from googletrans import Translator import spacy import gradio as gr import nltk from nltk.corpus import wordnet import wikipedia import re nltk.download('maxent_ne_chunker') #Chunker nltk.download('stopwords') #Stop Words List (Mainly Roman Languages) nltk.download('words') #200 000+ Alphabetical order list nltk.download('punkt') #Tokenizer nltk.download('verbnet') #For Description of Verbs nltk.download('omw') nltk.download('omw-1.4') #Multilingual Wordnet nltk.download('wordnet') #For Definitions, Antonyms and Synonyms nltk.download('shakespeare') nltk.download('dolch') #Sight words nltk.download('names') #People Names NER nltk.download('gazetteers') #Location NER nltk.download('opinion_lexicon') #Sentiment words nltk.download('averaged_perceptron_tagger') #Parts of Speech Tagging spacy.cli.download("en_core_web_sm") nlp = spacy.load('en_core_web_sm') translator = Translator() def Sentencechunker(sentence): Sentchunks = sentence.split(" ") chunks = [] for i in range(len(Sentchunks)): chunks.append(" ".join(Sentchunks[:i+1])) return " | ".join(chunks) def ReverseSentenceChunker(sentence): reversed_sentence = " ".join(reversed(sentence.split())) chunks = Sentencechunker(reversed_sentence) return chunks def three_words_chunk(sentence): words = sentence.split() chunks = [words[i:i+3] for i in range(len(words)-2)] chunks = [" ".join(chunk) for chunk in chunks] return " | ".join(chunks) def keep_nouns_verbs(sentence): doc = nlp(sentence) nouns_verbs = [] for token in doc: if token.pos_ in ['NOUN','VERB','PUNCT']: nouns_verbs.append(token.text) return " ".join(nouns_verbs) def unique_word_count(text="", state=None): if state is None: state = {} words = text.split() word_counts = state for word in words: if word in word_counts: word_counts[word] += 1 else: word_counts[word] = 1 sorted_word_counts = sorted(word_counts.items(), key=lambda x: x[1], reverse=True) return sorted_word_counts, def Wordchunker(word): chunks = [] for i in range(len(word)): chunks.append(word[:i+1]) return chunks def BatchWordChunk(sentence): words = sentence.split(" ") FinalOutput = "" Currentchunks = "" ChunksasString = "" for word in words: ChunksasString = "" Currentchunks = Wordchunker(word) for chunk in Currentchunks: ChunksasString += chunk + " " FinalOutput += "\n" + ChunksasString return FinalOutput # Translate from English to French langdest = gr.Dropdown(choices=["af", "de", "es", "ko", "ja", "zh-cn"], label="Choose Language", value="de") ChunkModeDrop = gr.Dropdown(choices=["Chunks", "Reverse", "Three Word Chunks", "Spelling Chunks"], label="Choose Chunk Type", value="Chunks") def FrontRevSentChunk (Chunkmode, Translate, Text, langdest): FinalOutput = "" TransFinalOutput = "" if Chunkmode=="Chunks": FinalOutput += Sentencechunker(Text) if Chunkmode=="Reverse": FinalOutput += ReverseSentenceChunker(Text) if Chunkmode=="Three Word Chunks": FinalOutput += three_words_chunk(Text) if Chunkmode=="Spelling Chunks": FinalOutput += BatchWordChunk(Text) if Translate: TransFinalOutput = FinalOutput translated = translator.translate(TransFinalOutput, dest=langdest) FinalOutput += "\n" + translated.text return FinalOutput # Define a function to filter out non-verb, noun, or adjective words def filter_words(words): # Use NLTK to tag each word with its part of speech tagged_words = nltk.pos_tag(words) # Define a set of parts of speech to keep (verbs, nouns, adjectives) keep_pos = {'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'NN', 'NNS', 'NNP', 'NNPS', 'JJ', 'JJR', 'JJS'} # Filter the list to only include words with the desired parts of speech filtered_words = [word for word, pos in tagged_words if pos in keep_pos] return filtered_words def SepHypandSynExpansion(text): # Tokenize the text tokens = nltk.word_tokenize(text) NoHits = "" FinalOutput = "" # Find synonyms and hypernyms of each word in the text for token in tokens: synonyms = [] hypernyms = [] for synset in wordnet.synsets(token): synonyms += synset.lemma_names() hypernyms += [hypernym.name() for hypernym in synset.hypernyms()] if not synonyms and not hypernyms: NoHits += f"{token} | " else: FinalOutput += "\n" f"{token}: hypernyms={hypernyms}, synonyms={synonyms} \n" NoHits = set(NoHits.split(" | ")) NoHits = filter_words(NoHits) NoHits = "Words to pay special attention to: \n" + str(NoHits) return NoHits, FinalOutput def WikiSearch(term): termtoks = term.split(" ") for item in termtoks: # Search for the term on Wikipedia and get the first result result = wikipedia.search(item, results=20) return result def find_string_positions(s, string): positions = [] start = 0 while True: position = s.find(string, start) if position == -1: break positions.append(position) start = position + len(string) return positions def splittext(string, split_positions): split_strings = [] prepos = 0 for pos in split_positions: pos -= 12 split_strings.append((string[prepos:pos])) #, string[pos:])) prepos = pos FinalOutput = "" stoutput = "" linenumber = 1 print(linenumber) for item in split_strings[1:]: stoutput = item[0:29] + "\n" + item[30:] stspaces = find_string_positions(stoutput, " ") FinalOutput += str(linenumber) + "\n" + stoutput[:stspaces[-2]] + "\n" FinalOutput += "\n" linenumber += 1 return FinalOutput[2:] def create_dictionary(word_list, word_dict = {}): word_list = set(word_list.split(" ")) for word in word_list: key = word[:2] if key not in word_dict: word_dict[key] = [word] else: word_dict[key].append(word) return word_dict def merge_lines(doc1, doc2, doc3, doc4): docs = [doc1, doc2, doc3, doc4] merged_lines = [] with open("merged_lines.txt", "w") as f, \ *map(open, docs) as handles: for lines in zip(*handles): merged_line = "".join(line.strip() for line in lines) f.write(merged_line + "\n") merged_lines.append(merged_line) return merged_lines def TTSforListeningPractice(text): return "not finished" with gr.Blocks() as lliface: with gr.Tab("Welcome "): gr.HTML("""

Spaces Test - Still Undercontruction

You only learn when you convert things you dont know to known --> Normally Repetition is the only reliable method for everybody

Knowledge is a Language but productive knowledge is find replace as well

LingQ is good option for per word state management

Arrows app json creator for easy knowledge graphing and spacy POS graph?

Vocab = Glossary + all non text wall(lists, diagrams, etc.)

https://huggingface.co/spaces/vumichien/whisper-speaker-diarization

""") gr.Interface(fn=unique_word_count, inputs="text", outputs="text", title="Wordcounter") gr.Interface(fn=SepHypandSynExpansion, inputs="text", outputs=["text", "text"], title="Word suggestions") gr.Interface(fn=WikiSearch, inputs="text", outputs="text", title="Unique word suggestions(wiki articles)") with gr.Tab("Spelling and Chunks"): gr.HTML("

Spelling is the end goal, you already know many letter orders called words so you need leverage them to remember random sequences") with gr.Tab("Spelling Simplification - Use a dual language list"): gr.Interface(fn=create_dictionary, inputs="text", outputs="text", title="Sort Text by first two letters") with gr.Tab("Chunks"): gr.Interface(fn=FrontRevSentChunk, inputs=[ChunkModeDrop, "checkbox", "text", langdest], outputs="text") gr.Interface(fn=keep_nouns_verbs, inputs=["text"], outputs="text", title="Noun and Verbs only (Plus punctuation)") with gr.Tab("Timing Practice - Repitition"): gr.HTML("

Run from it, Dread it, Repitition is inevitable - Thanos

Next Milestone is Turning this interface handsfree

") gr.HTML("""""") with gr.Tab("Knowledge Ideas"): gr.HTML("""

Good knowledge = ability to answer questions --> find Questions you cant answer and look for hidden answer within them

My One Word Theory = We only use more words than needed when we have to or are bored --> Headings exist because title is not sufficient, subheadings exist because headings are not sufficient, Book Text exists because subheadings are not sufficient

Big Picture = Expand the Heading and the subheadings and compare them to each other

Application of Knowledge = App Version of the text (eg. Jupyter Notebooks) is what you create and learn first

""") with gr.Tab("Beginner - Songs - Chorus"): gr.HTML("Essentially if the sounds are repeated or long notes they are easy to remember") gr.Interface(fn=TTSforListeningPractice, inputs="text", outputs="text", title="Placeholder - paste chorus here and use TTS or make notes to save here") with gr.Tab("Transcribe - RASMUS Whisper"): gr.HTML("""

If this tab doesnt work use the link below ⬇️

https://huggingface.co/spaces/RASMUS/Whisper-youtube-crosslingual-subtitles""") gr.Interface.load("spaces/RASMUS/Whisper-youtube-crosslingual-subtitles", title="Subtitles") with gr.Tab("Advanced - LingQ Addons ideas"): gr.HTML("Extra functions needed - Persitent Sentence translation, UNWFWO, POS tagging and Word Count per user of words in their account. Macaronic Text is also another way to practice only the important information") with gr.Row(): RomanFile = gr.File(label="Paste Roman") W4WFile = gr.File(label="Paste Word 4 Word") FullMeanFile = gr.File(label="Paste Full Meaning") MacaronicFile = gr.File(label="Paste Macaronic Text") with gr.Row(): MergeButton = gr.Button() with gr.Row(): MergeOutput = gr.TextArea(label="Output") MergeButton.click(merge_lines, inputs=[RomanFile, W4WFile, FullMeanFile, MacaronicFile], outputs=[MergeOutput]) with gr.Tab("Dictionary from text"): gr.Interface(fn=create_dictionary, inputs="text", outputs="text", title="Two Letter Dictionary") lliface.launch()