app.py

import streamlit as st
import sentence_transformers
from transformers import AutoTokenizer
from youtube_transcript_api import YouTubeTranscriptApi
import os
import ast 
import pandas as pd
from segmentation import SemanticTextSegmentation
import re
from symspellpy import SymSpell
import pkg_resources
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from torch import cuda 
from transformers import pipeline
import nltk
nltk.download('stopwords')
from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont


if not os.path.exists('./transcripts'):
    os.mkdir('./transcripts')
device = 'cuda' if cuda.is_available() else 'cpu'

def clean_text(link,start,end):
  
  tokenizer = AutoTokenizer.from_pretrained("CareerNinja/t5_large_1e-4_on_V3dataset")
  sym_spell = SymSpell(max_dictionary_edit_distance=2, prefix_length=7)
  dictionary_path = pkg_resources.resource_filename(
      "symspellpy", "frequency_dictionary_en_82_765.txt"
      )
  sym_spell.load_dictionary(dictionary_path, term_index=0, count_index=1)

  def id_ts_grabber(link):
    youtube_video = link.split("=")
    video_id = youtube_video[1]
    #print(f""" This is the video ID: {video_id} and this is the Timestamp: {time_stamp}""") 
    return video_id
    #print(f""" This is the video ID: {video_id} and no Timestamp was found""")
  
  def seg_getter(data,ts,es):
    starts = []
    for line in data:
        ccs = ast.literal_eval(line)
        starts.append(float(ccs['start']))
    #print(starts)
    #ts_ = float(ts.strip("s&end"))
    #es_ = float(es.strip(es[-1]))
    if not(es) :
        e_val = starts[-1]
    else:
      e_val = starts[min(range(len(starts)), key = lambda i: abs(starts[i]-float(es)))]

    t_val = starts[min(range(len(starts)), key = lambda i: abs(starts[i]-float(ts)))]
    tid = starts.index(t_val)
    eid = starts.index(e_val)
    ts_list_len = len(starts[tid:eid])
    return tid, ts_list_len
    

  def get_cc(video_id):
    try:
        transcript_list = YouTubeTranscriptApi.list_transcripts(video_id)
        try:
            # filter for manually created transcripts
            transcript = transcript_list.find_manually_created_transcript(['en','en-US','en-GB','en-IN'])
        except Exception as e:
            # print(e)
            transcript = None

        manual = True
        if not transcript:
            try:
                # or automatically generated ones
                transcript = transcript_list.find_generated_transcript(['en'])
                manual = False
            except Exception as e:
                # print(e)
                transcript = None

        if transcript:
            if manual: file_name = os.path.join('transcripts', str(video_id) + "_cc_manual" + ".txt")
            else: file_name = os.path.join('transcripts', str(video_id) + "_cc_auto" + ".txt")
            with open(file_name, 'w') as file:
                for line in transcript.fetch():
                    file.write(str(line).replace(r'\xa0', ' ').replace(r'\n', '') + '\n')
            # print(f"CC downloaded in {file_name}")
            return file_name
        else:
            #print("No transcript found")
            return None

    except Exception as e:
        #print(e)
        return None

  def transcript_creator(filename,timestamp,end_pt):
    #print(filename)
    with open(filename, 'r') as f:
      data = f.readlines()
    #print("This is data: ", data)
    transcripts = []
    #print("this is ts: ",timestamp)
    if not(timestamp)  and not(end_pt):
      #print("executing 1 ")
      for line in data:
        ccs = ast.literal_eval(line)
        transcripts.append(ccs['text'])
      return transcripts

    elif not(timestamp) and end_pt :
        timestamp = 0
        start,lenlist = seg_getter(data, timestamp, end_pt)
        
        for t in range(lenlist):
            ccs = ast.literal_eval(data[start+t])
            transcripts.append(ccs['text'])
        return transcripts 

    
    else :
        #print("executing 2")
        start,lenlist = seg_getter(data,timestamp,end_pt)
        #print(f""" This is the ts list{ts_len}""")
        for t in range(lenlist):
            ccs = ast.literal_eval(data[start+t])
            transcripts.append(ccs['text'])
        return transcripts 

  def transcript_collector(link,ts,es):
    vid = id_ts_grabber(link)
    print(f""" Fetching the transcript """)
    filename = get_cc(vid)
    return transcript_creator(filename, ts, es), vid

  transcript = pd.DataFrame(columns=['text', 'video_id'])
  transcript.loc[0,'text'],transcript.loc[0,'video_id'] = transcript_collector(link,start,end)
  
  def segment(corpus):
    text_data = [re.sub(r'\[.*?\]', '', x).strip() for x in corpus]
    text_data = [x for x in text_data if x != '']
    df = pd.DataFrame(text_data, columns=["utterance"])
    # remove new line, tab, return
    df["utterance"] = df["utterance"].apply(lambda x: x.replace("\n", " ").replace("\r", " ").replace("\t", " "))
    # remove Nan
    df.dropna(inplace=True)
    sts = SemanticTextSegmentation(df)
    texts = sts.get_segments()
    return texts
  
  sf = pd.DataFrame(columns=['Segmented_Text','video_id'])

  text = segment(transcript.at[0,'text'])
  
  for i in range(len(text)):
    sf.loc[i, 'Segmented_Text'] = text[i]
    sf.loc[i, 'video_id'] = transcript.at[0,'video_id']

  def word_seg(text):
    text = text.replace("\n", " ").replace("\r", " ").replace("\t", " ").replace("\xa0", " ")
    results = sym_spell.word_segmentation(text, max_edit_distance=0)
    texts = results.segmented_string
    #result = re.sub(r'[^\w\s]', '',texts).lower()
    return texts
  
  for i in range(len(sf)):
    #st.write(sf.at[i, 'Segmented_Text'])
    sf.loc[i, 'Segmented_Text'] = word_seg(sf.at[i, 'Segmented_Text'])
    sf.loc[i, 'Lengths'] = len(tokenizer(sf.at[i, 'Segmented_Text'])['input_ids'])

  texts = pd.DataFrame(columns=['texts'])

  def segment_loader(dataframe):
    flag = 0
    for i in range(len(dataframe)):
      if flag > 0:
        flag -= 1
        continue
      m = 512
      iter = 0
      texts.loc[i, 'texts'] = dataframe.at[i+iter, 'Segmented_Text']
      length = dataframe.at[i+iter, 'Lengths']
      texts.loc[i,'video_id'] = dataframe.at[i, 'video_id']
      while i+iter < len(dataframe)-1 and dataframe.at[i, 'video_id'] == dataframe.at[i+iter+1, 'video_id']:
          if length + dataframe.at[i + iter + 1, 'Lengths'] <= m :
              texts.loc[i,'texts'] +=  " " + dataframe.at[i+iter+1, 'Segmented_Text']
              length += dataframe.at[i+iter + 1,'Lengths']
              iter += 1
          else:
              break
          
      flag = iter
    return texts

  cleaned_text = segment_loader(sf)
  cleaned_text.reset_index(drop=True, inplace=True)

  return cleaned_text
 

def t5_summarizer(link,start, end):
    input_text = clean_text(link,start,end)
    lst_outputs = []
    tokenizer1 = AutoTokenizer.from_pretrained("CareerNinja/t5_large_1e-4_on_V3dataset")
    model1 = AutoModelForSeq2SeqLM.from_pretrained("CareerNinja/t5_large_1e-4_on_V3dataset")
    summarizer1 = pipeline("summarization", model=model1, tokenizer=tokenizer1)
    print(f""" Entered summarizer ! """)
    st.write('Below is the summary of the given URL: ')
    for i in range(len(input_text)):
        summary = summarizer1(input_text.at[i,'texts'], min_length=64, max_length=128)
        sumry = list(summary[0].values())
        input_text.loc[i,'Generated Summary'] = sumry[0]
        lst_outputs.append(sumry[0])
        st.write(input_text.at[i,'Generated Summary'])
        if i != len(input_text) - 1:
            st.write('=====================================================================================')
    return lst_outputs

def card_creator(path, text, y_value):
    img = Image.open(path)

    def text_wrap(text, font, max_width):
            """Wrap text base on specified width. 
            This is to enable text of width more than the image width to be display
            nicely.
            @params:
                text: str
                    text to wrap
                font: obj
                    font of the text
                max_width: int
                    width to split the text with
            @return
                lines: list[str]
                    list of sub-strings
            """
            lines = []
            
            # If the text width is smaller than the image width, then no need to split
            # just add it to the line list and return
            if font.getsize(text)[0]  <= max_width:
                lines.append(text)
            else:
                #split the line by spaces to get words
                words = text.split(' ')
                i = 0
                # append every word to a line while its width is shorter than the image width
                while i < len(words):
                    line = ''
                    while i < len(words) and font.getsize(line + words[i])[0] <= max_width:
                        line = line + words[i]+ " "
                        i += 1
                    if not line:
                        line = words[i]
                        i += 1
                    lines.append(line)
            return lines


    font_path = 'Montserrat-Regular.ttf'
    font = ImageFont.truetype(font=font_path, size=22)
    lines = text_wrap(text, font, img.size[0] - 44)
    line_height = font.getsize('hg')[1]

    draw = ImageDraw.Draw(img)
    #Draw text on image
    color = 'rgb(255,255,255)'  # white color
    x = 22
    y = y_value
    for line in lines:
        draw.text((x,y), line, fill=color, font=font)
        
        y = y + line_height    # update y-axis for new line
    img.save("card.png")
    st.image(img, caption="Summary Card")

def main():

  if 'submitted' not in st.session_state:
    st.session_state.submitted = False
    
  if 'opt' not in st.session_state:
    st.session_state.opt = []

  def callback():
      st.session_state.submitted = True

  st.title('Video Summarizer')
  url = st.text_input('Enter the Video Link')
  start_pt = st.text_input('Enter the Start point in secs')
  end_pt = st.text_input('Enter the end point in secs') 

  if (st.button("Submit URL", on_click=callback) and url) :
    opt = t5_summarizer(url,start_pt,end_pt)
    st.session_state.opt = opt
  #st.write(st.session_state)
    #text = st.text_input('Enter the Summary here to make a Summary Card.')
    #text = st.selectbox('Select the summary you want to creat a card of ', opt, key="text")
    #st.write('You selected:', option)
  if st.session_state.submitted and st.session_state.opt:
    text = st.selectbox('Select the summary you want to creat a card of ', st.session_state.opt)

    option = st.selectbox('Which color template would you like to use ?',('Elf Green','Dark Pastel Green'))
    if st.button("Generate Summary Card") and text and option:
      if option == 'Elf Green':
        if len(text) > 380 :
          st.error('Summary is too long !')
        else:
          card_creator('iteration5_empty.png',text,335)
      else : 
        if len(text) > 430 :
          st.error('Summary is too long !')
        else : 
          card_creator('X-93.png',text,285)

      with open("card.png", "rb") as file:
        btn = st.download_button(
              label="Download card",
              data=file,
              file_name="card.png",
              mime="image/png"
            )

main()