[Summer 2024] Building a Claude AI Chat App with Streamlit
As a Streamlit developer, you might have come across Streamlit's excellent tutorial for building LLM chat apps. It's a great starting point, but what if you want to use a different AI model, like Anthropic's Claude? In this post, we'll explore how to adapt the Streamlit tutorial to work with Claude, highlighting the key differences and providing insights for those new to AI app development.
The Basics: What's Different?
Before we dive into the code, let's outline the main differences between the official Streamlit tutorial and our Claude implementation:
API Integration: We're swapping OpenAI's API for Anthropic's.
Model Selection: Instead of GPT models, we're using Claude models.
Streaming Implementation: Claude has its own method for streaming responses.
Message Handling: While similar, Claude has specific requirements we need to address.
The Code: A Side-by-Side Comparison
Let's look at some key code snippets to see how the implementations differ:
1. Importing and Setting Up
Official Streamlit Tutorial:
import openai
import streamlit as st
client = openai.OpenAI(api_key=st.secrets["OPENAI_API_KEY"])
Our Claude Implementation:
import anthropic
import streamlit as st
client = anthropic.Anthropic(api_key=st.secrets["ANTHROPIC_API_KEY"])
The main difference here is the library we're importing and the client we're initializing.
2. Making API Calls
Official Streamlit Tutorial:
stream = client.chat.completions.create(
model=st.session_state["openai_model"],
messages=[
{"role": m["role"], "content": m["content"]}
for m in st.session_state.messages
],
stream=True,
)
Our Claude Implementation:
with client.messages.stream(
model="claude-3-5-sonnet-20240620",
max_tokens=1024,
messages=[{"role": "user", "content": prompt}]
) as stream:
# Streaming logic here
Notice how the Claude implementation uses a with statement and has slightly different parameters.
3. Handling Streamed Responses
Official Streamlit Tutorial:
response = st.write_stream(stream)
Our Claude Implementation:
text in stream.text_stream:
response_text += text
message_placeholder.markdown(response_text)
The Claude implementation requires us to manually handle the streaming text and update the UI.
Key Takeaways for New Developers
API Flexibility: Different AI providers have different APIs. Learning to adapt to these differences is a valuable skill.
Streaming Implementations: Streaming responses can vary between providers. Understanding how to handle these streams is crucial for creating responsive AI apps.
Model-Specific Parameters: Each AI model may have unique parameters. Always refer to the provider's documentation.
Error Handling: (Not shown in snippets) Remember to implement proper error handling for API calls and response processing.
UI Considerations: When working with streamed responses, think about how to update the UI smoothly for a good user experience.
Conclusion
Adapting the Streamlit tutorial to work with Claude demonstrates the flexibility of Streamlit and the similarities between different AI APIs. As a new developer, exercises like this can help you understand the core concepts of AI integration while learning to work with different tools and APIs.
Ready to try it yourself? Check out the full implementation on my GitHub and start building your own AI-powered chat apps with Streamlit and Claude!
If you want to try porting the logic yourself, feel free to refer to their offficial docs:
OpenAI x Streamlit Official Tutorial
Happy coding!
#tutorial #streamlit #anthropic #python #claude