Streamlit Agent

Learn how to create web interfaces for your AI agents using Streamlit

Quick Start

1. Install Dependencies

   First, install the required packages:

   pip install praisonaiagents streamlit
   
2. Create Script

   Create a new file app.py:

   import streamlit as st
   from praisonaiagents import Agent, Tools
   from praisonaiagents import duckduckgo
   
   st.title("AI Research Assistant")
   st.write("Enter your research query below to get started!")
   
   # Initialize the research agent
   agent = Agent(instructions="You are a Research Agent", tools=[duckduckgo])
   
   # Create the input field
   query = st.text_input("Research Query", placeholder="Enter your research topic...")
   
   # Add a search button
   if st.button("Search"):
       if query:
           with st.spinner("Researching..."):
               result = agent.start(query)
               st.write(result)
       else:
           st.warning("Please enter a research query")
   
3. Run Application

   Run your Streamlit app:

   streamlit run app.py
   

Features

Easy Integration

Seamlessly integrate AI agents with Streamlit’s UI components.

Interactive UI

Create responsive interfaces with real-time updates.

Progress Indicators

Built-in loading states and progress indicators.

Rich Output

Display formatted text, markdown, and other rich content.

Understanding the Code

The example demonstrates a simple research assistant with these key components:

1. UI Setup:

   • Title and description using st.title() and st.write()
   • Input field with st.text_input()
   • Search button with st.button()

2. Agent Integration:

   • Initialize the AI agent with specific instructions
   • Connect the agent to the UI components
   • Handle user input and display results

3. User Experience:

   • Loading spinner during processing
   • Input validation and error messages
   • Clean result display

Customization

You can enhance the UI with additional Streamlit components:

# Add sidebar options
st.sidebar.title("Settings")
model = st.sidebar.selectbox("Select Model", ["GPT-3", "GPT-4"])

# Add multiple input types
text_input = st.text_area("Long Query", height=100)
file_input = st.file_uploader("Upload File")

# Display results with formatting
st.markdown("### Results")
st.json(structured_data)
st.dataframe(tabular_data)

Next Steps

• Learn about Prompt Chaining for complex UI workflows
• Explore Evaluator Optimizer for improving responses
• Check out Gradio Integration for an alternative UI framework