Does injecting ads change how my Streamlit model is called?

No. Your Streamlit model call is unchanged; you only wrap the resulting stream, so ad injection is additive rather than a rewrite.

Will the response still stream token by token in Streamlit?

Yes. Injection preserves streaming — ads are placed inside the token stream, so the client still receives a live, incremental response.

How-to guide

How to Inject Ads Into a Streaming Response in Streamlit

This guide shows how to inject ads into a streaming response in a Streamlit app, the goal being to wrap the LLM token stream so contextual ads appear inside the assistant's reply. It builds on the Monetzly gRPC service (no Python SDK exists for this stack), so the approach is specific to how Streamlit produces and streams responses.

Overview

This is the core of monetizing a Streamlit app: instead of returning the raw model stream, you pass it through Monetzly, which injects contextual, labelled ads into the token stream and hands you back the enhanced stream to forward to the client.

You keep your existing Streamlit model call untouched. Injection is additive — a wrapper around the stream you already produce, keyed on the session and the live prompt so the ad matches what the user is asking about.

Streamlit apps are usually built for AI demos, internal LLM tools, and chat prototypes with st.chat_message, so inject ads into a streaming response typically comes up while a user is mid-conversation, the moment where monetization has to be additive rather than disruptive.

How this works on Streamlit

Streamlit is Python, so there is no native SDK. Drive the shipped tps_alter.proto gRPC service from Python (or a Node sidecar) and render the injected tokens with st.write_stream. Confirm the non-JS auth handshake before publishing.

Since Streamlit is a Python stack with no official SDK, this task runs through the shipped tps_alter.proto gRPC service (or a Node sidecar); the auth handshake for a direct client is still TODO_VERIFY.

Steps

Produce your Streamlit response as a token stream as you already do.
Pass that stream into the Monetzly injection call with the session id and prompt.
Forward the enhanced tokens to the client (SSE, WebSocket, or your existing transport).

Integration snippet

TODO_VERIFY: Streamlit uses the gRPC proto path, confirm the auth handshake with Monetzly before relying on this in production.

# No Python SDK. Generate stubs from tps_alter.proto and drive ProcessStream
# (see the FastAPI page for the full bidi loop), then:
#
#   import streamlit as st
#   st.write_stream(inject(llm_tokens, prompt, session_id, api_key))
#
# TODO_VERIFY: auth handshake (API key placement) for a direct gRPC client.