Merge branch 'feature/merge_nlp_implementations' of https://github.com/thepycoder/OpenReader-WebUI into thepycoder-feature/merge_nlp_implementations
This commit is contained in:
Richard Roberson
commit
8cf685633d
4 changed files with 164 additions and 141 deletions
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@ -1,123 +0,0 @@
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import { NextRequest, NextResponse } from 'next/server';
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import nlp from 'compromise';
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const MAX_BLOCK_LENGTH = 300;
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const preprocessSentenceForAudio = (text: string): string => {
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return text
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.replace(/\S*(?:https?:\/\/|www\.)([^\/\s]+)(?:\/\S*)?/gi, '- (link to $1) -')
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.replace(/(\w+)-\s+(\w+)/g, '$1$2') // Remove hyphenation
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// Remove special character *
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.replace(/\*/g, '')
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.replace(/\s+/g, ' ')
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.trim();
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};
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const splitIntoSentences = (text: string): string[] => {
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const paragraphs = text.split(/\n+/);
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const blocks: string[] = [];
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for (const paragraph of paragraphs) {
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if (!paragraph.trim()) continue;
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const cleanedText = preprocessSentenceForAudio(paragraph);
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const doc = nlp(cleanedText);
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const rawSentences = doc.sentences().out('array') as string[];
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let currentBlock = '';
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for (const sentence of rawSentences) {
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const trimmedSentence = sentence.trim();
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if (currentBlock && (currentBlock.length + trimmedSentence.length + 1) > MAX_BLOCK_LENGTH) {
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blocks.push(currentBlock.trim());
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currentBlock = trimmedSentence;
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} else {
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currentBlock = currentBlock
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? `${currentBlock} ${trimmedSentence}`
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: trimmedSentence;
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}
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}
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if (currentBlock) {
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blocks.push(currentBlock.trim());
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}
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}
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return blocks;
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};
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export async function POST(req: NextRequest) {
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// First check if the request body is empty
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const contentLength = req.headers.get('content-length');
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if (!contentLength || parseInt(contentLength) === 0) {
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return NextResponse.json(
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{ error: 'Request body is empty' },
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{ status: 400 }
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);
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}
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// Check content type
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const contentType = req.headers.get('content-type');
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if (!contentType?.includes('application/json')) {
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return NextResponse.json(
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{ error: 'Content-Type must be application/json' },
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{ status: 400 }
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);
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}
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try {
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// Get the raw body text first to validate it's not empty
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const rawBody = await req.text();
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if (!rawBody?.trim()) {
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return NextResponse.json(
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{ error: 'Request body is empty' },
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{ status: 400 }
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);
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}
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// Try to parse the JSON
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let body;
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try {
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body = JSON.parse(rawBody);
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} catch (e) {
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console.error('JSON parse error:', e);
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return NextResponse.json(
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{ error: 'Invalid JSON format' },
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{ status: 400 }
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);
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}
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// Validate the parsed body has the required text field
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if (!body || typeof body !== 'object') {
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return NextResponse.json(
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{ error: 'Invalid request body format' },
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{ status: 400 }
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);
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}
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const { text } = body;
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if (!text || typeof text !== 'string') {
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return NextResponse.json(
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{ error: 'Missing or invalid text field' },
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{ status: 400 }
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);
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}
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if (text.length <= MAX_BLOCK_LENGTH) {
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// Single sentence preprocessing
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const cleanedText = preprocessSentenceForAudio(text);
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return NextResponse.json({ sentences: [cleanedText] });
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}
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// Full text splitting into sentences
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const sentences = splitIntoSentences(text);
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return NextResponse.json({ sentences });
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} catch (error) {
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console.error('Error processing text:', error);
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return NextResponse.json(
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{ error: 'Internal server error' },
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{ status: 500 }
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);
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}
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}
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@ -37,6 +37,7 @@ import { useAudioContext } from '@/hooks/audio/useAudioContext';
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import { getLastDocumentLocation } from '@/utils/indexedDB';
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import { useBackgroundState } from '@/hooks/audio/useBackgroundState';
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import { withRetry } from '@/utils/audio';
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import { processTextToSentences } from '@/utils/nlp';
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// Media globals
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declare global {
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@ -150,28 +151,18 @@ export function TTSProvider({ children }: { children: ReactNode }): ReactElement
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const activeAbortControllers = useRef<Set<AbortController>>(new Set());
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/**
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* Processes text through the NLP API to split it into sentences
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* Processes text into sentences using the shared NLP utility
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*
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* @param {string} text - The text to be processed
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* @returns {Promise<string[]>} Array of processed sentences
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*/
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const processTextToSentences = useCallback(async (text: string): Promise<string[]> => {
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const processTextToSentencesLocal = useCallback(async (text: string): Promise<string[]> => {
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if (text.length < 1) {
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return [];
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}
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const response = await fetch('/api/nlp', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({ text }),
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});
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if (!response.ok) {
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throw new Error('Failed to process text');
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}
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const { sentences } = await response.json();
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return sentences;
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// Use the shared utility directly instead of making an API call
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return processTextToSentences(text);
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}, []);
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/**
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@ -308,7 +299,7 @@ export function TTSProvider({ children }: { children: ReactNode }): ReactElement
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setIsProcessing(true); // Set processing state before text processing starts
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console.log('Setting text:', text);
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processTextToSentences(text)
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processTextToSentencesLocal(text)
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.then(newSentences => {
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if (newSentences.length === 0) {
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console.warn('No sentences found in text');
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@ -337,7 +328,7 @@ export function TTSProvider({ children }: { children: ReactNode }): ReactElement
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duration: 3000,
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});
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});
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}, [isPlaying, handleBlankSection, abortAudio, processTextToSentences]);
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}, [isPlaying, handleBlankSection, abortAudio, processTextToSentencesLocal]);
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/**
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* Toggles the playback state between playing and paused
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153
src/utils/nlp.ts
Normal file
153
src/utils/nlp.ts
Normal file
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@ -0,0 +1,153 @@
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/**
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* Natural Language Processing Utilities
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*
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* This module provides consistent sentence processing functionality across the application.
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* It handles text preprocessing, sentence splitting, and block creation for optimal TTS processing.
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*/
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import nlp from 'compromise';
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const MAX_BLOCK_LENGTH = 300;
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/**
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* Preprocesses text for audio generation by cleaning up various text artifacts
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*
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* @param {string} text - The text to preprocess
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* @returns {string} The cleaned text
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*/
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export const preprocessSentenceForAudio = (text: string): string => {
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return text
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.replace(/\S*(?:https?:\/\/|www\.)([^\/\s]+)(?:\/\S*)?/gi, '- (link to $1) -')
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.replace(/(\w+)-\s+(\w+)/g, '$1$2') // Remove hyphenation
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// Remove special character *
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.replace(/\*/g, '')
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.replace(/\s+/g, ' ')
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.trim();
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};
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/**
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* Splits text into sentences and groups them into blocks suitable for TTS processing
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*
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* @param {string} text - The text to split into sentences
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* @returns {string[]} Array of sentence blocks
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*/
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export const splitIntoSentences = (text: string): string[] => {
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const paragraphs = text.split(/\n+/);
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const blocks: string[] = [];
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for (const paragraph of paragraphs) {
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if (!paragraph.trim()) continue;
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const cleanedText = preprocessSentenceForAudio(paragraph);
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const doc = nlp(cleanedText);
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const rawSentences = doc.sentences().out('array') as string[];
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let currentBlock = '';
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for (const sentence of rawSentences) {
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const trimmedSentence = sentence.trim();
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if (currentBlock && (currentBlock.length + trimmedSentence.length + 1) > MAX_BLOCK_LENGTH) {
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blocks.push(currentBlock.trim());
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currentBlock = trimmedSentence;
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} else {
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currentBlock = currentBlock
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? `${currentBlock} ${trimmedSentence}`
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: trimmedSentence;
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}
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}
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if (currentBlock) {
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blocks.push(currentBlock.trim());
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}
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}
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return blocks;
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};
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/**
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* Main sentence processing function that handles both short and long texts
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*
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* @param {string} text - The text to process
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* @returns {string[]} Array of processed sentences/blocks
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*/
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export const processTextToSentences = (text: string): string[] => {
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if (!text || text.length < 1) {
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return [];
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}
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if (text.length <= MAX_BLOCK_LENGTH) {
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// Single sentence preprocessing
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const cleanedText = preprocessSentenceForAudio(text);
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return [cleanedText];
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}
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// Full text splitting into sentences
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return splitIntoSentences(text);
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};
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/**
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* Gets raw sentences from text without preprocessing or grouping
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* This is useful for text matching and highlighting
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*
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* @param {string} text - The text to extract sentences from
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* @returns {string[]} Array of raw sentences
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*/
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export const getRawSentences = (text: string): string[] => {
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if (!text || text.length < 1) {
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return [];
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}
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return nlp(text).sentences().out('array') as string[];
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};
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/**
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* Enhanced sentence processing that returns both processed sentences and raw sentences
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* This allows for better mapping between the two for click-to-highlight functionality
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*
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* @param {string} text - The text to process
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* @returns {Object} Object containing processed sentences and raw sentences with mapping
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*/
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export const processTextWithMapping = (text: string): {
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processedSentences: string[];
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rawSentences: string[];
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sentenceMapping: Array<{ processedIndex: number; rawIndices: number[] }>;
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} => {
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const rawSentences = getRawSentences(text);
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const processedSentences = processTextToSentences(text);
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// Create a mapping between processed sentences and raw sentences
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const sentenceMapping: Array<{ processedIndex: number; rawIndices: number[] }> = [];
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// For simple mapping, we'll track which raw sentences contributed to each processed sentence
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let rawIndex = 0;
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for (let processedIndex = 0; processedIndex < processedSentences.length; processedIndex++) {
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const processedSentence = processedSentences[processedIndex];
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const rawIndices: number[] = [];
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// Find which raw sentences are contained in this processed sentence
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let remainingText = processedSentence;
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while (rawIndex < rawSentences.length && remainingText.length > 0) {
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const rawSentence = rawSentences[rawIndex];
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const cleanedRawSentence = preprocessSentenceForAudio(rawSentence);
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if (remainingText.includes(cleanedRawSentence) || cleanedRawSentence.includes(remainingText)) {
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rawIndices.push(rawIndex);
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rawIndex++;
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break;
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} else {
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rawIndex++;
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}
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}
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sentenceMapping.push({ processedIndex, rawIndices });
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}
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return {
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processedSentences,
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rawSentences,
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sentenceMapping
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};
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};
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@ -1,9 +1,9 @@
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import { pdfjs } from 'react-pdf';
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import nlp from 'compromise';
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import stringSimilarity from 'string-similarity';
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import type { TextItem } from 'pdfjs-dist/types/src/display/api';
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import type { PDFDocumentProxy } from 'pdfjs-dist';
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import "core-js/proposals/promise-with-resolvers";
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import { processTextToSentences } from '@/utils/nlp';
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// Function to detect if we need to use legacy build
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function shouldUseLegacyBuild() {
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@ -325,7 +325,9 @@ export function handleTextClick(
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if (bestMatch.rating >= similarityThreshold) {
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const matchText = bestMatch.text;
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const sentences = nlp(pdfText).sentences().out('array') as string[];
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// Use the same sentence processing logic as TTSContext for consistency
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const sentences = processTextToSentences(pdfText);
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console.log("sentences inside handleTextClick: %d", sentences.length)
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let bestSentenceMatch = { sentence: '', rating: 0 };
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for (const sentence of sentences) {
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