.error("Missing GEMINI_API_KEY in .env file. Please add it and restart the app.")
    st.stop()

client = genai.Client(api_key=API_KEY)
MODEL_NAME = "models/gemini-2.5-flash"

st.set_page_config(page_title="PersonalBot", layout="centered")
st.title("🤖 PersonalBot")
st.markdown("An intelligent assistant powered by **Gemini** and **Streamlit**.")

if "history" not in st.session_state:
    st.session_state.history = []


def get_gemini_response(prompt):
    response = client.models.generate_content(
        model=MODEL_NAME,
        contents=prompt
    )
    return response.text


for role, msg in st.session_state.history:
    if role == "user":
        st.markdown(f"**🧑 You:** {msg}")
    else:
        st.markdown(f"**🤖 PersonalBot:** {msg}")

user_input = st.text_input("Type your message here...")

if st.button("Send"):
    if user_input.strip():
        st.session_state.history.append(("user", user_input))
        with st.spinner("PersonalBot is thinking..."):
            try:
                reply = get_gemini_response(user_input)
            except Exception as e:
                reply = f" Error: {e}"

        st.session_state.history.append(("bot", reply))
        st.rerun()
    else:
        st.warning("Please enter a message before sending.")

Step 4: Run the Streamlit App

We will start the Streamlit server and it will open our chatbot model in browser. The default URL is usually http://localhost:8501.

python -m streamlit run app.py

Output:

The source code can be downloaded from here.

Advantages

• Rapid Deployment: Streamlit makes it effortless to transform simple Python scripts into interactive web apps which is perfect for quick AI demos or prototypes.
• Intelligent AI Responses: Integrating Google Gemini ensures the model provides human-like, context-aware answers with exceptional reasoning and creativity.
• Interactive User Interface: Streamlit offers dynamic UI components like text inputs, buttons and markdowns to build engaging, chat-style AI interfaces.
• Easy Integration & Scalability: The architecture can be easily extended, allowing developers to connect databases, APIs or even train custom models for specialized tasks.