Update app.py

app.py

@@ -18,6 +18,10 @@ import numpy as np
 from docx import Document
 import textwrap
 import pandas as pd
+import pykakasi
+import hangul_romanize
+import pinyin
+from langdetect import detect
 
 #Uncomment these for Huggingface
 nltk.download('maxent_ne_chunker') #Chunker
@@ -39,6 +43,15 @@ spacy.cli.download("en_core_web_sm")
 spacy.cli.download('ko_core_news_sm')
 spacy.cli.download('ja_core_news_sm')
 spacy.cli.download('zh_core_web_sm')
+spacy.cli.download("es_core_news_sm")
+spacy.cli.download("de_core_news_sm")
+
+nlp_en = spacy.load("en_core_web_sm")
+nlp_de = spacy.load("de_core_news_sm")
+nlp_es = spacy.load("es_core_news_sm")
+nlp_ko = spacy.load("ko_core_news_sm")
+nlp_ja = spacy.load("ja_core_news_sm")
+nlp_zh = spacy.load("zh_core_web_sm")
 
 nlp = spacy.load('en_core_web_sm')
 translator = Translator()
@@ -273,24 +286,29 @@ def split_verbs_nouns(text):
     doc = nlp(text)
     
     verbs_nouns = []
+    verbs_nouns_str = ""
     other_words = []
+    other_words_str = ""
     pos_string = []
 
     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)
+            verbs_nouns_str += token.text + " || "
+            other_words_str += "__ " 
+            #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)
-        pos_string.append(token.pos_)
+            other_words_str += token.text + " || "
+            #other_words.append(token.text)
+        #pos_string.append(token.pos_)
 
-    verbs_nouns_text = " ".join(verbs_nouns)
-    other_words_text = " ".join(other_words)
-    pos_string_text = " ".join(pos_string)
+    verbs_nouns_text = verbs_nouns_str  #" ".join(verbs_nouns)
+    other_words_text = other_words_str  #" ".join(other_words)
+    pos_string_text = "Debug Test" #" ".join(pos_string)
     
-    return pos_string_text, verbs_nouns_text, other_words_text
+    return other_words_text, pos_string_text, verbs_nouns_text
 
 SRTLangOptions = gr.Dropdown(choices=["en", "ja", "ko", "zh-cn"], value="en", label="choose the language of the srt")
 
@@ -440,7 +458,7 @@ def text_to_links(text): #TextCompFormat
     html = ""
     for line in lines:
         if line.startswith("http"):
-            html += f"<a href='{line}'> --  -- </a> | \n"
+            html += f"<a href='{line}'> --  -- </a> | "
         else:
             html += line + "Not a link <br> \n"
     return html
@@ -637,7 +655,7 @@ def UnknownTrackTexttoApp(text): #Copy of def OptimisedTtAppForUNWFWO(text):
     with open(HTMLDownloadTemp, 'w') as f:
         f.write(FinalOutput)
 
-    return FinalOutput, FinalOutput, HTMLDownloadTemp
+    return HTMLDownloadTemp, FinalOutput, FinalOutput
 
 #Kathryn Lingel - Pyambic Pentameter Example - PyCon US
 #Basic Language Model Code
@@ -776,9 +794,158 @@ def arrealtimetestidea(img):
 
 #------------------------------------------------------------------------------------------------------------------------------
 
+def ImageTranslationTest(text, video):
+
+    Finalvideo = video
+    return Finalvideo
+
+def AutoSyllablePractice(String):
+    FinalOutput = ""
+
+    stringlen = len(String)
+
+    vowels =["a", "e", "i", "o", "y"]
+    VowelSyllables = []
+    allvowels = ""
+
+    for i in vowels:
+        if i in String:
+            allvowels = allvowels + " " + String.replace(i, i + " ")
+            allvowels = allvowels + " " + String.replace(i, " " + i)
+    VowelSyllables = allvowels.split(" ")
+
+    VowelSyllablesstr = ""
+
+    for item in VowelSyllables:
+        VowelSyllablesstr += item + ", "
+
+    FinalOutput += VowelSyllablesstr        
+    return FinalOutput
+
+def GuidedReading(textspreprocess,seperator):
+  FinalOutput = ""
+  
+  if seperator == "Sentences": 
+    textspreprocess = textspreprocess.split(".")
+    FinalOutput = ""
+  elif seperator == "lines":
+      textspreprocess = textspreprocess.splitlines()
+  else: textspreprocess = textspreprocess.split(seperator)
+
+  # Load language-specific models
+  nlp_en = spacy.load("en_core_web_sm")
+  nlp_de = spacy.load("de_core_news_sm")
+  nlp_es = spacy.load("es_core_news_sm")
+  nlp_ko = spacy.load("ko_core_news_sm")
+  nlp_ja = spacy.load("ja_core_news_sm")
+  nlp_zh = spacy.load("zh_core_web_sm")
+
+  # Create a dictionary of language codes and models
+  nlp_dict = {"en": nlp_en, "de": nlp_de, "es": nlp_es, "ko": nlp_ko, "ja": nlp_ja, "zh-cn": nlp_zh}
+
+  # Define a function to POS tag and transliterate a text given its language code
+  def pos_tag_and_transliterate(text, lang):
+      # Get the model for the language
+      nlp = nlp_dict.get(lang)
+      if nlp is None:
+          return None # No model found for the language
+      # Process the text and get a list of (token, tag) tuples
+      doc = nlp(text)
+      original_pos_tags = [(token.text, token.pos_) for token in doc]
+      # Use different libraries for different languages
+      if lang == "ja":
+          # Use pykakasi for Japanese
+          from pykakasi import kakasi
+          # Set the modes using properties
+          k = kakasi()
+          k.hira2a = True # Hiragana to ascii
+          k.kata2a = True # Katakana to ascii
+          k.kanji2a = True # Kanji to ascii
+          k.roman = "Hepburn" # Use Hepburn romanization
+          #words = re.findall(r"\S+|\s+", text)
+          words = [token.text for token in doc]
+          # Create a dictionary that maps each original word to its transliterated form with spaces
+          translit_dict = {word: k.convert(word)[0]['hepburn'] for word in words}
+          # Get the transliterated text with spaces
+          transliterated = " ".join(translit_dict.values())
+          # Replace the words in the original POS tag list with their transliterated forms
+          translit_pos_tags = [(translit_dict.get(word, word), tag) for word, tag in original_pos_tags]
+          # Get the transliterated language code
+          lang_translit = lang + "-translit"
+      elif lang == "ko":
+          # Use hangul-romanize for Korean
+          from hangul_romanize import Transliter
+          from hangul_romanize.rule import academic
+          transliter = Transliter(academic)
+          # Create a dictionary that maps each original word to its transliterated form with spaces
+          words = [token.text for token in doc]
+          translit_dict = {word: " ".join(transliter.translit(word)) for word in words}
+          # Get the transliterated text with spaces
+          transliterated = " ".join(translit_dict.values())
+          # Replace the words in the original POS tag list with their transliterated forms
+          translit_pos_tags = [(translit_dict.get(word, word), tag) for word, tag in original_pos_tags]
+          # Get the transliterated language code
+          lang_translit = lang + "-translit"
+      elif lang == "zh-cn":
+          # Use pinyin for Chinese
+          from pinyin import get
+          # Get the transliterated text without spaces
+          transliterated = get(text)
+          # Replace the words in the original POS tag list with their transliterated forms
+          translit_pos_tags = [(get(word), tag) for word, tag in original_pos_tags]
+          # Get the transliterated language code
+          lang_translit = lang + "-translit"
+      else:
+          # No transliteration needed for other languages
+          return (text, original_pos_tags, text, original_pos_tags, lang)
+      # Return a tuple of the original text, the original POS tags, the transliterated text, the transliterated POS tags, and the transliterated language code
+      return (text, original_pos_tags, transliterated, translit_pos_tags, lang_translit)
+
+  # Create an empty list to store the results
+  texts = []
+
+  # Loop through each text in the list
+  for text in textspreprocess:
+      # Detect the language of the text
+      lang = detect(text)
+      # Add the text and the language as a tuple to the results list
+      texts.append((text, lang))
+
+  # Process each text in the texts list and print the results
+  for text, lang in texts:
+      result = pos_tag_and_transliterate(text, lang)
+      if result is not None:
+        FinalOutput += f"\nLanguage: {lang}"
+        FinalOutput += f"\nText: {result[0]}"
+        if lang in ["ja", "ko", "zh-cn"]:
+            FinalOutput += f"\nTransliterated Text: {result[2]}"
+        FinalOutput += f"\n POS tags: {result[1]}"
+        if lang in ["ja", "ko", "zh-cn"]:
+            FinalOutput += f"\nTPOS tags: {result[3]}"
+        FinalOutput += f"\n"
+  
+  return FinalOutput
+
+guidedreadingseperator = gr.Dropdown(choices=[".", ",", "lines", "Sentences"], value=".", interactive=True)
+textspreprocess = [
+    ["Bing created this example \nApple is looking at buying U.K. startup for $1 billion \nApple schaut sich für 1 Milliarde Dollar ein Startup aus Großbritannien an \nApple está mirando comprar una startup del Reino Unido por mil millones de dólares \n애플은 영국의 스타트업을 10억 달러에 사려고 한다 \nアップルは、英国のスタートアップを10億ドルで買収する予定だ \n苹果正考虑以10亿美元收购英国初创公司", "\n"]
+  ]
 
 LLPromptIdeas = """
 
+<p> Lets try this I will give you the english word and you find the <<chinese>> word and then mix the meaning of the word with the mnemonic <p/>
+
+Bings Explanation
+Youre welcome. Im happy to help. If I wanted to explain the rules of this game clearly and concisely, I would do something like this:
+
+The game is about finding the <<Chinese>> word for an English word and making a mnemonic that mixes the meaning of the word with the mnemonic.
+The game has two players: one who gives the English word and one who finds the <<Chinese>> word and makes the mnemonic.
+The player who gives the English word can choose any word they want, as long as it has a <<Chinese>> equivalent.
+The player who finds the <<Chinese>> word and makes the mnemonic can use any online or offline resources they want, such as dictionaries, translators, or search engines.
+The player who finds the <<Chinese>> word and makes the mnemonic should write their answer in the format: The <<Chinese>> word for X is Y <<(pinyin)>>, which means Z. Here are some possible mnemonics that mix the meaning of the word with the mnemonic: A, B, C, D. </p>
+
+<hr>
+
 <p> Make jokes while following rules for a syllogism jokes game:
 
 The game can be played with any number of people.
@@ -789,18 +956,40 @@ The person who makes the last valid syllogism wins the game. </p>
 
 <hr>
 
-<p> Lets try this I will give you the english word and you find the <<chinese>> word and then mix the meaning of the word with the mnemonic </>
+Do you know pydot?
+Please create code for a class diagragm using the pydot library in python for the following topic/entity 
 
-Bings Explanation
-Youre welcome. Im happy to help. If I wanted to explain the rules of this game clearly and concisely, I would do something like this:
+<hr>
+
+(System/First request) Your job is to lengthen Text sent to you in a meaningful way. You must create 20 paragraphs for each Text line sent by the user
+(User) Text: I went to the beach
+
+
+<hr>
+
+replace as many words with emojis in the sentence Life is very sweet <br>
+next sentence is AI Town is a virtual town where AI characters live, chat and socialize.
+
+<hr>
+
+<p> </p>
 
-The game is about finding the <<Chinese>> word for an English word and making a mnemonic that mixes the meaning of the word with the mnemonic.
-The game has two players: one who gives the English word and one who finds the <<Chinese>> word and makes the mnemonic.
-The player who gives the English word can choose any word they want, as long as it has a <<Chinese>> equivalent.
-The player who finds the <<Chinese>> word and makes the mnemonic can use any online or offline resources they want, such as dictionaries, translators, or search engines.
-The player who finds the <<Chinese>> word and makes the mnemonic should write their answer in the format: The <<Chinese>> word for X is Y <<(pinyin)>>, which means Z. Here are some possible mnemonics that mix the meaning of the word with the mnemonic: A, B, C, D. </p>
 """
 
+def display_website(link):
+  html = f"<iframe src='{link}' width='100%' height='1000px'></iframe>"
+  gr.Info("If 404 then the space/page has probably been disabled - normally due to a better alternative")
+  return html
+
+
+randomExposuremessageText = ["Great Test for LLM function calling (with Gradio Client)", "Unknown Tracker Tab = Incomplete Reading Assistant Idea - HTML app based on text to be read", "Bing mnemonic - lost = dont ignore unusual sounds here inside lost cave", "1000 verbs in lists of 100, verbs = easy setence structure estimation (SVO, SOV, etc.)", "Can put any message here in the navigatoin tab"]
+
+def randommarquee():
+    randomExposuremessagelistitem = ""
+    randomExposuremessagelistitem = str(random.sample(randomExposuremessageText, 1)).replace("['", "").replace("']", "")
+    #randomExposuremessagelistitem2 = str(random.sample(randomExposuremessageText, 1)).replace("['", "").replace("']", "")
+    return f" <marquee> { randomExposuremessagelistitem } </marquee> "
+
 
 # Define the Gradio interface inputs and outputs for video split
 spvvideo_file_input = gr.File(label='Video File')
@@ -814,32 +1003,53 @@ groupoutput_text = gr.Textbox(label="Grouped words")
 
 Translationchuncksize = gr.Number(value=4998)
 
-randomExposuremessagelistitem = str(random.sample(["Bing mnemonic - lost = dont ignore unusual sounds here inside lost cave", "1000 verbs in lists of 100, verbs = easy setence structure estimation (SVO, SOV, etc.)", "Can put any message here in the navigatoin tab"], 1)).replace("['", "").replace("']", "")
-randomExposuremessage = f" <marquee> { randomExposuremessagelistitem } </marquee> "
-
+randomExposuremessage = randommarquee()
+randomExposuremessage2 = randommarquee()
 
+VideoTestInput = gr.File(label="select a mp4 video file", file_types=[".mp4"])
 
 with gr.Blocks() as lliface: #theme=gr.themes.Glass(primary_hue='green', secondary_hue='red', neutral_hue='blue', )
-  PracticeExposure = gr.HTML(randomExposuremessage)
-  gr.HTML("Advanced Repitition = Combinatorics --> to understand a sentence properly you need understanding of every word --> in language that means use with other words --> Combos within the unique words in a sentence, paragraph, page, etc. --> as close to 3 word sentences")
-  gr.HTML("<p>Timing Practice - Repitition: Run from it, Dread it, Repitition is inevitable - Thanos --> Repitition of reaction - Foreign in eyes/ears native in mind (For beginners) | Repitition is a multitask activity like driving must be subconcious process to show mastery </p>")
-  gr.HTML(""" <a href='https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard'> -- Open LLM Leaderboard -- </a> | <a href='https://huggingface.co/spaces/sanchit-gandhi/whisper-jax'> -- Whisper JAX -- </a> | <a href="https://translate.google.com/?hl=en&tab=TT"> -- Google Translate -- </a> | <a href='https://huggingface.co/spaces/damo-vilab/modelscope-text-to-video-synthesis'> -- Modelscope Text to Video -- </a> | <a href='https://huggingface.co/spaces/stabilityai/stable-diffusion'> -- stable-diffusion 2 -- </a> | <a href='https://huggingface.co/spaces/stabilityai/stable-diffusion-1'> -- stable-diffusion 1 -- </a> | <a href='https://huggingface.co/spaces/kakaobrain/karlo'> -- karlo 1 -- </a> | <a href='https://huggingface.co/spaces/suno/bark'> -- Bark (TTS) -- </a> | <a href='https://chat.lmsys.org/'> -- Offline Text Model Demos -- </a> | <a href='https://huggingface.co/spaces/curt-park/segment-anything-with-clip'> -- SAM with Clip -- </a> | <a href='https://beta.elevenlabs.io/'> -- Eleven Labs -- </a> | <a href='https://www.d-id.com/'> -- Animate an Image -- </a> | <a href='https://voice.ai/'> -- Clone a voice -- </a> | <a href='https://openai.com/pricing'> -- OpenAI pricing -- </a> | <a href='https://huggingface.co/spaces/sohojoe/soho-clip-embeddings-explorer'> -- Image Training Data Search -- </a> | <a href='https://huggingface.co/spaces/huggingchat/chat-ui'> -- Huggingface Chat -- </a> | <a href='https://huggingface.co/spaces/bguisard/stable-diffusion-nano'> -- 128x128 Stable Diffusion (Fast) -- </a> | <a href='https://huggingface.co/spaces/colonelwatch/abstracts-index'> -- Search 95 million research abstracts -- </a> | <a href='https://huggingface.co/datasets/roneneldan/TinyStories'> -- Tiny Stories Dataset -- </a> | <a href='https://huggingface.co/spaces/lykeven/visualglm-6b'> -- Visualglm6b - Discuss images -- </a> | <a href='https://huggingface.co/spaces/xinyu1205/Recognize_Anything-Tag2Text'> -- RAM and Tag2Text -- </a> | <a href='https://huggingface.co/camenduru/potat1'> -- Potat1 Text2vid -- </a> | """)
+  gr.HTML('<div style="display: flex; justify-content: center; align-items: center; height: 100%;"> ---- Under Construction: Slowly figuring out what AI intergrated interface means (Chat vs Forms vs Function calling vs Sensor + Trigger) ----</div>')
+  with gr.Row():
+    PracticeExposure = gr.HTML(randomExposuremessage)
+    PracticeExposure2 = gr.HTML(randomExposuremessage2)
   with gr.Row():
     with gr.Column(scale=1):
-        with gr.Tab("Rep - Gradio"):
-            gr.HTML("""Gradio Version Below <iframe height="1200" style="width: 100%;" scrolling="no" title="Memorisation Aid" src="https://codepen.io/kwabs22/embed/GRXKQgj?default-tab=result&editable=true" frameborder="no" loading="lazy" allowtransparency="true" allowfullscreen="true">
+        gr.HTML("Advanced Repitition = Combinatorics --> to understand a sentence properly you need understanding of every word --> in language that means use with other words --> Combos within the unique words in a sentence, paragraph, page, etc. --> as close to 3 word sentences")
+    with gr.Column(scale=1):
+        gr.HTML("<p>Timing Practice - Repitition: Run from it, Dread it, Repitition is inevitable - Thanos --> Repitition of reaction - Foreign in eyes/ears native in mind (For beginners) | Repitition is a multitask activity like driving must be subconcious process to show mastery </p>")
+  gr.HTML(""" <a href='https://github.com/eugeneyan/open-llms'> -- Opensource List -- </a> | <a href='https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard'> -- Open LLM Leaderboard -- </a> | <a href='https://huggingface.co/spaces/sanchit-gandhi/whisper-jax'> -- Whisper JAX -- </a> | <a href="https://translate.google.com/?hl=en&tab=TT"> -- Google Translate -- </a> | <a href='https://huggingface.co/spaces/damo-vilab/modelscope-text-to-video-synthesis'> -- Modelscope Text to Video -- </a> | <a href='https://huggingface.co/spaces/stabilityai/stable-diffusion'> -- stable-diffusion 2 -- </a> | <a href='https://huggingface.co/spaces/stabilityai/stable-diffusion-1'> -- stable-diffusion 1 -- </a> | <a href='https://huggingface.co/spaces/kakaobrain/karlo'> -- karlo 1 -- </a> | <a href='https://huggingface.co/spaces/suno/bark'> -- Bark (TTS) -- </a> | <a href='https://chat.lmsys.org/'> -- Offline Text Model Demos -- </a> | <a href='https://huggingface.co/spaces/curt-park/segment-anything-with-clip'> -- SAM with Clip -- </a> | <a href='https://beta.elevenlabs.io/'> -- Eleven Labs -- </a> | <a href='https://www.d-id.com/'> -- Animate an Image -- </a> | <a href='https://voice.ai/'> -- Clone a voice -- </a> | <a href='https://openai.com/pricing'> -- OpenAI pricing -- </a> | <a href='https://huggingface.co/spaces/sohojoe/soho-clip-embeddings-explorer'> -- Image Training Data Search -- </a> | <a href='https://huggingface.co/spaces/huggingchat/chat-ui'> -- Huggingface Chat -- </a> | <a href='https://huggingface.co/spaces/bguisard/stable-diffusion-nano'> -- 128x128 Stable Diffusion (Fast) -- </a> | <a href='https://huggingface.co/spaces/colonelwatch/abstracts-index'> -- Search 95 million research abstracts -- </a> | <a href='https://huggingface.co/datasets/roneneldan/TinyStories'> -- Tiny Stories Dataset -- </a> | <a href='https://huggingface.co/spaces/lykeven/visualglm-6b'> -- Visualglm6b - Discuss images -- </a> | <a href='https://huggingface.co/spaces/xinyu1205/Recognize_Anything-Tag2Text'> -- RAM and Tag2Text -- </a> | <a href='https://huggingface.co/camenduru/potat1'> -- Potat1 Text2vid -- </a> | <a href='https://twitter.com/willdepue/status/1661781355452325889'> -- Alexandria Prohect (Will Deque) - Free Embeddings -- </a> |  <a href='https://artsandculture.google.com/'> -- Google Arts and Culture Portal -- </a> | <a href='https://huggingface.co/spaces/Matthijs/whisper_word_timestamps'> -- Word Level Timestamps  -- </a> | <a href='https://huggingface.co/spaces/zaanind/NLLB-translation'> -- NLLB 600M Demo -- </a> | <a href='https://huggingface.co/spaces/hysts/zeroscope-v2'> -- Zeroscope v2 Text to video -- </a> | <a href='https://huggingface.co/spaces/SpacesExamples/ComfyUI'> -- ComfyUI Text to Image -- </a> | <a href='https://huggingface.co/spaces/DeepFloyd/IF'> -- Deepfloyd IF - Text in image -- </a> | <a href='https://huggingface.co/spaces/ysharma/ChatGPT-Plugins-in-Gradio'> -- ChatGPT Custom Plugins Test Space -- </a> | <a href='https://www.reddit.com/r/LocalLLaMA/'> -- r/LocalLlama -- </a> | <a href='https://www.reddit.com/r/singularity/'> -- r/Singularity -- </a> | <a href='https://huggingface.co/spaces/hysts/SD-XL'> -- SD-XL Test Space -- </a> | <a href='https://huggingface.co/spaces/facebook/seamless_m4t'> -- Seamless M4T - Translation one stop shop -- </a> | <a href='https://huggingface.co/spaces/codellama/codellama-playground'> -- Code Llama playground -- </a> | <a href='https://huggingface.co/spaces/Voicemod/Text-to-Sing'> -- Text to sing -- </a> | <a href='https://huggingface.co/spaces/camenduru-com/webui'> -- Stable Diffusion Webui (Camenduru Space) -- </a> | <a href='https://huggingface.co/spaces/ysharma/WizardCoder34b'> -- Wizard Coder 34B -- </a> | <a href='https://huggingface.co/spaces/chansung/co-write-with-llama2'> -- Cowrite with llama2 -- </a> | <a href='https://huggingface.co/spaces/fffiloni/Image-to-Story'> -- Image to Story -- </a> | <a href='https://huggingface.co/spaces/fffiloni/CLIP-Interrogator-2'> -- Clip interrogator 2 -- </a> | <a href='https://github.com/THUDM/AgentBench'> -- Agent Benchmarks -- </a> | <a href='https://www.convex.dev/ai-town'> -- AI Town Live Demo -- </a> = <a href='https://github.com/a16z-infra/ai-town'> -- AI Town Repository (Deployment]) -- </a> | <a href='https://github.com/joonspk-research/generative_agents/tree/main'> -- Generative Agents: Interactive Simulacra of Human Behavior (Research paper Repository) -- </a> | """)
+  with gr.Accordion("LLM HF Spaces (Click Here to Open) - Ask for Translation of image tags made below, sentence to emojis, Wordlists, Test Conversations, Get Grammar Explanations etc., Can use GPT-4 or new SOTA to review the conversation", open=False):
+    with gr.Row():
+        linktochat = gr.Dropdown(choices=["https://sdk.vercel.ai/docs", "https://lmsys-chatbot-arena.hf.space", "https://huggingface-projects-llama-2-7b-chat.hf.space", "https://ysharma-explore-llamav2-with-tgi.hf.space", "https://mosaicml-mpt-30b-chat.hf.space", "https://huggingfaceh4-falcon-chat.hf.space", "https://uwnlp-guanaco-playground-tgi.hf.space", "https://stabilityai-stablelm-tuned-alpha-chat.hf.space", "https://mosaicml-mpt-7b-storywriter.hf.space", "https://huggingfaceh4-starchat-playground.hf.space", "https://bigcode-bigcode-playground.hf.space", "https://mosaicml-mpt-7b-chat.hf.space", "https://huggingchat-chat-ui.hf.space", "https://togethercomputer-openchatkit.hf.space"], label="Choose/Cancel type any .hf.space link here (can also type a link)'", allow_custom_value=True)
+        chatspacebtn = gr.Button("Use the chosen URL to load interface with a chat model. For sdk.vercel click the chat button on the top left")
+    with gr.Row():
+        chatspace = gr.HTML("Chat Space Chosen will load here")
+        chatspacebtn.click(display_website, inputs=linktochat, outputs=chatspace)
+  with gr.Row():
+    with gr.Column(scale=1):
+        with gr.Tab("Rep - HTML"):
+            gr.HTML("UNNWFWO = Unknown Native Word Foreign Word Order i.e. during active listening practice you only need the words you dont know")
+            gr.HTML("""<iframe height="1200" style="width: 100%;" scrolling="no" title="Memorisation Aid" src="https://codepen.io/kwabs22/embed/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("Rep - Gradio"):
              gr.Interface(fn=group_words, inputs=groupinput_text, outputs=groupoutput_text, description="Word Grouping and Rotation - Group a list of words into sets of 10 and rotate them every 60 seconds.") #.queue() 
         with gr.Tab("Navigation"):
             gr.HTML("Picture Annotation <br> Chorus Focused Word List <br> Merged Subtitles <br> Repetitive Audio (TTS) <br> Word and Sentence Jumbling <br> Unkown: Wordnet <br> Unknown: Wikipeadia <br>")
-            PracticeExposureInput = gr.Textbox(placeholer="Exposure practice = look up", label="Exposure at the top")
+            PracticeExposureInput = gr.Textbox("", placeholder="Exposure practice = look up", label="Exposure at the top")
             PracticeExposurebtn = gr.Button("Change Default")
             PracticeExposurebtn.click(fn=changeexposuretext, inputs=PracticeExposureInput, outputs=PracticeExposure)
+        with gr.Tab("Words Lists"):
+            gr.HTML("Stop, Sight(Dolch) and other Wordlists")
+            gr.HTML("Wikipeadia <br> Basic: <a href='https://en.wikipedia.org/wiki/Dolch_word_list'> -- Dolch (Sight) Words -- </a> |  <br> Advanced: <a href='https://en.wikipedia.org/wiki/Blend_word'> -- Blend Word -- </a> | <a href='https://en.wikipedia.org/wiki/List_of_portmanteaus'> -- List_of_portmanteaus -- </a> | ")
+            gr.HTML("Reddit <br> <a href='https://www.reddit.com/r/wordplay/'> -- Wordplay -- </a> | ")
+            gr.HTML("Language Tests <br> ")
+            gr.HTML("Other <br> <a href='https://english.stackexchange.com/'> -- English (StackExchange) -- </a> | <a href='https://english.stackexchange.com/questions/572884/what-is-it-called-when-two-words-are-combined-by-overlapping-each-other'> -- Overlapping Blends (StackExchange) -- </a> |   ")
         with gr.Tab("Vector Database = Memorisation"):
-            gr.HTML("Open AI - 2500000 character text = <1$ (0.0004 per 1000 tokens), Cohere Multilingual = free for personal use / Commercial use =    \n Vector Database query = Better than text search but not for logical relationships")           
+            gr.HTML("Phrasebook on demand in realtime <br><br> Open AI - 10000 * 1000tokens (+- 4000 characters) = 1$ (0.0001 per 1000 tokens / 750 words), Cohere Multilingual = free for personal use / Commercial use =    \n Vector Database query = Better than text search but not for logical relationships")           
     with gr.Column(scale=3): 
         with gr.Tab("Beginner - Listen + Read"):
+            gr.Label("1 new word a minute while recycling the words from the previous minutes")
             with gr.Row():
                 with gr.Column(scale=1):
                     gr.HTML("Listening - Songs - Chorus <br> Anticipation of the item to remember is how you learn lyrics that is why songs are easy as if you heard it 10 times already your capacity to anticipate the words is great <br><br> This is where TTS helps as you are ignoring all words except the words just before the actual <br> <b>Tiny Stories dataset is like a graded reader</b> <br>")
@@ -848,11 +1058,24 @@ with gr.Blocks() as lliface: #theme=gr.themes.Glass(primary_hue='green', seconda
                     gr.Interface(fn=AutoChorusInvestigator, inputs="text", outputs="text", description="Paste Full Lyrics to try find only chorus lines")
                     gr.Interface(fn=AutoChorusPerWordScheduler, inputs="text", outputs="text", description="Create order of repitition for tts practice")
                 with gr.Column(scale=1):
-                    gr.HTML("""Reading - Caption images (SD/Dalle-E) <br> <a href='https://unsplash.com/'> -- Unsplash - free images -- </a> | <a href="https://huggingface.co/spaces/pharma/CLIP-Interrogator"> --Huggingface CLIP-Interrogator Space-- </a> | <a href='https://huggingface.co/spaces/xinyu1205/Recognize_Anything-Tag2Text'> -- Tag2Text is faster than clip -- </a> | <br> <a href='https://huggingface.co/spaces/bkhmsi/Word-To-Image'> -- Transform word to an image -- </a> | <a href='https://huggingface.co/spaces/microsoft/Promptist'> -- Promptist (Microsoft) -- </a> | """)
-                    gr.Interface(fn=add_text_to_image , inputs=["image", "text"], outputs="image", description="Create Annotated images (Can create using stable diffusion and use the prompt) - Describe from one side to the other to make guessing easy")
+                    gr.HTML("""Reading - Caption images (SD/Dalle-E) <br> <a href='https://unsplash.com/'> -- Unsplash - free images -- </a> | <a href="https://huggingface.co/spaces/pharma/CLIP-Interrogator"> --Huggingface CLIP-Interrogator Space-- </a> | <a href='https://huggingface.co/spaces/fffiloni/CLIP-Interrogator-2'> -- Clip interrogator 2 -- </a> | <a href='https://huggingface.co/spaces/xinyu1205/Recognize_Anything-Tag2Text'> -- Tag2Text is faster than clip -- </a> | <br> <a href='https://huggingface.co/spaces/bkhmsi/Word-To-Image'> -- Transform word to an image -- </a> | <a href='https://huggingface.co/spaces/microsoft/Promptist'> -- Promptist (Microsoft) -- </a> | <a href='https://huggingface.co/spaces/xinyu1205/Recognize_Anything-Tag2Text'> -- RAM and Tag2Text -- </a> | <a href='https://huggingface.co/spaces/curt-park/segment-anything-with-clip'> -- SAM with Clip -- </a> """)
+                    with gr.Accordion("RAM/Tag2Text Space - Create Tags here and Copy paste", open=False):
+                        RAMSpaceLink = gr.Textbox("https://xinyu1205-recognize-anything.hf.space")
+                        RAMSpacetest = gr.HTML("")
+                        RAMSpacetestbtn = gr.Button('Load Space')
+                        RAMSpacetestbtn.click(display_website, RAMSpaceLink, RAMSpacetest)
+                    with gr.Accordion("SAM Space Test", open=False):
+                        SAMSpaceLink = gr.Textbox("https://curt-park-segment-anything-with-clip.hf.space")
+                        SAMSpacetest = gr.HTML("")
+                        SAMSpacetestbtn = gr.Button('Load Space')
+                        SAMSpacetestbtn.click(display_website, SAMSpaceLink, SAMSpacetest)
                     gr.HTML("Use Shift Enter To put text on new lines if the text doesnt fit <br> if theres an error you have to remove the foreign letters and place roman ones")
+                    gr.Interface(fn=add_text_to_image , inputs=["image", "text"], outputs="image", description="Create Annotated images (Can create using stable diffusion and use the prompt) - Describe from one side to the other to make guessing easy")
         #with gr.Tab("Transcribe - RASMUS Whisper"):
             #gr.Interface.load("spaces/RASMUS/Whisper-youtube-crosslingual-subtitles", title="Subtitles")
+        with gr.Tab("Beginner - Vague Language"):
+            gr.HTML("Some Vague Words - Quantifiers, Pronouns, etc. <br/> <br/> Very, Many, Few, Lots,   <hr> Find Replace all nouns with something/someone or and for verbs figure out how to generalise them")
+
         with gr.Tab("Advanced - LingQ Addon Ideas"):
             gr.HTML("<a href='https://www.lingq.com/en/'>Find LingQ Here --> https://www.lingq.com/en/</a>")
             with gr.Tab("Visual - Multiline Custom Video Subtitles"):
@@ -890,13 +1113,16 @@ with gr.Blocks() as lliface: #theme=gr.themes.Glass(primary_hue='green', seconda
                 with gr.Tab("Split video to segments"):
                     gr.HTML("<a href='https://www.vlchelp.com/automated-screenshots-interval/'>How to make screenshot in vlc - https://www.vlchelp.com/automated-screenshots-interval/</a><br>")
                     gr.Interface(VideotoSegment, inputs=[spvvideo_file_input, spvsubtitle_file_input], outputs=spvdownload_output)
+                    gr.TextArea("Placeholder for ffmpeg command generator and ffmpeg-python code to split video")
                 gr.Text("Text to Closed Class + Adjectives + Punctuation or Noun Verb + Punctuation ")
             with gr.Tab("Audio - Only English thoughts as practice"):
                 gr.HTML("For Audio Most productive is real time recall of native (where your full reasoning ability will always be) <br><hr> Find Replace new lines of the foreign text with full stops or | to get per word translation")
                 gr.Interface(fn=TTSforListeningPractice, inputs=["text", TTSLangOptions2], outputs="audio", description="Paste only english words in foreign order and then keep removing the words from this to practice as effectively")   
-        with gr.Tab("Transition is the end goal"):
+        with gr.Tab("Transition is the end goal (SOV, SVO, VSO)"):
+            gr.Interface(fn=FrontRevSentChunk, inputs=[ChunkModeDrop, "checkbox", "text", langdest], outputs="text", description="Chunks creator")
             with gr.Row():
                 with gr.Column():
+                    gr.Interface(fn=AutoSyllablePractice, inputs="text", outputs="text", description="One Word At A Time | Audio Spelling Practice Using vowels as the seperator")
                     gr.Textbox("A word is a list of letter as a fact is a list of words. Both are in a specific order. What is most important is practice the order so randomiser is the tool", lines=4)
                     gr.Interface(fn=RandomiseTextbyType, inputs=["text", RandomiseTextType], outputs="text", description="Randomise order within words, sentences, paragrahs")
                 with gr.Column():
@@ -910,20 +1136,25 @@ with gr.Blocks() as lliface: #theme=gr.themes.Glass(primary_hue='green', seconda
                     gr.HTML("<p> Spell multiple words simultaneously for simultaneous access </p> <p> Spelling Simplification - Use a dual language list? | Spelling is the end goal, you already know many letter orders called words so you need leverage them to remember random sequences")
                     gr.Interface(fn=create_dictionary, inputs="text", outputs="text", title="Sort Text by first two letters")
                     gr.Interface(fn=keep_nouns_verbs, inputs=["text"], outputs="text", description="Noun and Verbs only (Plus punctuation)")
-                    gr.Interface(fn=FrontRevSentChunk, inputs=[ChunkModeDrop, "checkbox", "text", langdest], outputs="text", description="Chunks creator")
         with gr.Tab("Unknown Tracker"):
             gr.HTML("Repitition of things you know is a waste of time when theres stuff you dont know <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> Vocab = Glossary + all non text wall(lists, diagrams, etc.)</p>")
             gr.Textbox("Placeholder for a function that creates a set list and can takes a list for known words and auto find replaces the stuff you know out of the content")
             gr.Textbox("Place holder for a translate to english interface so that highlighting can still work as only english supported for now")
-            gr.Interface(fn=UnknownTrackTexttoApp, inputs="text", outputs=["html", "text", "file"], description="Use the text from here to create lists you use for the TTS section")
+            gr.Interface(fn=UnknownTrackTexttoApp, inputs="text", outputs=["file", "html", "text"], description="HTML mini App - UNNWFWO (To track verbs you dont know for listening practice). Use the text from here to create lists you use for the TTS section")
             with gr.Tab("Unique word ID - use in Infranodus"):
+                with gr.Accordion(label="Infranodus", open=False):
+                    gr.HTML(" <a href='https://infranodus.com/'> -- Infranodus - Word Level Knowledge graphs -- </a> | <br> Use the below interfaces to find the items that dont have entries --> These will represent new concepts or people which need to be understood individually to fully understand the text --> Infranodus search will help find related and unrelated investigation paths <br><br> TODO Figure Output Zoom / Image Dimensions")
+                    gr.Image(source="upload", label="Open Infranodus Screenshot")
+                    gr.Image(source="upload", label="Open Infranodus Screenshot")
                 gr.Interface(fn=unique_word_count, inputs="text", outputs="text", description="Wordcounter")
                 gr.Interface(fn=SepHypandSynExpansion, inputs="text", outputs=["text", "text"], description="Word suggestions - Analyse the unique words in infranodus")
                 gr.Interface(fn=WikiSearch, inputs="text", outputs="text", description="One word at a time 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("Thinking Practice"):      
-            with gr.Tab("Sentence to Format"):
+                gr.HTML("Questions - Taking and suggesting questions to ask = new education --> Esp. Infranodus type outer discourse identification as question generation")
+                gr.HTML("The point of reading is to refine future actions especially problem solving --> Creating problem scenarios = thinking ahead of time = One form of effective reading")          
+        with gr.Tab("Thinking Practice (POS)"):
+            gr.HTML("By removing all nouns and verbs you get a format to practice thinking about your words to use to make sentences which make sense within constraints")      
+            with gr.Tab("Sentence to Practice Format"):
                 gr.Interface(fn=split_verbs_nouns , inputs="text", outputs=["text", "text", "text"], description="Comprehension reading and Sentence Format Creator")
         with gr.Tab("Knowledge Ideas - Notetaking"):
             gr.HTML("""<p>Good knowledge = ability to answer questions --> find Questions you cant answer and look for hidden answer within them </p>
@@ -932,18 +1163,25 @@ with gr.Blocks() as lliface: #theme=gr.themes.Glass(primary_hue='green', seconda
             <p>Application of Knowledge = App Version of the text (eg. Jupyter Notebooks) is what you create and learn first</p>
             """)
             gr.Interface(fn=TextCompFormat, inputs=["textarea", HTMLCompMode], outputs="text", description="Convert Text to HTML Dropdown or Links which you paste in any html file")    
-            gr.Interface(fn=create_collapsiblebutton, inputs=["textbox", "textbox", "textarea"], outputs="textarea", description="Button and Div HTML Generator, Generate the HTML for a button and the corresponding div element.")
+            gr.Interface(fn=create_collapsiblebutton, inputs=["textbox", "textbox", "textarea"], outputs="textbox", description="Button and Div HTML Generator, Generate the HTML for a button and the corresponding div element.")
         with gr.Tab("Automated Reading Assitant"):
+            gr.Interface(fn=GuidedReading, inputs=["text", guidedreadingseperator], outputs="text", description="POS Tag and Transliteration", examples=textspreprocess)
             gr.Textbox('Parts of Speech based | Automating the Notetaking Tab either directly or using visual llm to use this interface efficiently')
             gr.HTML("Types of comprehension agent <hr> Speed of Comprehension = Verb comprehension <br> From the following please extract the verbs <br> now explain each in context <br> Next, use picture descriptions for each word in the verb list <br> Create combinations using the verb list <hr> ")
+            gr.HTML("How VERBS RELATE TO EACH OTHER --> Shared Nodes - what other verbs are connected to the noun in a INFRANODUS With POS Tag filters")
             gr.HTML("Tree and Branches approach to learning = familiarity with keywords/headings/summaries before reading the whole text <hr> Productivity/Work revolves around repitition which can be found looking for plurals and grouping terms eg. Headings and Hyper/Hyponyms Analysis")        
-        with gr.Tab("AR"):
+            gr.HTML("Sentence to PyDot graph")
+            gr.HTML("Currently a bug that locks all buttons in the space when you use this above example - Reload to fix")
+        with gr.Tab("Video/AR"):
+            gr.HTML("HUD Experiment - so far sending the frame directly to a html frame --> that only solves image but the audio will be skipping so how do we solve that")
+            gr.Interface(fn=ImageTranslationTest , inputs=["text", VideoTestInput], outputs="video")
+            gr.HTML("Nicolai Nielsen Youtube channel - aruco markers = position --> can test using premade ones from an image search")
             gr.Textbox("Alpha Test version = Real time Lablling of All things in view using SAM and Clip Interrogator and OpenCV on pydroid --> Adjusted Demo")
             gr.HTML("Some Prompt ideas --> Prompt: Describe the place where these descriptions may be (You job is to be speculative for brainstorming purposes): A dog and a boy, the area is texas, the weather is sunny, the date is 01 May 2021 <hr> Prompt Content Ideas Ideas Clip Interrogator + Location Data aka tags for place, location and time + general news updates on the location + overview of the items in the location  <br> Location based advise is most important but after that is information observed by appliances in the location eg. Times Computer turned on, times geyser inspected, amount of time keys havent been touched etc. <br> each location will have an ai personality that will relay more information ")
-            gr.HTML("<a href='https://huggingface.co/spaces/curt-park/segment-anything-with-clip'> -- SAM with Clip -- </a>")
+            gr.HTML("<a href='https://huggingface.co/spaces/xinyu1205/Recognize_Anything-Tag2Text'> -- RAM and Tag2Text -- </a> | <a href='https://huggingface.co/spaces/curt-park/segment-anything-with-clip'> -- SAM with Clip -- </a>")
             gr.Interface(fn=arrealtimetestidea, inputs='image', outputs="text", description="Vision Assistant - see and execute")
             gr.Textbox("Placeholder for webcam stream")
-            #gr.Interface(fn=arrealtimetestidea, inputs='webcam', outputs="text", description="Vision Assistant aka Free Observation llm judgement (GPT Vision API goes here when released). FPS is the difference between realtime app and static image")
+            gr.Interface(fn=arrealtimetestidea, inputs="text", outputs="text", description="Vision Assistant aka Free Observation llm judgement (GPT Vision API goes here when released). FPS is the difference between realtime app and static image")
         with gr.Tab("Random Ideas"):
             gr.HTML("""<p>Spaces Test - Still Undercontruction   --> Next Milestone is Turning this interface handsfree | Knowledge is a Language but productive knowledge is find replace as well | LingQ is good option for per word state management</p> <p> Arrows app json creator for easy knowledge graphing and spacy POS graph? --> Questions? -->  
             <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> """)
@@ -951,6 +1189,11 @@ with gr.Blocks() as lliface: #theme=gr.themes.Glass(primary_hue='green', seconda
             gr.HTML("Predictable to identify the parts of picture being described --> The description moves in one direction from one side of the image to the other side is easiest <hr>")
             gr.HTML("Image = instant comprehension like Stable Diffusion --> Audiovisual experience is the most optimal reading experience <br> Manga with summary descriptions for the chapters = Most aligned visual to audio experience")
         with gr.Tab("LLM Prompts and games"):
-            gr.HTML(LLPromptIdeas)
+            gr.HTML("TODO = Llama-cpp-python with falcon 7b / openllama 7b intergrated into each of the interfaces in this space aka --> interfaces as tools for open source llm <br><br> <b>Test using gradio space/interfaces through the api as function calls for gpt3.5 and 4</b>")
+            with gr.Accordion("Some prompt ideas"):
+                gr.HTML(LLPromptIdeas)
+        with gr.Tab("Gradio Client Tests"):
+            gr.HTML("How to return componets here in gradio (as each client interface needs different inputs) like in react")
+
             
 lliface.queue().launch() #(inbrowser="true")