Praison AI

Gemini Streamlit UI

Create interactive chat interfaces with Google's Gemini models using Streamlit

Diagram

Query

Input

Streamlit UI

Vector DB

Knowledge Agent

Gemini

Output

Prerequisites

  1. Install Package

    Install required packages:

    bash
    pip install "praisonaiagents[llm]" streamlit
  2. Setup Environment

    Configure environment:

    bash
    export GOOGLE_API_KEY=your-api-key
  3. Create File

    Create a new file called app.py and add the following code:

  4. Run Application

    Start the Streamlit application:

    bash
    streamlit run app.py

Code

python
import streamlit as st
from praisonaiagents import Agent

st.title("Gemini 2.0 Thinking AI Agent")

# Initialize the agent
@st.cache_resource
def get_agent():
    llm_config = {
        "model": "gemini/gemini-2.0-flash-thinking-exp-01-21",
        "response_format": {"type": "text"}
    }

    return Agent(
        instructions="You are a helpful assistant",
        llm=llm_config
    )

agent = get_agent()

# Create text area input field
user_question = st.text_area("Ask your question:", height=150)

# Add ask button
if st.button("Ask"):
    if user_question:
        with st.spinner('Thinking...'):
            result = agent.start(user_question)
            st.write("### Answer")
            st.write(result)
    else:
        st.warning("Please enter a question")

Features

Interactive Chat

Real-time chat interface with message history.

 Knowledge Base

RAG capabilities with ChromaDB integration.

 Model Integration

Uses Google’s Gemini Pro model.

 Session Management

Maintains chat history in session state.