clickable links updated

app.py

@@ -1,792 +1,792 @@
-# 0- libraries
-import transformers
-import gradio as gr
-
-from youtube_transcript_api import YouTubeTranscriptApi
-from huggingface_hub import InferenceClient
-from pytube import YouTube
-import pytube
-import torch
-
-# 1 - abstractive_summary
-# 1.1 - initialize
-import os
-save_dir = os.path.join(os.getcwd(), "docs")
-if not os.path.exists(save_dir):
-    os.mkdir(save_dir)
-transcription_model_id = "openai/whisper-large"
-llm_model_id = "tiiuae/falcon-7b-instruct"
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-
-# 1.2 - transcription
-def get_yt_transcript(url):
-    text = ""
-    vid_id = pytube.extract.video_id(url)
-    temp = YouTubeTranscriptApi.get_transcript(vid_id)
-    for t in temp:
-        text += t["text"] + " "
-    return text
-
-# 1.2.1 - locally_transcribe
-def transcribe_yt_vid(url):
-    # download YouTube video's audio
-    yt = YouTube(str(url))
-    audio = yt.streams.filter(only_audio=True).first()
-    out_file = audio.download(filename="audio.mp3", output_path=save_dir)
-
-    # defining an automatic-speech-recognition pipeline
-    asr = transformers.pipeline(
-        "automatic-speech-recognition",
-        model=transcription_model_id,
-        device_map="auto",
-    )
-
-    # setting model config parameters
-    asr.model.config.forced_decoder_ids = asr.tokenizer.get_decoder_prompt_ids(
-        language="en", task="transcribe"
-    )
-
-    # invoking the Whisper model
-    temp = asr(out_file, chunk_length_s=20)
-    text = temp["text"]
-
-    # we can do this at the end to release GPU memory
-    del asr
-    torch.cuda.empty_cache()
-
-    return text
-
-# 1.2.1 - api_transcribe
-def transcribe_yt_vid_api(url, api_token):
-    # download YouTube video's audio
-    yt = YouTube(str(url))
-    audio = yt.streams.filter(only_audio=True).first()
-    out_file = audio.download(filename="audio.wav", output_path=save_dir)
-
-    # Initialize client for the Whisper model
-    client = InferenceClient(model=transcription_model_id, token=api_token)
-
-    import librosa
-    import soundfile as sf
-
-    text = ""
-    t = 25  # audio chunk length in seconds
-    x, sr = librosa.load(out_file, sr=None)
-    # This gives x as audio file in numpy array and sr as original sampling rate
-    # The audio needs to be split in 20 second chunks since the API call truncates the response
-    for _, i in enumerate(range(0, (len(x) // (t * sr)) + 1)):
-        y = x[t * sr * i : t * sr * (i + 1)]
-        split_path = os.path.join(save_dir, "audio_split.wav")
-        sf.write(split_path, y, sr)
-        text += client.automatic_speech_recognition(split_path)
-
-    return text
-
-
-# 1.2.3 - transcribe locally or api
-def transcribe_youtube_video(url, force_transcribe=False, use_api=False, api_token=None):
-
-    yt = YouTube(str(url))
-    text = ""
-    # get the transcript from YouTube if available
-    try:
-        text = get_yt_transcript(url)
-    except:
-        pass
-
-    # transcribes the video if YouTube did not provide a transcription
-    # or if you want to force_transcribe anyway
-    if text == "" or force_transcribe:
-        if use_api:
-            text = transcribe_yt_vid_api(url, api_token=api_token)
-            transcript_source = "The transcript was generated using {} via the Hugging Face Hub API.".format(
-                transcription_model_id
-            )
-        else:
-            text = transcribe_yt_vid(url)
-            transcript_source = (
-                "The transcript was generated using {} hosted locally.".format(
-                    transcription_model_id
-                )
-            )
-    else:
-        transcript_source = "The transcript was downloaded from YouTube."
-
-    return yt.title, text, transcript_source
-
-
-# 1.3 - turn to paragraph or points
-def turn_to_paragraph(text):
-    # REMOVE HTML TAGS
-    from bs4 import BeautifulSoup
-
-    # Parse the HTML text
-    soup = BeautifulSoup(text, "html.parser")
-    # Get the text without HTML tags
-    text = soup.get_text()  # print(text_without_tags)
-
-    # Remove leading and trailing whitespaces
-    text = text.strip()
-    # Check if the string ends with "User" and remove it
-    if text.endswith("User"):
-        text = text[: -len("User")]
-    # Replace dashes and extra whitespaces with spaces
-    text = (
-        text.replace(" -", "")
-        .replace("  ", "")
-        .replace("\n", " ")
-        .replace("- ", "")
-        .replace("`", "")
-    )
-    # text = text.replace("  ", "\n\n") # to keep second paragraph if it exists # sometime it's good to turn this on. but let's keep it off
-    text = text.replace("  ", " ")  # off this if ^ is on
-
-    return text
-
-
-# 1.3.1
-def turn_to_points(text):  # input must be from `turn_to_paragraph()`
-    # text = text.replace(". ", ".\n-") # to keep second paragraph if it exists
-    text_with_dashes = ".\n".join("- " + line.strip() for line in text.split(". "))
-    text_with_dashes = text_with_dashes.replace("\n\n", "\n\n- ")  # for first sentence of new paragraph
-    return text_with_dashes
-
-# 1.3.2 - combined funtions above for paragraph_or_points
-def paragraph_or_points(text, pa_or_po):
-    if pa_or_po == "Points":
-        return turn_to_points(turn_to_paragraph(text))
-    else:  # default is Paragraph
-        return turn_to_paragraph(text)
-
-# 1.4 - summarization
-def summarize_text(title, text, temperature, words, use_api=False, api_token=None, do_sample=False, length="Short", pa_or_po="Paragraph",):
-
-    from langchain.chains.llm import LLMChain
-    from langchain.prompts import PromptTemplate
-    from langchain.chains import ReduceDocumentsChain, MapReduceDocumentsChain
-    from langchain.chains.combine_documents.stuff import StuffDocumentsChain
-    import torch
-    import transformers
-    from transformers import BitsAndBytesConfig
-    from transformers import AutoTokenizer, AutoModelForCausalLM
-
-    from langchain import HuggingFacePipeline
-    import torch
-
-    model_kwargs1 = {
-        "temperature": temperature,
-        "do_sample": do_sample,
-        "min_new_tokens": 200 - 25,
-        "max_new_tokens": 200 + 25,
-        "repetition_penalty": 20.0,
-    }
-    model_kwargs2 = {
-        "temperature": temperature,
-        "do_sample": do_sample,
-        "min_new_tokens": words,
-        "max_new_tokens": words + 100,
-        "repetition_penalty": 20.0,
-    }
-    if not do_sample:
-        del model_kwargs1["temperature"]
-        del model_kwargs2["temperature"]
-
-    if use_api:
-
-        from langchain import HuggingFaceHub
-
-        # os.environ["HUGGINGFACEHUB_API_TOKEN"] = api_token
-        llm = HuggingFaceHub(
-            repo_id=llm_model_id,
-            model_kwargs=model_kwargs1,
-            huggingfacehub_api_token=api_token,
-        )
-        llm2 = HuggingFaceHub(
-            repo_id=llm_model_id,
-            model_kwargs=model_kwargs2,
-            huggingfacehub_api_token=api_token,
-        )
-        summary_source = (
-            "The summary was generated using {} via Hugging Face API.".format(
-                llm_model_id
-            )
-        )
-
-    else:
-        quantization_config = BitsAndBytesConfig(
-            load_in_4bit=True,
-            bnb_4bit_compute_dtype=torch.float16,
-            bnb_4bit_quant_type="nf4",
-            bnb_4bit_use_double_quant=True,
-        )
-
-        tokenizer = AutoTokenizer.from_pretrained(llm_model_id)
-        model = AutoModelForCausalLM.from_pretrained(
-            llm_model_id,
-            # quantization_config=quantization_config
-        )
-        model.to_bettertransformer()
-
-        pipeline = transformers.pipeline(
-            "text-generation",
-            model=model,
-            tokenizer=tokenizer,
-            torch_dtype=torch.bfloat16,
-            device_map="auto",
-            pad_token_id=tokenizer.eos_token_id,
-            **model_kwargs1,
-        )
-        pipeline2 = transformers.pipeline(
-            "text-generation",
-            model=model,
-            tokenizer=tokenizer,
-            torch_dtype=torch.bfloat16,
-            device_map="auto",
-            pad_token_id=tokenizer.eos_token_id,
-            **model_kwargs2,
-        )
-        llm = HuggingFacePipeline(pipeline=pipeline)
-        llm2 = HuggingFacePipeline(pipeline=pipeline2)
-
-        summary_source = "The summary was generated using {} hosted locally.".format(
-            llm_model_id
-        )
-
-    # Map
-    map_template = """
-    Summarize the following video in a clear way:\n
-    ----------------------- \n
-    TITLE: `{title}`\n
-    TEXT:\n
-    `{docs}`\n
-    ----------------------- \n
-    SUMMARY:\n
-    """
-    map_prompt = PromptTemplate(
-        template=map_template, input_variables=["title", "docs"]
-    )
-    map_chain = LLMChain(llm=llm, prompt=map_prompt)
-
-    # Reduce - Collapse
-    collapse_template = """
-    TITLE: `{title}`\n
-    TEXT:\n
-    `{doc_summaries}`\n
-    ----------------------- \n
-    Turn the text of a video above into a long essay:\n
-    """
-
-    collapse_prompt = PromptTemplate(
-        template=collapse_template, input_variables=["title", "doc_summaries"]
-    )
-    collapse_chain = LLMChain(llm=llm, prompt=collapse_prompt)  # LLM 1 <-- LLM
-
-    # Takes a list of documents, combines them into a single string, and passes this to an LLMChain
-    collapse_documents_chain = StuffDocumentsChain(
-        llm_chain=collapse_chain, document_variable_name="doc_summaries"
-    )
-
-    # Final Reduce - Combine
-    combine_template_short = """\n
-    TITLE: `{title}`\n
-    TEXT:\n
-    `{doc_summaries}`\n
-    ----------------------- \n
-    Turn the text of a video above into a 3-sentence summary:\n
-    """
-    combine_template_medium = """\n
-    TITLE: `{title}`\n
-    TEXT:\n
-    `{doc_summaries}`\n
-    ----------------------- \n
-    Turn the text of a video above into a long summary:\n
-    """
-    combine_template_long = """\n
-    TITLE: `{title}`\n
-    TEXT:\n
-    `{doc_summaries}`\n
-    ----------------------- \n
-    Turn the text of a video above into a long essay:\n
-    """
-    # Turn the text of a video above into a 3-sentence summary:\n
-    # Turn the text of a video above into a long summary:\n
-    # Turn the text of a video above into a long essay:\n
-    if length == "Medium":
-        combine_prompt = PromptTemplate(
-            template=combine_template_medium,
-            input_variables=["title", "doc_summaries", "words"],
-        )
-    elif length == "Long":
-        combine_prompt = PromptTemplate(
-            template=combine_template_long,
-            input_variables=["title", "doc_summaries", "words"],
-        )
-    else:  # default is short
-        combine_prompt = PromptTemplate(
-            template=combine_template_short,
-            input_variables=["title", "doc_summaries", "words"],
-        )
-    combine_chain = LLMChain(llm=llm2, prompt=combine_prompt)  # LLM 2 <-- LLM2
-
-    # Takes a list of documents, combines them into a single string, and passes this to an LLMChain
-    combine_documents_chain = StuffDocumentsChain(
-        llm_chain=combine_chain, document_variable_name="doc_summaries"
-    )
-
-    # Combines and iteratively reduces the mapped documents
-    reduce_documents_chain = ReduceDocumentsChain(
-        # This is final chain that is called.
-        combine_documents_chain=combine_documents_chain,
-        # If documents exceed context for `StuffDocumentsChain`
-        collapse_documents_chain=collapse_documents_chain,
-        # The maximum number of tokens to group documents into.
-        token_max=800,
-    )
-
-    # Combining documents by mapping a chain over them, then combining results
-    map_reduce_chain = MapReduceDocumentsChain(
-        # Map chain
-        llm_chain=map_chain,
-        # Reduce chain
-        reduce_documents_chain=reduce_documents_chain,
-        # The variable name in the llm_chain to put the documents in
-        document_variable_name="docs",
-        # Return the results of the map steps in the output
-        return_intermediate_steps=False,
-    )
-
-    from langchain.document_loaders import TextLoader
-    from langchain.text_splitter import TokenTextSplitter
-
-    with open(save_dir + "/transcript.txt", "w") as f:
-        f.write(text)
-    loader = TextLoader(save_dir + "/transcript.txt")
-    doc = loader.load()
-    text_splitter = TokenTextSplitter(chunk_size=800, chunk_overlap=100)
-    docs = text_splitter.split_documents(doc)
-
-    summary = map_reduce_chain.run(
-        {"input_documents": docs, "title": title, "words": words}
-    )
-
-    try:
-        del (map_reduce_chain, reduce_documents_chain,
-            combine_chain, collapse_documents_chain,
-            map_chain, collapse_chain,
-            llm, llm2,
-            pipeline, pipeline2,
-            model, tokenizer)
-    except:
-        pass
-    torch.cuda.empty_cache()
-
-    summary = paragraph_or_points(summary, pa_or_po)
-
-    return summary, summary_source
-
-
-# 1.5 - complete function [DELETED]
-
-# 2 - extractive/low-abstractive summary for Key Sentence Highlight
-# 2.1 - chunking + hosted inference, summary [DELETED]
-
-# 2.2 - add spaces between punctuations
-import re
-def add_space_before_punctuation(text):
-    # Define a regular expression pattern to match punctuation
-    punctuation_pattern = r"([.,!?;:])"
-
-    # Use re.sub to add a space before each punctuation
-    modified_text = re.sub(punctuation_pattern, r" \1", text)
-
-    bracket_pattern = r'([()])'
-    modified_text = re.sub(bracket_pattern, r" \1 ", modified_text)
-
-    return modified_text
-
-
-# 2.3 - highlight same words (yellow)
-from difflib import ndiff
-def highlight_text_with_diff(text1, text2):
-    diff = list(ndiff(text1.split(), text2.split()))
-
-    highlighted_diff = []
-    for item in diff:
-        if item.startswith(" "):
-            highlighted_diff.append(
-                '<span style="background-color: rgba(255, 255, 0, 0.25);">'
-                + item
-                + " </span>"
-            )  # Unchanged words
-        elif item.startswith("+"):
-            highlighted_diff.append(item[2:] + " ")
-
-    return "".join(highlighted_diff) # output in string HTML format
-
-# 2.4 - combined - `highlight_key_sentences`
-#   extractive/low-abstractive summarizer with facebook/bart-large-cnn
-#   highlight feature
-def highlight_key_sentences(original_text, api_key):
-
-    import requests
-
-    API_TOKEN = api_key
-    headers = {"Authorization": f"Bearer {API_TOKEN}"}
-    API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
-
-    def query(payload):
-        response = requests.post(API_URL, headers=headers, json=payload)
-        return response.json()
-
-    def chunk_text(text, chunk_size=1024):
-        # Split the text into chunks
-        chunks = [text[i : i + chunk_size] for i in range(0, len(text), chunk_size)]
-        return chunks
-
-    def summarize_long_text(long_text):
-        # Split the long text into chunks
-        text_chunks = chunk_text(long_text)
-
-        # Summarize each chunk
-        summaries = []
-        for chunk in text_chunks:
-            data = query(
-                {
-                    "inputs": f"{chunk}",
-                    "parameters": {"do_sample": False},
-                }
-            )  # what if do_sample=True?
-            summaries.append(data[0]["summary_text"])
-
-        # Combine the summaries of all chunks
-        full_summary = " ".join(summaries)
-        return full_summary
-
-    summarized_text = summarize_long_text(original_text)
-
-    original_text = add_space_before_punctuation(original_text)
-    summarized_text = add_space_before_punctuation(summarized_text)
-
-    return highlight_text_with_diff(summarized_text, original_text)  # output in string HTML format
-
-
-# 3 - extract_keywords
-# 3.1 - initialize & load pipeline
-from transformers import (
-    TokenClassificationPipeline,
-    AutoModelForTokenClassification,
-    AutoTokenizer,
-)
-from transformers.pipelines import AggregationStrategy
-import numpy as np
-
-# Define keyphrase extraction pipeline
-class KeyphraseExtractionPipeline(TokenClassificationPipeline):
-    def __init__(self, model, *args, **kwargs):
-        super().__init__(
-            model=AutoModelForTokenClassification.from_pretrained(model),
-            tokenizer=AutoTokenizer.from_pretrained(model),
-            *args,
-            **kwargs,
-        )
-
-    def postprocess(self, all_outputs):
-        results = super().postprocess(
-            all_outputs=all_outputs,
-            aggregation_strategy=AggregationStrategy.SIMPLE,
-        )
-        return np.unique([result.get("word").strip() for result in results])
-
-
-# Load pipeline
-model_name = "ml6team/keyphrase-extraction-kbir-inspec"
-extractor = KeyphraseExtractionPipeline(model=model_name)
-
-# 3.2 - re-arrange keywords order
-import re
-def rearrange_keywords(text, keywords):  # text:str, keywords:List
-    # Find the positions of each keyword in the text
-    keyword_positions = {word: text.lower().index(word.lower()) for word in keywords}
-
-    # Sort the keywords based on their positions in the text
-    sorted_keywords = sorted(keywords, key=lambda x: keyword_positions[x])
-
-    return sorted_keywords
-
-# 3.3 - `keywords_extractor` function
-def keywords_extractor_list(summary):  # List  : Flashcards
-    keyphrases = extractor(summary)  # extractor() from above | text.replace("\n", " ")
-    list_keyphrases = keyphrases.tolist()
-
-    # rearrange first
-    list_keyphrases = rearrange_keywords(summary, list_keyphrases)
-
-    return list_keyphrases  # returns List
-
-def keywords_extractor_str(summary):  # str   : Keywords Highlight & Fill in the Blank
-    keyphrases = extractor(summary)  # extractor() from above | text.replace("\n", " ")
-    list_keyphrases = keyphrases.tolist()
-
-    # rearrange first
-    list_keyphrases = rearrange_keywords(summary, list_keyphrases)
-
-    # join List elements to one string
-    all_keyphrases = " ".join(list_keyphrases)
-
-    return all_keyphrases  # returns one string
-
-# 3.4 - keywords highlight
-# 3.4.1 - highlight same words (green)
-def highlight_green(text1, text2):  # keywords(str), text
-    diff = list(ndiff(text1.split(), text2.split()))
-
-    highlighted_diff = []
-    for item in diff:
-        if item.startswith(" "):
-            highlighted_diff.append(
-                '<span style="background-color: rgba(0, 255, 0, 0.25);">'
-                + item
-                + " </span>"
-            )  # Unchanged words
-        elif item.startswith("+"):
-            highlighted_diff.append(item[2:] + " ")
-
-    return "".join(highlighted_diff) # output in string HTML format
-
-
-# 3.4.2 - combined - keywords highlight
-def keywords_highlight(text):
-    keywords = keywords_extractor_str(text) # keywords; one string
-    text = add_space_before_punctuation(text)
-    return highlight_green(keywords, text) # output in string HTML format
-
-
-# 3.5 - flashcards
-# 3.5.1 - pair_keywords_sentences
-def pair_keywords_sentences(text, search_words):  # text:str, search_words:List
-
-    result_html = "<span style='text-align: center;'>"
-
-    # Split the text into sentences
-    sentences = re.split(r"(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s", text)
-
-    # Create a dictionary to store sentences for each keyword
-    keyword_sentences = {word: [] for word in search_words}
-
-    # Iterate through sentences and search for keywords
-    for sentence in sentences:
-        for word in search_words:
-            if re.search(
-                r"\b{}\b".format(re.escape(word)), sentence, flags=re.IGNORECASE
-            ):
-                keyword_sentences[word].append(sentence)
-
-    # Print the results
-    for word, sentences in keyword_sentences.items():
-        result_html += "<h2>" + word + "</h2> \n"
-
-        for sentence in sentences:
-            result_html += "<p>" + sentence + "</p> \n"
-
-        result_html += "\n"
-
-    result_html += "</span>"
-
-    return result_html
-
-# 3.5.2 combined - flashcards
-def flashcards(text):
-    keywords = keywords_extractor_list(text) # keywords; a List
-    text = add_space_before_punctuation(text)
-    return pair_keywords_sentences(text, keywords) # output in string HTML format
-
-
-# 3.6 - fill in the blank
-# 3.6.1 - underline same words
-def underline_keywords(text1, text2):  # keywords(str), text
-    diff = list(ndiff(text1.split(), text2.split()))
-
-    highlighted_diff = []
-    for item in diff:
-        if item.startswith(" "):
-            highlighted_diff.append(
-                "_______"
-            )  # Unchanged words. make length independent of word length?
-        elif item.startswith("+"):
-            highlighted_diff.append(item[2:] + " ")
-
-    return "".join(highlighted_diff) # output in string HTML format
-
-
-# 3.6.2 - combined - underline
-def fill_in_blanks(text):
-    keywords = keywords_extractor_str(text)  # keywords; one string
-    text = add_space_before_punctuation(text)
-    return underline_keywords(keywords, text)  # output in string HTML format
-
-
-# 4 - misc
-emptyTabHTML = "<br>\n<p style='color: gray; text-align: center'>Please generate a summary first.</p>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n"
-
-
-def empty_tab():
-    return emptyTabHTML
-
-
-# 5 - the app
-import gradio as gr
-
-with gr.Blocks() as demo:
-    gr.Markdown("<br>")
-
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown("# ✍️ Summarizer for Learning")
-        with gr.Column():
-            gr.HTML("<div style='color: red; text-align: right'>Please use your <a href='#HFAPI' style='color: red'>Hugging Face Access Token.</a></div>")
-
-    with gr.Row():
-        with gr.Column():
-            with gr.Tab("YouTube"):
-                yt_link = gr.Textbox(show_label=False, placeholder="Insert YouTube link here ... (video needs to have caption)")
-                yt_transcript = gr.Textbox(show_label=False, placeholder="Transcript will be shown here ...", lines=12)
-            with gr.Tab("Article"):
-                gr.Textbox(show_label=False, placeholder="WORK IN PROGRESS", interactive=False)
-                gr.Textbox(show_label=False, placeholder="", lines=12, interactive=False)
-            with gr.Tab("Text"):
-                gr.Dropdown(["WORK IN PROGRESS", "Example 2"], show_label=False, value="WORK IN PROGRESS", interactive=False)
-                gr.Textbox(show_label=False, placeholder="", lines=12, interactive=False)
-            with gr.Row():
-                clrButton = gr.ClearButton([yt_link, yt_transcript])
-                subButton = gr.Button(variant="primary", value="Summarize")
-
-            with gr.Accordion("Settings", open=False):
-                length = gr.Radio(["Short", "Medium", "Long"], label="Length", value="Short", interactive=True)
-                pa_or_po = gr.Radio(["Paragraphs", "Points"], label="Summarize to", value="Paragraphs", interactive=True)
-                gr.Checkbox(label="Add headings", interactive=False)
-                gr.Radio(["One section", "Few sections"], label="Summarize into", interactive=False)  # info="Only for 'Medium' or 'Long'"
-                with gr.Row():
-                    clrButtonSt1 = gr.ClearButton([length, pa_or_po], interactive=True)
-                    subButtonSt1 = gr.Button(value="Set Current as Default", interactive=False)
-                    subButtonSt1 = gr.Button(value="Show Default", interactive=False)
-
-            with gr.Accordion("Advanced Settings", open=False):
-                with gr.Group(visible=False):
-                    gr.HTML("<p style='text-align: center;'>&nbsp; YouTube transcription</p>")
-                    force_transcribe_with_app = gr.Checkbox(
-                        label="Always transcribe with app",
-                        info="The app first checks if caption on YouTube is available. If ticked, the app will transcribe the video for you but slower.",
-                    )
-                with gr.Group():
-                    gr.HTML("<p style='text-align: center;'>&nbsp; Summarization</p>")
-                    gr.Radio(["High Abstractive", "Low Abstractive", "Extractive"], label="Type of summarization", value="High Abstractive", interactive=False)
-                    gr.Dropdown(
-                        [
-                            "tiiuae/falcon-7b-instruct",
-                            "GPT2 (work in progress)",
-                            "OpenChat 3.5 (work in progress)",
-                        ],
-                        label="Model",
-                        value="tiiuae/falcon-7b-instruct",
-                        interactive=False,
-                    )
-                    temperature = gr.Slider(0.10, 0.30, step=0.05, label="Temperature", value=0.15,
-                        info="Temperature is limited to 0.1 ~ 0.3 window, as it is shown to produce best result.",
-                        interactive=True,
-                    )
-                    do_sample = gr.Checkbox(label="do_sample", value=True,
-                        info="If ticked, do_sample produces more creative and diverse text, otherwise the app will use greedy decoding that generate more consistent and predictable summary.",
-                    )
-
-                with gr.Group():
-                    gr.HTML("<p style='text-align: center;'>&nbsp; Highlight</p>")
-                    check_key_sen = gr.Checkbox(label="Highlight key sentences", info="In original text", value=True, interactive=False)
-                    gr.Checkbox(label="Highlight keywords", info="In summary", value=True, interactive=False)
-                    gr.Checkbox(label="Turn text to paragraphs", interactive=False)
-
-                with gr.Group():
-                    gr.HTML("<p style='text-align: center;'>&nbsp; Quiz mode</p>")
-                    gr.Checkbox(label="Fill in the blanks", value=True, interactive=False)
-                    gr.Checkbox(label="Flashcards", value=True, interactive=False)
-                    gr.Checkbox(label="Re-write summary", interactive=False)  # info="Only for 'Short'"
-
-                with gr.Row():
-                    clrButtonSt2 = gr.ClearButton(interactive=True)
-                    subButtonSt2 = gr.Button(value="Set Current as Default", interactive=False)
-                    subButtonSt2 = gr.Button(value="Show Default", interactive=False)
-
-        with gr.Column():
-            with gr.Tab("Summary"):  # Output
-                title = gr.Textbox(show_label=False, placeholder="Title")
-                summary = gr.Textbox(lines=11, show_copy_button=True, label="", placeholder="Summarized output ...")
-            with gr.Tab("Key sentences", render=True):
-                key_sentences = gr.HTML(emptyTabHTML)
-                showButtonKeySen = gr.Button(value="Generate")
-            with gr.Tab("Keywords", render=True):
-                keywords = gr.HTML(emptyTabHTML)
-                showButtonKeyWor = gr.Button(value="Generate")
-            with gr.Tab("Fill in the blank", render=True):
-                blanks = gr.HTML(emptyTabHTML)
-                showButtonFilBla = gr.Button(value="Generate")
-            with gr.Tab("Flashcards", render=True):
-                flashCrd = gr.HTML(emptyTabHTML)
-                showButtonFlash = gr.Button(value="Generate")
-            gr.Markdown("<span style='color: gray'>The app is a work in progress. Output may be odd and some features are disabled. [Learn more]().</span>")
-            with gr.Group():
-                gr.HTML("<p id='HFAPI' style='text-align: center;'>&nbsp; 🤗 Hugging Face Access Token [<a href='https://huggingface.co/docs/hub/security-tokens'>more</a>]</p>")
-                hf_access_token = gr.Textbox(
-                    show_label=False,
-                    placeholder="example: hf_******************************",
-                    type="password",
-                    info="The app does not store the token.",
-                )
-            with gr.Accordion("Info", open=False, visible=False):
-                transcript_source = gr.Textbox(show_label=False, placeholder="transcript_source")
-                summary_source = gr.Textbox(show_label=False, placeholder="summary_source")
-                words = gr.Slider(minimum=100, maximum=500, value=250, label="Length of the summary")
-                # words: what should be the constant value?
-                use_api = gr.Checkbox(label="use_api", value=True)
-
-            subButton.click(
-                fn=transcribe_youtube_video,
-                inputs=[yt_link, force_transcribe_with_app, use_api, hf_access_token],
-                outputs=[title, yt_transcript, transcript_source],
-                queue=True,
-            ).then(
-                fn=summarize_text,
-                inputs=[title, yt_transcript, temperature, words, use_api, hf_access_token, do_sample, length, pa_or_po],
-                outputs=[summary, summary_source],
-                api_name="summarize_text",
-                queue=True,
-            )
-
-            subButton.click(fn=empty_tab, outputs=[key_sentences])
-            subButton.click(fn=empty_tab, outputs=[keywords])
-            subButton.click(fn=empty_tab, outputs=[flashCrd])
-            subButton.click(fn=empty_tab, outputs=[blanks])
-
-            showButtonKeySen.click(
-                fn=highlight_key_sentences,
-                inputs=[yt_transcript, hf_access_token],
-                outputs=[key_sentences],
-                queue=True,
-            )
-
-            # Keywords
-            showButtonKeyWor.click(fn=keywords_highlight, inputs=[summary], outputs=[keywords], queue=True)
-
-            # Flashcards
-            showButtonFlash.click(fn=flashcards, inputs=[summary], outputs=[flashCrd], queue=True)
-
-            # Fill in the blanks
-            showButtonFilBla.click(fn=fill_in_blanks, inputs=[summary], outputs=[blanks], queue=True)
-    
-    gr.Examples(
-        examples=["https://www.youtube.com/watch?v=P6FORpg0KVo", "https://www.youtube.com/watch?v=bwEIqjU2qgk"],
-        inputs=[yt_link]
-    )
-
-if __name__ == "__main__":
-    demo.launch(show_api=False)
-    # demo.launch(show_api=False, debug=True)
-    # demo.launch(show_api=False, share=True)
+# 0- libraries
+import transformers
+import gradio as gr
+
+from youtube_transcript_api import YouTubeTranscriptApi
+from huggingface_hub import InferenceClient
+from pytube import YouTube
+import pytube
+import torch
+
+# 1 - abstractive_summary
+# 1.1 - initialize
+import os
+save_dir = os.path.join(os.getcwd(), "docs")
+if not os.path.exists(save_dir):
+    os.mkdir(save_dir)
+transcription_model_id = "openai/whisper-large"
+llm_model_id = "tiiuae/falcon-7b-instruct"
+HF_TOKEN = os.environ.get("HF_TOKEN", None)
+
+# 1.2 - transcription
+def get_yt_transcript(url):
+    text = ""
+    vid_id = pytube.extract.video_id(url)
+    temp = YouTubeTranscriptApi.get_transcript(vid_id)
+    for t in temp:
+        text += t["text"] + " "
+    return text
+
+# 1.2.1 - locally_transcribe
+def transcribe_yt_vid(url):
+    # download YouTube video's audio
+    yt = YouTube(str(url))
+    audio = yt.streams.filter(only_audio=True).first()
+    out_file = audio.download(filename="audio.mp3", output_path=save_dir)
+
+    # defining an automatic-speech-recognition pipeline
+    asr = transformers.pipeline(
+        "automatic-speech-recognition",
+        model=transcription_model_id,
+        device_map="auto",
+    )
+
+    # setting model config parameters
+    asr.model.config.forced_decoder_ids = asr.tokenizer.get_decoder_prompt_ids(
+        language="en", task="transcribe"
+    )
+
+    # invoking the Whisper model
+    temp = asr(out_file, chunk_length_s=20)
+    text = temp["text"]
+
+    # we can do this at the end to release GPU memory
+    del asr
+    torch.cuda.empty_cache()
+
+    return text
+
+# 1.2.1 - api_transcribe
+def transcribe_yt_vid_api(url, api_token):
+    # download YouTube video's audio
+    yt = YouTube(str(url))
+    audio = yt.streams.filter(only_audio=True).first()
+    out_file = audio.download(filename="audio.wav", output_path=save_dir)
+
+    # Initialize client for the Whisper model
+    client = InferenceClient(model=transcription_model_id, token=api_token)
+
+    import librosa
+    import soundfile as sf
+
+    text = ""
+    t = 25  # audio chunk length in seconds
+    x, sr = librosa.load(out_file, sr=None)
+    # This gives x as audio file in numpy array and sr as original sampling rate
+    # The audio needs to be split in 20 second chunks since the API call truncates the response
+    for _, i in enumerate(range(0, (len(x) // (t * sr)) + 1)):
+        y = x[t * sr * i : t * sr * (i + 1)]
+        split_path = os.path.join(save_dir, "audio_split.wav")
+        sf.write(split_path, y, sr)
+        text += client.automatic_speech_recognition(split_path)
+
+    return text
+
+
+# 1.2.3 - transcribe locally or api
+def transcribe_youtube_video(url, force_transcribe=False, use_api=False, api_token=None):
+
+    yt = YouTube(str(url))
+    text = ""
+    # get the transcript from YouTube if available
+    try:
+        text = get_yt_transcript(url)
+    except:
+        pass
+
+    # transcribes the video if YouTube did not provide a transcription
+    # or if you want to force_transcribe anyway
+    if text == "" or force_transcribe:
+        if use_api:
+            text = transcribe_yt_vid_api(url, api_token=api_token)
+            transcript_source = "The transcript was generated using {} via the Hugging Face Hub API.".format(
+                transcription_model_id
+            )
+        else:
+            text = transcribe_yt_vid(url)
+            transcript_source = (
+                "The transcript was generated using {} hosted locally.".format(
+                    transcription_model_id
+                )
+            )
+    else:
+        transcript_source = "The transcript was downloaded from YouTube."
+
+    return yt.title, text, transcript_source
+
+
+# 1.3 - turn to paragraph or points
+def turn_to_paragraph(text):
+    # REMOVE HTML TAGS
+    from bs4 import BeautifulSoup
+
+    # Parse the HTML text
+    soup = BeautifulSoup(text, "html.parser")
+    # Get the text without HTML tags
+    text = soup.get_text()  # print(text_without_tags)
+
+    # Remove leading and trailing whitespaces
+    text = text.strip()
+    # Check if the string ends with "User" and remove it
+    if text.endswith("User"):
+        text = text[: -len("User")]
+    # Replace dashes and extra whitespaces with spaces
+    text = (
+        text.replace(" -", "")
+        .replace("  ", "")
+        .replace("\n", " ")
+        .replace("- ", "")
+        .replace("`", "")
+    )
+    # text = text.replace("  ", "\n\n") # to keep second paragraph if it exists # sometime it's good to turn this on. but let's keep it off
+    text = text.replace("  ", " ")  # off this if ^ is on
+
+    return text
+
+
+# 1.3.1
+def turn_to_points(text):  # input must be from `turn_to_paragraph()`
+    # text = text.replace(". ", ".\n-") # to keep second paragraph if it exists
+    text_with_dashes = ".\n".join("- " + line.strip() for line in text.split(". "))
+    text_with_dashes = text_with_dashes.replace("\n\n", "\n\n- ")  # for first sentence of new paragraph
+    return text_with_dashes
+
+# 1.3.2 - combined funtions above for paragraph_or_points
+def paragraph_or_points(text, pa_or_po):
+    if pa_or_po == "Points":
+        return turn_to_points(turn_to_paragraph(text))
+    else:  # default is Paragraph
+        return turn_to_paragraph(text)
+
+# 1.4 - summarization
+def summarize_text(title, text, temperature, words, use_api=False, api_token=None, do_sample=False, length="Short", pa_or_po="Paragraph",):
+
+    from langchain.chains.llm import LLMChain
+    from langchain.prompts import PromptTemplate
+    from langchain.chains import ReduceDocumentsChain, MapReduceDocumentsChain
+    from langchain.chains.combine_documents.stuff import StuffDocumentsChain
+    import torch
+    import transformers
+    from transformers import BitsAndBytesConfig
+    from transformers import AutoTokenizer, AutoModelForCausalLM
+
+    from langchain import HuggingFacePipeline
+    import torch
+
+    model_kwargs1 = {
+        "temperature": temperature,
+        "do_sample": do_sample,
+        "min_new_tokens": 200 - 25,
+        "max_new_tokens": 200 + 25,
+        "repetition_penalty": 20.0,
+    }
+    model_kwargs2 = {
+        "temperature": temperature,
+        "do_sample": do_sample,
+        "min_new_tokens": words,
+        "max_new_tokens": words + 100,
+        "repetition_penalty": 20.0,
+    }
+    if not do_sample:
+        del model_kwargs1["temperature"]
+        del model_kwargs2["temperature"]
+
+    if use_api:
+
+        from langchain import HuggingFaceHub
+
+        # os.environ["HUGGINGFACEHUB_API_TOKEN"] = api_token
+        llm = HuggingFaceHub(
+            repo_id=llm_model_id,
+            model_kwargs=model_kwargs1,
+            huggingfacehub_api_token=api_token,
+        )
+        llm2 = HuggingFaceHub(
+            repo_id=llm_model_id,
+            model_kwargs=model_kwargs2,
+            huggingfacehub_api_token=api_token,
+        )
+        summary_source = (
+            "The summary was generated using {} via Hugging Face API.".format(
+                llm_model_id
+            )
+        )
+
+    else:
+        quantization_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_compute_dtype=torch.float16,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_use_double_quant=True,
+        )
+
+        tokenizer = AutoTokenizer.from_pretrained(llm_model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            llm_model_id,
+            # quantization_config=quantization_config
+        )
+        model.to_bettertransformer()
+
+        pipeline = transformers.pipeline(
+            "text-generation",
+            model=model,
+            tokenizer=tokenizer,
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+            pad_token_id=tokenizer.eos_token_id,
+            **model_kwargs1,
+        )
+        pipeline2 = transformers.pipeline(
+            "text-generation",
+            model=model,
+            tokenizer=tokenizer,
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+            pad_token_id=tokenizer.eos_token_id,
+            **model_kwargs2,
+        )
+        llm = HuggingFacePipeline(pipeline=pipeline)
+        llm2 = HuggingFacePipeline(pipeline=pipeline2)
+
+        summary_source = "The summary was generated using {} hosted locally.".format(
+            llm_model_id
+        )
+
+    # Map
+    map_template = """
+    Summarize the following video in a clear way:\n
+    ----------------------- \n
+    TITLE: `{title}`\n
+    TEXT:\n
+    `{docs}`\n
+    ----------------------- \n
+    SUMMARY:\n
+    """
+    map_prompt = PromptTemplate(
+        template=map_template, input_variables=["title", "docs"]
+    )
+    map_chain = LLMChain(llm=llm, prompt=map_prompt)
+
+    # Reduce - Collapse
+    collapse_template = """
+    TITLE: `{title}`\n
+    TEXT:\n
+    `{doc_summaries}`\n
+    ----------------------- \n
+    Turn the text of a video above into a long essay:\n
+    """
+
+    collapse_prompt = PromptTemplate(
+        template=collapse_template, input_variables=["title", "doc_summaries"]
+    )
+    collapse_chain = LLMChain(llm=llm, prompt=collapse_prompt)  # LLM 1 <-- LLM
+
+    # Takes a list of documents, combines them into a single string, and passes this to an LLMChain
+    collapse_documents_chain = StuffDocumentsChain(
+        llm_chain=collapse_chain, document_variable_name="doc_summaries"
+    )
+
+    # Final Reduce - Combine
+    combine_template_short = """\n
+    TITLE: `{title}`\n
+    TEXT:\n
+    `{doc_summaries}`\n
+    ----------------------- \n
+    Turn the text of a video above into a 3-sentence summary:\n
+    """
+    combine_template_medium = """\n
+    TITLE: `{title}`\n
+    TEXT:\n
+    `{doc_summaries}`\n
+    ----------------------- \n
+    Turn the text of a video above into a long summary:\n
+    """
+    combine_template_long = """\n
+    TITLE: `{title}`\n
+    TEXT:\n
+    `{doc_summaries}`\n
+    ----------------------- \n
+    Turn the text of a video above into a long essay:\n
+    """
+    # Turn the text of a video above into a 3-sentence summary:\n
+    # Turn the text of a video above into a long summary:\n
+    # Turn the text of a video above into a long essay:\n
+    if length == "Medium":
+        combine_prompt = PromptTemplate(
+            template=combine_template_medium,
+            input_variables=["title", "doc_summaries", "words"],
+        )
+    elif length == "Long":
+        combine_prompt = PromptTemplate(
+            template=combine_template_long,
+            input_variables=["title", "doc_summaries", "words"],
+        )
+    else:  # default is short
+        combine_prompt = PromptTemplate(
+            template=combine_template_short,
+            input_variables=["title", "doc_summaries", "words"],
+        )
+    combine_chain = LLMChain(llm=llm2, prompt=combine_prompt)  # LLM 2 <-- LLM2
+
+    # Takes a list of documents, combines them into a single string, and passes this to an LLMChain
+    combine_documents_chain = StuffDocumentsChain(
+        llm_chain=combine_chain, document_variable_name="doc_summaries"
+    )
+
+    # Combines and iteratively reduces the mapped documents
+    reduce_documents_chain = ReduceDocumentsChain(
+        # This is final chain that is called.
+        combine_documents_chain=combine_documents_chain,
+        # If documents exceed context for `StuffDocumentsChain`
+        collapse_documents_chain=collapse_documents_chain,
+        # The maximum number of tokens to group documents into.
+        token_max=800,
+    )
+
+    # Combining documents by mapping a chain over them, then combining results
+    map_reduce_chain = MapReduceDocumentsChain(
+        # Map chain
+        llm_chain=map_chain,
+        # Reduce chain
+        reduce_documents_chain=reduce_documents_chain,
+        # The variable name in the llm_chain to put the documents in
+        document_variable_name="docs",
+        # Return the results of the map steps in the output
+        return_intermediate_steps=False,
+    )
+
+    from langchain.document_loaders import TextLoader
+    from langchain.text_splitter import TokenTextSplitter
+
+    with open(save_dir + "/transcript.txt", "w") as f:
+        f.write(text)
+    loader = TextLoader(save_dir + "/transcript.txt")
+    doc = loader.load()
+    text_splitter = TokenTextSplitter(chunk_size=800, chunk_overlap=100)
+    docs = text_splitter.split_documents(doc)
+
+    summary = map_reduce_chain.run(
+        {"input_documents": docs, "title": title, "words": words}
+    )
+
+    try:
+        del (map_reduce_chain, reduce_documents_chain,
+            combine_chain, collapse_documents_chain,
+            map_chain, collapse_chain,
+            llm, llm2,
+            pipeline, pipeline2,
+            model, tokenizer)
+    except:
+        pass
+    torch.cuda.empty_cache()
+
+    summary = paragraph_or_points(summary, pa_or_po)
+
+    return summary, summary_source
+
+
+# 1.5 - complete function [DELETED]
+
+# 2 - extractive/low-abstractive summary for Key Sentence Highlight
+# 2.1 - chunking + hosted inference, summary [DELETED]
+
+# 2.2 - add spaces between punctuations
+import re
+def add_space_before_punctuation(text):
+    # Define a regular expression pattern to match punctuation
+    punctuation_pattern = r"([.,!?;:])"
+
+    # Use re.sub to add a space before each punctuation
+    modified_text = re.sub(punctuation_pattern, r" \1", text)
+
+    bracket_pattern = r'([()])'
+    modified_text = re.sub(bracket_pattern, r" \1 ", modified_text)
+
+    return modified_text
+
+
+# 2.3 - highlight same words (yellow)
+from difflib import ndiff
+def highlight_text_with_diff(text1, text2):
+    diff = list(ndiff(text1.split(), text2.split()))
+
+    highlighted_diff = []
+    for item in diff:
+        if item.startswith(" "):
+            highlighted_diff.append(
+                '<span style="background-color: rgba(255, 255, 0, 0.25);">'
+                + item
+                + " </span>"
+            )  # Unchanged words
+        elif item.startswith("+"):
+            highlighted_diff.append(item[2:] + " ")
+
+    return "".join(highlighted_diff) # output in string HTML format
+
+# 2.4 - combined - `highlight_key_sentences`
+#   extractive/low-abstractive summarizer with facebook/bart-large-cnn
+#   highlight feature
+def highlight_key_sentences(original_text, api_key):
+
+    import requests
+
+    API_TOKEN = api_key
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+    API_URL = "https://api-inference.huggingface.co/models/facebook/bart-large-cnn"
+
+    def query(payload):
+        response = requests.post(API_URL, headers=headers, json=payload)
+        return response.json()
+
+    def chunk_text(text, chunk_size=1024):
+        # Split the text into chunks
+        chunks = [text[i : i + chunk_size] for i in range(0, len(text), chunk_size)]
+        return chunks
+
+    def summarize_long_text(long_text):
+        # Split the long text into chunks
+        text_chunks = chunk_text(long_text)
+
+        # Summarize each chunk
+        summaries = []
+        for chunk in text_chunks:
+            data = query(
+                {
+                    "inputs": f"{chunk}",
+                    "parameters": {"do_sample": False},
+                }
+            )  # what if do_sample=True?
+            summaries.append(data[0]["summary_text"])
+
+        # Combine the summaries of all chunks
+        full_summary = " ".join(summaries)
+        return full_summary
+
+    summarized_text = summarize_long_text(original_text)
+
+    original_text = add_space_before_punctuation(original_text)
+    summarized_text = add_space_before_punctuation(summarized_text)
+
+    return highlight_text_with_diff(summarized_text, original_text)  # output in string HTML format
+
+
+# 3 - extract_keywords
+# 3.1 - initialize & load pipeline
+from transformers import (
+    TokenClassificationPipeline,
+    AutoModelForTokenClassification,
+    AutoTokenizer,
+)
+from transformers.pipelines import AggregationStrategy
+import numpy as np
+
+# Define keyphrase extraction pipeline
+class KeyphraseExtractionPipeline(TokenClassificationPipeline):
+    def __init__(self, model, *args, **kwargs):
+        super().__init__(
+            model=AutoModelForTokenClassification.from_pretrained(model),
+            tokenizer=AutoTokenizer.from_pretrained(model),
+            *args,
+            **kwargs,
+        )
+
+    def postprocess(self, all_outputs):
+        results = super().postprocess(
+            all_outputs=all_outputs,
+            aggregation_strategy=AggregationStrategy.SIMPLE,
+        )
+        return np.unique([result.get("word").strip() for result in results])
+
+
+# Load pipeline
+model_name = "ml6team/keyphrase-extraction-kbir-inspec"
+extractor = KeyphraseExtractionPipeline(model=model_name)
+
+# 3.2 - re-arrange keywords order
+import re
+def rearrange_keywords(text, keywords):  # text:str, keywords:List
+    # Find the positions of each keyword in the text
+    keyword_positions = {word: text.lower().index(word.lower()) for word in keywords}
+
+    # Sort the keywords based on their positions in the text
+    sorted_keywords = sorted(keywords, key=lambda x: keyword_positions[x])
+
+    return sorted_keywords
+
+# 3.3 - `keywords_extractor` function
+def keywords_extractor_list(summary):  # List  : Flashcards
+    keyphrases = extractor(summary)  # extractor() from above | text.replace("\n", " ")
+    list_keyphrases = keyphrases.tolist()
+
+    # rearrange first
+    list_keyphrases = rearrange_keywords(summary, list_keyphrases)
+
+    return list_keyphrases  # returns List
+
+def keywords_extractor_str(summary):  # str   : Keywords Highlight & Fill in the Blank
+    keyphrases = extractor(summary)  # extractor() from above | text.replace("\n", " ")
+    list_keyphrases = keyphrases.tolist()
+
+    # rearrange first
+    list_keyphrases = rearrange_keywords(summary, list_keyphrases)
+
+    # join List elements to one string
+    all_keyphrases = " ".join(list_keyphrases)
+
+    return all_keyphrases  # returns one string
+
+# 3.4 - keywords highlight
+# 3.4.1 - highlight same words (green)
+def highlight_green(text1, text2):  # keywords(str), text
+    diff = list(ndiff(text1.split(), text2.split()))
+
+    highlighted_diff = []
+    for item in diff:
+        if item.startswith(" "):
+            highlighted_diff.append(
+                '<span style="background-color: rgba(0, 255, 0, 0.25);">'
+                + item
+                + " </span>"
+            )  # Unchanged words
+        elif item.startswith("+"):
+            highlighted_diff.append(item[2:] + " ")
+
+    return "".join(highlighted_diff) # output in string HTML format
+
+
+# 3.4.2 - combined - keywords highlight
+def keywords_highlight(text):
+    keywords = keywords_extractor_str(text) # keywords; one string
+    text = add_space_before_punctuation(text)
+    return highlight_green(keywords, text) # output in string HTML format
+
+
+# 3.5 - flashcards
+# 3.5.1 - pair_keywords_sentences
+def pair_keywords_sentences(text, search_words):  # text:str, search_words:List
+
+    result_html = "<span style='text-align: center;'>"
+
+    # Split the text into sentences
+    sentences = re.split(r"(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s", text)
+
+    # Create a dictionary to store sentences for each keyword
+    keyword_sentences = {word: [] for word in search_words}
+
+    # Iterate through sentences and search for keywords
+    for sentence in sentences:
+        for word in search_words:
+            if re.search(
+                r"\b{}\b".format(re.escape(word)), sentence, flags=re.IGNORECASE
+            ):
+                keyword_sentences[word].append(sentence)
+
+    # Print the results
+    for word, sentences in keyword_sentences.items():
+        result_html += "<h2>" + word + "</h2> \n"
+
+        for sentence in sentences:
+            result_html += "<p>" + sentence + "</p> \n"
+
+        result_html += "\n"
+
+    result_html += "</span>"
+
+    return result_html
+
+# 3.5.2 combined - flashcards
+def flashcards(text):
+    keywords = keywords_extractor_list(text) # keywords; a List
+    text = add_space_before_punctuation(text)
+    return pair_keywords_sentences(text, keywords) # output in string HTML format
+
+
+# 3.6 - fill in the blank
+# 3.6.1 - underline same words
+def underline_keywords(text1, text2):  # keywords(str), text
+    diff = list(ndiff(text1.split(), text2.split()))
+
+    highlighted_diff = []
+    for item in diff:
+        if item.startswith(" "):
+            highlighted_diff.append(
+                "_______"
+            )  # Unchanged words. make length independent of word length?
+        elif item.startswith("+"):
+            highlighted_diff.append(item[2:] + " ")
+
+    return "".join(highlighted_diff) # output in string HTML format
+
+
+# 3.6.2 - combined - underline
+def fill_in_blanks(text):
+    keywords = keywords_extractor_str(text)  # keywords; one string
+    text = add_space_before_punctuation(text)
+    return underline_keywords(keywords, text)  # output in string HTML format
+
+
+# 4 - misc
+emptyTabHTML = "<br>\n<p style='color: gray; text-align: center'>Please generate a summary first.</p>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n<br>\n"
+
+
+def empty_tab():
+    return emptyTabHTML
+
+
+# 5 - the app
+import gradio as gr
+
+with gr.Blocks() as demo:
+    gr.Markdown("<br>")
+
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("# ✍️ Summarizer for Learning")
+        with gr.Column():
+            gr.HTML("<div style='color: red; text-align: right'>Please use your <a href='#HFAPI' style='color: red'>Hugging Face Access Token.</a></div>")
+
+    with gr.Row():
+        with gr.Column():
+            with gr.Tab("YouTube"):
+                yt_link = gr.Textbox(show_label=False, placeholder="Insert YouTube link here ... (video needs to have caption)")
+                yt_transcript = gr.Textbox(show_label=False, placeholder="Transcript will be shown here ...", lines=12)
+            with gr.Tab("Article"):
+                gr.Textbox(show_label=False, placeholder="WORK IN PROGRESS", interactive=False)
+                gr.Textbox(show_label=False, placeholder="", lines=12, interactive=False)
+            with gr.Tab("Text"):
+                gr.Dropdown(["WORK IN PROGRESS", "Example 2"], show_label=False, value="WORK IN PROGRESS", interactive=False)
+                gr.Textbox(show_label=False, placeholder="", lines=12, interactive=False)
+            with gr.Row():
+                clrButton = gr.ClearButton([yt_link, yt_transcript])
+                subButton = gr.Button(variant="primary", value="Summarize")
+
+            with gr.Accordion("Settings", open=False):
+                length = gr.Radio(["Short", "Medium", "Long"], label="Length", value="Short", interactive=True)
+                pa_or_po = gr.Radio(["Paragraphs", "Points"], label="Summarize to", value="Paragraphs", interactive=True)
+                gr.Checkbox(label="Add headings", interactive=False)
+                gr.Radio(["One section", "Few sections"], label="Summarize into", interactive=False)  # info="Only for 'Medium' or 'Long'"
+                with gr.Row():
+                    clrButtonSt1 = gr.ClearButton([length, pa_or_po], interactive=True)
+                    subButtonSt1 = gr.Button(value="Set Current as Default", interactive=False)
+                    subButtonSt1 = gr.Button(value="Show Default", interactive=False)
+
+            with gr.Accordion("Advanced Settings", open=False):
+                with gr.Group(visible=False):
+                    gr.HTML("<p style='text-align: center;'>&nbsp; YouTube transcription</p>")
+                    force_transcribe_with_app = gr.Checkbox(
+                        label="Always transcribe with app",
+                        info="The app first checks if caption on YouTube is available. If ticked, the app will transcribe the video for you but slower.",
+                    )
+                with gr.Group():
+                    gr.HTML("<p style='text-align: center;'>&nbsp; Summarization</p>")
+                    gr.Radio(["High Abstractive", "Low Abstractive", "Extractive"], label="Type of summarization", value="High Abstractive", interactive=False)
+                    gr.Dropdown(
+                        [
+                            "tiiuae/falcon-7b-instruct",
+                            "GPT2 (work in progress)",
+                            "OpenChat 3.5 (work in progress)",
+                        ],
+                        label="Model",
+                        value="tiiuae/falcon-7b-instruct",
+                        interactive=False,
+                    )
+                    temperature = gr.Slider(0.10, 0.30, step=0.05, label="Temperature", value=0.15,
+                        info="Temperature is limited to 0.1 ~ 0.3 window, as it is shown to produce best result.",
+                        interactive=True,
+                    )
+                    do_sample = gr.Checkbox(label="do_sample", value=True,
+                        info="If ticked, do_sample produces more creative and diverse text, otherwise the app will use greedy decoding that generate more consistent and predictable summary.",
+                    )
+
+                with gr.Group():
+                    gr.HTML("<p style='text-align: center;'>&nbsp; Highlight</p>")
+                    check_key_sen = gr.Checkbox(label="Highlight key sentences", info="In original text", value=True, interactive=False)
+                    gr.Checkbox(label="Highlight keywords", info="In summary", value=True, interactive=False)
+                    gr.Checkbox(label="Turn text to paragraphs", interactive=False)
+
+                with gr.Group():
+                    gr.HTML("<p style='text-align: center;'>&nbsp; Quiz mode</p>")
+                    gr.Checkbox(label="Fill in the blanks", value=True, interactive=False)
+                    gr.Checkbox(label="Flashcards", value=True, interactive=False)
+                    gr.Checkbox(label="Re-write summary", interactive=False)  # info="Only for 'Short'"
+
+                with gr.Row():
+                    clrButtonSt2 = gr.ClearButton(interactive=True)
+                    subButtonSt2 = gr.Button(value="Set Current as Default", interactive=False)
+                    subButtonSt2 = gr.Button(value="Show Default", interactive=False)
+
+        with gr.Column():
+            with gr.Tab("Summary"):  # Output
+                title = gr.Textbox(show_label=False, placeholder="Title")
+                summary = gr.Textbox(lines=11, show_copy_button=True, label="", placeholder="Summarized output ...")
+            with gr.Tab("Key sentences", render=True):
+                key_sentences = gr.HTML(emptyTabHTML)
+                showButtonKeySen = gr.Button(value="Generate")
+            with gr.Tab("Keywords", render=True):
+                keywords = gr.HTML(emptyTabHTML)
+                showButtonKeyWor = gr.Button(value="Generate")
+            with gr.Tab("Fill in the blank", render=True):
+                blanks = gr.HTML(emptyTabHTML)
+                showButtonFilBla = gr.Button(value="Generate")
+            with gr.Tab("Flashcards", render=True):
+                flashCrd = gr.HTML(emptyTabHTML)
+                showButtonFlash = gr.Button(value="Generate")
+            gr.Markdown("<span style='color: gray'>The app is a work in progress. Output may be odd and some features are disabled. [Learn more](https://huggingface.co/spaces/reflection777/summarizer-for-learning/blob/main/README.md).</span>")
+            with gr.Group():
+                gr.HTML("<p id='HFAPI' style='text-align: center;'>&nbsp; 🤗 Hugging Face Access Token [<a href='https://huggingface.co/settings/tokens'>more</a>]</p>")
+                hf_access_token = gr.Textbox(
+                    show_label=False,
+                    placeholder="example: hf_******************************",
+                    type="password",
+                    info="The app does not store the token.",
+                )
+            with gr.Accordion("Info", open=False, visible=False):
+                transcript_source = gr.Textbox(show_label=False, placeholder="transcript_source")
+                summary_source = gr.Textbox(show_label=False, placeholder="summary_source")
+                words = gr.Slider(minimum=100, maximum=500, value=250, label="Length of the summary")
+                # words: what should be the constant value?
+                use_api = gr.Checkbox(label="use_api", value=True)
+
+            subButton.click(
+                fn=transcribe_youtube_video,
+                inputs=[yt_link, force_transcribe_with_app, use_api, hf_access_token],
+                outputs=[title, yt_transcript, transcript_source],
+                queue=True,
+            ).then(
+                fn=summarize_text,
+                inputs=[title, yt_transcript, temperature, words, use_api, hf_access_token, do_sample, length, pa_or_po],
+                outputs=[summary, summary_source],
+                api_name="summarize_text",
+                queue=True,
+            )
+
+            subButton.click(fn=empty_tab, outputs=[key_sentences])
+            subButton.click(fn=empty_tab, outputs=[keywords])
+            subButton.click(fn=empty_tab, outputs=[flashCrd])
+            subButton.click(fn=empty_tab, outputs=[blanks])
+
+            showButtonKeySen.click(
+                fn=highlight_key_sentences,
+                inputs=[yt_transcript, hf_access_token],
+                outputs=[key_sentences],
+                queue=True,
+            )
+
+            # Keywords
+            showButtonKeyWor.click(fn=keywords_highlight, inputs=[summary], outputs=[keywords], queue=True)
+
+            # Flashcards
+            showButtonFlash.click(fn=flashcards, inputs=[summary], outputs=[flashCrd], queue=True)
+
+            # Fill in the blanks
+            showButtonFilBla.click(fn=fill_in_blanks, inputs=[summary], outputs=[blanks], queue=True)
+    
+    gr.Examples(
+        examples=["https://www.youtube.com/watch?v=P6FORpg0KVo", "https://www.youtube.com/watch?v=bwEIqjU2qgk"],
+        inputs=[yt_link]
+    )
+
+if __name__ == "__main__":
+    demo.launch(show_api=False)
+    # demo.launch(show_api=False, debug=True)
+    # demo.launch(show_api=False, share=True)