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import React, { createContext, useState, useRef, useCallback } from "react";
import { AutoProcessor, AutoModelForImageTextToText, RawImage, TextStreamer } from "@huggingface/transformers";
import type { LlavaProcessor, PreTrainedModel, Tensor } from "@huggingface/transformers";
import type { VLMContextValue } from "../types/vlm";
const VLMContext = createContext<VLMContextValue | null>(null);
const MODEL_ID = "onnx-community/FastVLM-0.5B-ONNX";
const MAX_NEW_TOKENS = 512;
export { VLMContext };
export const VLMProvider: React.FC<React.PropsWithChildren> = ({ children }) => {
const [isLoaded, setIsLoaded] = useState(false);
const [isLoading, setIsLoading] = useState(false);
const [error, setError] = useState<string | null>(null);
const processorRef = useRef<LlavaProcessor | null>(null);
const modelRef = useRef<PreTrainedModel | null>(null);
const loadPromiseRef = useRef<Promise<void> | null>(null);
const inferenceLock = useRef(false);
const canvasRef = useRef<HTMLCanvasElement | null>(null);
const loadModel = useCallback(
async (onProgress?: (msg: string) => void) => {
if (isLoaded) {
onProgress?.("Model already loaded!");
return;
}
if (loadPromiseRef.current) {
return loadPromiseRef.current;
}
setIsLoading(true);
setError(null);
loadPromiseRef.current = (async () => {
try {
onProgress?.("Loading processor...");
processorRef.current = await AutoProcessor.from_pretrained(MODEL_ID);
onProgress?.("Processor loaded. Loading model...");
modelRef.current = await AutoModelForImageTextToText.from_pretrained(MODEL_ID, {
dtype: {
embed_tokens: "fp16",
vision_encoder: "q4",
decoder_model_merged: "q4",
},
device: "webgpu",
});
onProgress?.("Model loaded successfully!");
setIsLoaded(true);
} catch (e) {
const errorMessage = e instanceof Error ? e.message : String(e);
setError(errorMessage);
console.error("Error loading model:", e);
throw e;
} finally {
setIsLoading(false);
loadPromiseRef.current = null;
}
})();
return loadPromiseRef.current;
},
[isLoaded],
);
const runInference = useCallback(
async (video: HTMLVideoElement, instruction: string, onTextUpdate?: (text: string) => void): Promise<string> => {
if (inferenceLock.current) {
console.log("Inference already running, skipping frame");
return ""; // Return empty string to signal a skip
}
inferenceLock.current = true;
if (!processorRef.current || !modelRef.current) {
throw new Error("Model/processor not loaded");
}
if (!canvasRef.current) {
canvasRef.current = document.createElement("canvas");
}
const canvas = canvasRef.current;
canvas.width = video.videoWidth;
canvas.height = video.videoHeight;
const ctx = canvas.getContext("2d", { willReadFrequently: true });
if (!ctx) throw new Error("Could not get canvas context");
ctx.drawImage(video, 0, 0);
const frame = ctx.getImageData(0, 0, canvas.width, canvas.height);
const rawImg = new RawImage(frame.data, frame.width, frame.height, 4);
const messages = [
{
role: "system",
content: `You are a helpful visual AI assistant. Respond concisely and accurately to the user's query in one sentence.`,
},
{ role: "user", content: `<image>${instruction}` },
];
const prompt = processorRef.current.apply_chat_template(messages, {
add_generation_prompt: true,
});
const inputs = await processorRef.current(rawImg, prompt, {
add_special_tokens: false,
});
let streamed = "";
const streamer = new TextStreamer(processorRef.current.tokenizer!, {
skip_prompt: true,
skip_special_tokens: true,
callback_function: (t: string) => {
streamed += t;
onTextUpdate?.(streamed.trim());
},
});
const outputs = (await modelRef.current.generate({
...inputs,
max_new_tokens: MAX_NEW_TOKENS,
do_sample: false,
streamer,
repetition_penalty: 1.2,
})) as Tensor;
const decoded = processorRef.current.batch_decode(outputs.slice(null, [inputs.input_ids.dims.at(-1), null]), {
skip_special_tokens: true,
});
inferenceLock.current = false;
return decoded[0].trim();
},
[],
);
return (
<VLMContext.Provider
value={{
isLoaded,
isLoading,
error,
loadModel,
runInference,
}}
>
{children}
</VLMContext.Provider>
);
};
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