Create util.py

util.py

@@ -1,142 +1,2 @@
-from datasets import Dataset
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainer, TrainingArguments
-from youtube_transcript_api import YouTubeTranscriptApi
-from deepmultilingualpunctuation import PunctuationModel
-from googletrans import Translator
-import time
-import torch
-import re
-
-
-def load_model(cp):
-    tokenizer = AutoTokenizer.from_pretrained("VietAI/vit5-base")
-    model  = AutoModelForSeq2SeqLM.from_pretrained(cp)
-    return tokenizer, model 
-
-
-def summarize(text, model, tokenizer, num_beams=4, device='cpu'):
-    model.to(device)
-    inputs = tokenizer.encode(text, return_tensors="pt", max_length=1024, truncation=True, padding = True).to(device)
-    
-    with torch.no_grad():
-        summary_ids = model.generate(inputs, max_length=256, num_beams=num_beams)
-    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-    
-    return summary
-
-
-def processed(text):
-    processed_text = text.replace('\n', ' ')
-    processed_text = processed_text.lower()
-    return processed_text
-
-
-def get_subtitles(video_url):
-    try:
-        video_id = video_url.split("v=")[1]
-        transcript = YouTubeTranscriptApi.get_transcript(video_id, languages=['en'])
-        subs = " ".join(entry['text'] for entry in transcript)
-        print(subs)
-
-        return transcript, subs
-
-    except Exception as e:
-        return [], f"An error occurred: {e}"
-
-from youtube_transcript_api import YouTubeTranscriptApi
-
-
-def restore_punctuation(text):
-    model = PunctuationModel()
-    result = model.restore_punctuation(text)
-    return result
-
-
-def translate_long(text, language='vi'):
-    translator = Translator()
-    limit = 4700
-    chunks = []
-    current_chunk = ''
-
-    sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', text)
-
-    for sentence in sentences:
-        if len(current_chunk) + len(sentence) <= limit:
-            current_chunk += sentence.strip() + ' '
-        else:
-            chunks.append(current_chunk.strip())
-            current_chunk = sentence.strip() + ' '
-
-    if current_chunk:
-        chunks.append(current_chunk.strip())
-
-    translated_text = ''
-
-    for chunk in chunks:
-        try:
-            time.sleep(1)
-            translation = translator.translate(chunk, dest=language)
-            translated_text += translation.text + ' '
-        except Exception as e:
-            translated_text += chunk + ' '
-
-    return translated_text.strip()
-
-def split_into_chunks(text, max_words=800, overlap_sentences=2):
-    sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', text)
-    
-    chunks = []
-    current_chunk = []
-    current_word_count = 0
-    
-    for sentence in sentences:
-        word_count = len(sentence.split())
-        if current_word_count + word_count <= max_words:
-            current_chunk.append(sentence)
-            current_word_count += word_count
-        else:
-            if len(current_chunk) >= overlap_sentences:
-                overlap = current_chunk[-overlap_sentences:]
-            chunks.append(' '.join(current_chunk))
-            current_chunk = current_chunk[-overlap_sentences:] + [sentence]
-            current_word_count = sum(len(sent.split()) for sent in current_chunk)
-    if current_chunk:
-        if len(current_chunk) >= overlap_sentences:
-            overlap = current_chunk[-overlap_sentences:]
-        chunks.append(' '.join(current_chunk))
-    
-    return chunks
-
-
-def post_processing(text):
-    sentences = re.split(r'(?<=[.!?])\s*', text)
-    for i in range(len(sentences)):
-        if sentences[i]:
-            sentences[i] = sentences[i][0].upper() + sentences[i][1:]
-    text = " ".join(sentences)
-    return text
-
-
-def display(text):
-    sentences = re.split(r'(?<=[.!?])\s*', text)
-    unique_sentences = list(dict.fromkeys(sentences[:-1]))
-    formatted_sentences = [f"• {sentence}" for sentence in unique_sentences]
-    return formatted_sentences
-
-
-
-def pipeline(url, model, tokenizer):
-    trans, sub = get_subtitles(url)
-    sub = restore_punctuation(sub)
-    vie_sub = translate_long(sub)
-    vie_sub = processed(vie_sub)
-    chunks = split_into_chunks(vie_sub, 700, 2)
-    sum_para = []
-    for i in chunks:
-        tmp = summarize(i, model, tokenizer, num_beams=3)
-        sum_para.append(tmp)
-    suma = ''.join(sum_para)
-    del sub, vie_sub, sum_para, chunks
-    suma = post_processing(suma)
-    re = display(suma)
-    return re
+def update(name):
+    return f"Welcome to Gradio, {name}!"