Multimodal Integration
Explore how to create reliable multimodal AI workflows that process audio, text, and images together. Understand how to manage API requests, validate data at each step, enforce routing policies, and secure interactions. This lesson helps you design integrated pipelines that maintain coherence, avoid UI desync, and handle failures effectively in production systems.
A single user interaction can include voice plus an image, and the app still needs to respond inside a latency budget without ambiguous UI states like did it hear me and did it see the image. That pressure forces an end-to-end pipeline where each step declares what it consumes and produces, and where the UI renders only artifacts tied to the current request.
This lesson's scope covers one Streamlit workflow that calls existing FastAPI endpoints for POST /stt, POST /vision, POST /images, and POST /tts, while persisting a shared request state.
Before touching routing, trace one request boundary to boundary and name the artifacts that flow. Streamlit captures audio as bytes, sends those bytes to POST /stt, receives transcript text, then chooses a text-only path or a text plus image path. If an image is attached, the UI sends image bytes plus the transcript to POST /vision and receives a structured JSON result. If the user asked for an output image, the UI calls POST /images with a prompt plus optional input image bytes, and receives image bytes or a URL plus metadata. Finally, the UI selects a short final answer string and sends it to POST /tts to receive audio bytes for playback.
The request stays coherent only if we validate and log at the boundaries where types can silently drift. Validate media types at upload and before ...