Multimodal Integration
Explore how to build end-to-end multimodal AI pipelines that process voice, image, and text data seamlessly. Understand request validation, routing policies, and strategies to prevent UI desynchronization, ensuring stable, latency-aware user interactions and robust artifact management.
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.
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 either 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 each API call, ...