Building Interactive AI Media Canvas Tools: React, Polotno, Redux Toolkit & Generative Pipelines

The Frontier of AI-Native Creative Interfaces

Building generative AI products like Gimmefy AI requires moving beyond basic text-chat inputs toward rich, interactive browser canvases where users can co-create, edit, and manipulate visual media alongside intelligent assistants.

Delivering this experience demands a rock-solid frontend architecture capable of orchestrating Polotno canvas rendering, multi-layer vector operations, Redux Toolkit state serialization, and asynchronous AI generation streams.

1. Canvas Layer Management with Polotno & React

The canvas engine must support complex graphical transformations (cropping, scaling, filters, text overlays, and AI inpainting) at 60 FPS:

tsx

import React, { useEffect, useRef } from 'react';
import { createStore } from 'polotno/model/store';
import { Workspace } from 'polotno/canvas/workspace';
import { PolotnoContainer, SidePanelWrap, WorkspaceWrap } from 'polotno';

export function AICanvasEditor({ initialImageUrl }: { initialImageUrl?: string }) {
  const storeRef = useRef(createStore());
  const store = storeRef.current;

  useEffect(() => {
    // Initialize canvas page
    const page = store.addPage();
    if (initialImageUrl) {
      page.addElement({
        type: 'image',
        src: initialImageUrl,
        x: 50,
        y: 50,
        width: 600,
        height: 400,
      });
    }
  }, [store, initialImageUrl]);

  return (
    <PolotnoContainer className="h-[800px] w-full border rounded-xl overflow-hidden shadow-2xl">
      <SidePanelWrap store={store} />
      <WorkspaceWrap>
        <Workspace store={store} />
      </WorkspaceWrap>
    </PolotnoContainer>
  );
}

2. Managing Complex Undo/Redo & State in Redux Toolkit

While the canvas maintains local DOM coordinates, the application state (active project, AI prompt history, layer revisions, export presets) is managed via Redux Toolkit:

typescript

import { createSlice, PayloadAction } from '@reduxjs/toolkit';

interface CanvasState {
  activeTool: 'select' | 'text' | 'ai-inpaint' | 'crop';
  isGeneratingAI: boolean;
  historyTimeline: string[];
  activeStep: number;
}

const initialState: CanvasState = {
  activeTool: 'select',
  isGeneratingAI: false,
  historyTimeline: [],
  activeStep: 0,
};

export const canvasSlice = createSlice({
  name: 'canvas',
  initialState,
  reducers: {
    setTool(state, action: PayloadAction<CanvasState['activeTool']>) {
      state.activeTool = action.payload;
    },
    recordSnapshot(state, action: PayloadAction<string>) {
      state.historyTimeline = state.historyTimeline.slice(0, state.activeStep + 1);
      state.historyTimeline.push(action.payload);
      state.activeStep = state.historyTimeline.length - 1;
    },
    undo(state) {
      if (state.activeStep > 0) state.activeStep -= 1;
    },
    redo(state) {
      if (state.activeStep < state.historyTimeline.length - 1) state.activeStep += 1;
    },
  },
});

3. Streaming AI Generations Directly to Canvas Layers

Instead of blocking the UI during diffusion model generation, we stream progress percentages over Server-Sent Events (SSE) and replace temporary shimmer placeholders upon generation completion: