app.py

from googletrans import Translator
import spacy
import gradio as gr
import nltk
from nltk.corpus import wordnet
import wikipedia
import re
import time
import random

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 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(roman_file, w4w_file, full_mean_file, macaronic_file):
    files = [roman_file, w4w_file, full_mean_file, macaronic_file]
    merged_lines = []

    with open(roman_file.name, "r") as f1, open(w4w_file.name, "r") as f2, \
            open(full_mean_file.name, "r") as f3, open(macaronic_file.name, "r") as f4:
        for lines in zip(f1, f2, f3, f4):
            merged_line = "\n".join(line.strip() for line in lines)
            merged_lines.append(merged_line)

    return "\n".join(merged_lines)

def TTSforListeningPractice(text):
	return "not finished"

def group_words(inlist):
    inlisttoks = inlist.split(" ")
    inlistset = set(inlisttoks)

    word_groups = []
    current_group = []

    for word in inlisttoks:
        current_group.append(word)
        if len(current_group) == 10:
            word_groups.append(current_group)
            current_group = []
    if current_group:
        word_groups.append(current_group)

    current_group_index = 0
    current_group_time = 0

    while True:
        if current_group_time == 60:
            current_group_index = (current_group_index + 1) % len(word_groups)
            current_group_time = 0
        else:
            if current_group_time % 10 == 0:
                random.shuffle(word_groups[current_group_index])
            current_group_time += 10

        yield " ".join(word_groups[current_group_index])
        time.sleep(10) 

def split_verbs_nouns(text):
    nlp = spacy.load("en_core_web_sm")
    doc = nlp(text)
    
    verbs_nouns = []
    other_words = []
    
    for token in doc:
        if token.pos_ in ["VERB", "NOUN"]:
            verbs_nouns.append(token.text)
        elif token.text in [punct.text for punct in doc if punct.is_punct]:
            verbs_nouns.append(token.text)
            other_words.append(token.text)
        else:
            other_words.append(token.text)
    
    verbs_nouns_text = " ".join(verbs_nouns)
    other_words_text = " ".join(other_words)
    
    return verbs_nouns_text, other_words_text

def split_srt_file(text): #file_path):
    # Open the SRT file and read its contents
    #with open(file_path, 'r') as f:
    #    srt_contents = f.read()
    srt_contents = text
    
    # Split the SRT file by timestamp
    srt_sections = srt_contents.split('\n\n')

    # Loop through each section of the SRT file
    for i in range(len(srt_sections)):
        # Split the section into its timestamp and subtitle text
        section_lines = srt_sections[i].split('\n')
        timestamp = section_lines[1]
        subtitle_text = ' | '.join(section_lines[2:])

        # Replace spaces in the subtitle text with " | "
        subtitle_text = subtitle_text.replace(' ', ' | ')

        # Reconstruct the section with the updated subtitle text
        srt_sections[i] = f"{section_lines[0]}\n{timestamp}\n{subtitle_text[3:]}"

    # Join the SRT sections back together into a single string
    return '\n\n'.join(srt_sections)

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 = find_string_positions(string, " --> ")  
  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:]   

groupinput_text = gr.inputs.Textbox(lines=2, label="Enter a list of words")
groupoutput_text = gr.outputs.Textbox(label="Grouped words")

with gr.Blocks() as lliface:
  with gr.Tab("Welcome"):
    gr.HTML("""<h1> Spaces Test - Still Undercontruction </h1> <p> You only learn when you convert things you dont know to known --> Normally Repetition is the only reliable method for everybody </p>  
    <p> Knowledge is a Language but productive knowledge is find replace as well </p> <p>LingQ is good option for per word state management</p> <p> Arrows app json creator for easy knowledge graphing and spacy POS graph? </p> 
    <p> Vocab = Glossary + all non text wall(lists, diagrams, etc.)</p>
	<p> https://huggingface.co/spaces/vumichien/whisper-speaker-diarization<br></p>
    <p> In Language the goal is bigger vocab --> Knowledge equivalent = question answer pairs but to get to those you need related information pairs</p> 
    <p> ChatGPT Turns Learning into a read only what you dont know ask only what you dont know feedback loop --> All you have to do is keep track of what prompts you have asked in the past</p>
    <p> Spell multiple words simultaneously for simultaneous access </p>
    """)
    with gr.Tab("Unique word ID"):
      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("Automating related information linking"):
      gr.HTML("Questions - Tacking and suggesting questions to ask = new education")  
  with gr.Tab("Spelling and Chunks"):
    gr.Text("Merged Spelling Practice Placeholder - Spell multiple words simultaneously for simultaneous access")  
    gr.HTML("<p> 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("<p>Run from it, Dread it, Repitition is inevitable - Thanos</p> <p>Next Milestone is Turning this interface handsfree</p>")  
    with gr.Tab("Gradio Version"):
      gr.Interface(fn=group_words, inputs=groupinput_text, outputs=groupoutput_text, title="Word Grouping and Rotation", description="Group a list of words into sets of 10 and rotate them every 60 seconds.").queue()          
    with gr.Tab("HTML Version"):
      gr.HTML("""<iframe height="1200" style="width: 100%;" scrolling="no" title="Memorisation Aid" src="https://codepen.io/kwabs22/embed/preview/GRXKQgj?default-tab=result&editable=true" frameborder="no" loading="lazy" allowtransparency="true" allowfullscreen="true">
  See the Pen <a href="https://codepen.io/kwabs22/pen/GRXKQgj">
  Memorisation Aid</a> by kwabs22 (<a href="https://codepen.io/kwabs22">@kwabs22</a>)
  on <a href="https://codepen.io">CodePen</a>.
</iframe>""")
  with gr.Tab("Knowledge Ideas"):
      gr.HTML("""<p>Good knowledge = ability to answer questions --> find Questions you cant answer and look for hidden answer within them </p>
      <p>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</p>
      <p>Big Picture = Expand the Heading and the subheadings and compare them to each other</p>
      <p>Application of Knowledge = App Version of the text (eg. Jupyter Notebooks) is what you create and learn first</p>
	  """)  
  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("""<p>If this tab doesnt work use the link below ⬇️</p> <a href="https://huggingface.co/spaces/RASMUS/Whisper-youtube-crosslingual-subtitles">https://huggingface.co/spaces/RASMUS/Whisper-youtube-crosslingual-subtitles</a>""")
    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.Tab("Merged Subtitles"):
        gr.HTML("SRT Contents to W4W Split SRT for Google Translate")
        gr.Interface(fn=split_srt_file, inputs="text", outputs="text")
        gr.HTML("Text for w4w creation in G Translate")
        gr.Interface(fn=splittext, inputs="text", outputs="text")
        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")
            SentGramFormula = gr.File(label="Paste Sentence Grammar Formula 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("Sentence to Format"):
        gr.Interface(fn=split_verbs_nouns , inputs="text", outputs=["text", "text"], title="Comprehension reading and Sentence Format Creator")
        gr.Text("Text to Closed Class + Adjectives + Punctuation or Noun Verb + Punctuation ")
  with gr.Tab("Dictionary from text"):
    gr.Interface(fn=create_dictionary, inputs="text", outputs="text", title="Two Letter Dictionary")
  

lliface.launch()