> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Gemini Streamlit UI

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

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
flowchart LR
    In[Input] --> UI[("Streamlit UI")]
    VDB[(Vector DB)] --> Agent
    Gemini[("Gemini")] --> Agent
    UI --> Agent[("Knowledge Agent")]
    Agent --> |Query| VDB
    Agent --> Out[Output]
    Out --> UI
    
    style In fill:#8B0000,color:#fff
    style UI fill:#FF4B4B,color:#fff,shape:circle
    style Gemini fill:#4169E1,color:#fff,shape:circle
    style Agent fill:#2E8B57,color:#fff,shape:circle
    style VDB fill:#4169E1,color:#fff,shape:cylinder
    style Out fill:#8B0000,color:#fff
```

## Prerequisites

<Steps>
  <Step title="Install Package">
    Install required packages:

    ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    pip install "praisonaiagents[llm]" streamlit
    ```

    <Note>
      streamlit for UI<br />
      praisonaiagents\[llm] for Gemini model access (It uses Litellm)
    </Note>
  </Step>

  <Step title="Setup Environment">
    Configure environment:

    ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    export GOOGLE_API_KEY=your-api-key
    ```

    <Note>
      Get your API key from [Google AI Studio](https://makersuite.google.com/app/apikey)
    </Note>
  </Step>

  <Step title="Create File">
    Create a new file called `app.py` and add the following code:
  </Step>

  <Step title="Run Application">
    Start the Streamlit application:

    ```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    streamlit run app.py
    ```
  </Step>
</Steps>

## Code

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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

<CardGroup cols={2}>
  <Card title="Interactive Chat" icon="comments">
    Real-time chat interface with message history.
  </Card>

  <Card title="Knowledge Base" icon="database">
    RAG capabilities with ChromaDB integration.
  </Card>

  <Card title="Model Integration" icon="server">
    Uses Google's Gemini Pro model.
  </Card>

  <Card title="Session Management" icon="clock-rotate-left">
    Maintains chat history in session state.
  </Card>
</CardGroup>

<Note>
  Make sure you have a valid Google API key and sufficient quota for using Gemini models.
</Note>