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74 lines
3.1 KiB
Markdown
74 lines
3.1 KiB
Markdown
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# Streamlit
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> **[Streamlit](https://streamlit.io/) is a faster way to build and share data apps.**
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> Streamlit turns data scripts into shareable web apps in minutes. All in pure Python. No front‑end experience required.
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> See more examples at [streamlit.io/generative-ai](https://streamlit.io/generative-ai).
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[](https://codespaces.new/langchain-ai/streamlit-agent?quickstart=1)
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In this guide we will demonstrate how to use `StreamlitCallbackHandler` to display the thoughts and actions of an agent in an
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interactive Streamlit app. Try it out with the running app below using the [MRKL agent](/docs/modules/agents/how_to/mrkl/):
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<iframe loading="lazy" src="https://mrkl-minimal.streamlit.app/?embed=true&embed_options=light_theme"
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style={{ width: 100 + '%', border: 'none', marginBottom: 1 + 'rem', height: 600 }}
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allow="camera;clipboard-read;clipboard-write;"
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></iframe>
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## Installation and Setup
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```bash
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pip install langchain streamlit
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```
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You can run `streamlit hello` to load a sample app and validate your install succeeded. See full instructions in Streamlit's
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[Getting started documentation](https://docs.streamlit.io/library/get-started).
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## Display thoughts and actions
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To create a `StreamlitCallbackHandler`, you just need to provide a parent container to render the output.
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```python
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from langchain.callbacks import StreamlitCallbackHandler
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import streamlit as st
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st_callback = StreamlitCallbackHandler(st.container())
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```
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Additional keyword arguments to customize the display behavior are described in the
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[API reference](https://api.python.langchain.com/en/latest/modules/callbacks.html#langchain.callbacks.StreamlitCallbackHandler).
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### Scenario 1: Using an Agent with Tools
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The primary supported use case today is visualizing the actions of an Agent with Tools (or Agent Executor). You can create an
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agent in your Streamlit app and simply pass the `StreamlitCallbackHandler` to `agent.run()` in order to visualize the
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thoughts and actions live in your app.
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```python
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from langchain.llms import OpenAI
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from langchain.agents import AgentType, initialize_agent, load_tools
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from langchain.callbacks import StreamlitCallbackHandler
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import streamlit as st
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llm = OpenAI(temperature=0, streaming=True)
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tools = load_tools(["ddg-search"])
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agent = initialize_agent(
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tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
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)
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if prompt := st.chat_input():
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st.chat_message("user").write(prompt)
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with st.chat_message("assistant"):
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st_callback = StreamlitCallbackHandler(st.container())
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response = agent.run(prompt, callbacks=[st_callback])
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st.write(response)
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```
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**Note:** You will need to set `OPENAI_API_KEY` for the above app code to run successfully.
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The easiest way to do this is via [Streamlit secrets.toml](https://docs.streamlit.io/library/advanced-features/secrets-management),
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or any other local ENV management tool.
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### Additional scenarios
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Currently `StreamlitCallbackHandler` is geared towards use with a LangChain Agent Executor. Support for additional agent types,
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use directly with Chains, etc will be added in the future.
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