Spaces:
Sleeping
Sleeping
| # -*- coding: utf-8 -*- | |
| """Gradio NLP Group Project.ipynb | |
| Automatically generated by Colab. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1hDGMwj7G7avlxrqmXe6SIN9LjLRRsuqE | |
| """ | |
| import gradio as gr | |
| from youtube_transcript_api import YouTubeTranscriptApi | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, MarianMTModel, MarianTokenizer, AutoModelForSequenceClassification | |
| import torch | |
| class TextProcessor: | |
| def __init__(self, text): | |
| self.text = text | |
| def summarize_text(self, text): | |
| tokenizer = AutoTokenizer.from_pretrained('cranonieu2021/pegasus-on-lectures') | |
| model = AutoModelForSeq2SeqLM.from_pretrained("cranonieu2021/pegasus-on-lectures") | |
| inputs = tokenizer(text, max_length=1024, return_tensors="pt", truncation=True) | |
| summary_ids = model.generate(inputs.input_ids, max_length=150, min_length=40, length_penalty=2.0, num_beams=4, early_stopping=True) | |
| summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| return summary | |
| def translate_text(self, text): | |
| model_name = "sfarjebespalaia/enestranslatorforsummaries" | |
| tokenizer = MarianTokenizer.from_pretrained(model_name) | |
| model = MarianMTModel.from_pretrained(model_name) | |
| src_text = [text] | |
| tokenized_text = tokenizer.prepare_seq2seq_batch(src_text, return_tensors="pt") | |
| translated = model.generate(**tokenized_text) | |
| translated_text = tokenizer.decode(translated[0], skip_special_tokens=True) | |
| return translated_text | |
| def classify_text(self, text): | |
| model_name = "gserafico/roberta-base-finetuned-classifier-roberta1" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_name) | |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| predicted_class_idx = torch.argmax(outputs.logits, dim=1).item() | |
| labels = { | |
| 0: 'Social Sciences', | |
| 1: 'Arts', | |
| 2: 'Natural Sciences', | |
| 3: 'Business and Law', | |
| 4: 'Engineering and Technology' | |
| } | |
| return labels[predicted_class_idx] | |
| def get_transcript(video_id): | |
| try: | |
| transcripts = YouTubeTranscriptApi.list_transcripts(video_id) | |
| except NoTranscriptFound: | |
| return "No transcript found for this video.", 'en' | |
| except TranscriptsDisabled: | |
| return "Transcripts are disabled for this video.", 'en' | |
| except Exception as e: | |
| return f"An error occurred: {str(e)}", 'en' | |
| available_languages = [] | |
| for transcript in transcripts: | |
| language_details = { | |
| 'Language': transcript.language, | |
| 'Language Code': transcript.language_code, | |
| 'Is Generated': transcript.is_generated, | |
| 'Is Translatable': transcript.is_translatable | |
| } | |
| available_languages.append(language_details) | |
| available_languages = [transcript['Language Code'] for transcript in available_languages if transcript['Language Code'] in ['en']] | |
| if 'en' in available_languages: | |
| transcript_list = YouTubeTranscriptApi.get_transcript(video_id, languages=['en']) | |
| transcript_text = ' '.join([i['text'] for i in transcript_list]) | |
| return transcript_text, 'en' | |
| else: | |
| return 'Transcript in unsupported language.' | |
| def process_text(video_id): | |
| transcript, language = get_transcript(video_id) | |
| if transcript.startswith("An error occurred:") or "No transcript" in transcript: | |
| return {"Error": transcript, "Language Detected": "None"} | |
| processor = TextProcessor(transcript) | |
| try: | |
| summarized_text = processor.summarize_text(transcript) | |
| translated_text = processor.translate_text(summarized_text) | |
| classification_result = processor.classify_text(summarized_text) | |
| results = { | |
| 'Language Detected': language, | |
| 'Summarized Text': summarized_text, | |
| 'Translated Text': translated_text, | |
| 'Classification Result': classification_result | |
| } | |
| except Exception as e: | |
| results = {'Error': f"An error occurred during processing: {str(e)}"} | |
| return results | |
| iface = gr.Interface( | |
| fn=process_text, | |
| inputs=[gr.Textbox(label="YouTube Video ID")], | |
| outputs=[gr.JSON(label="Results")], | |
| title="Text Processing App with YouTube Transcript", | |
| description="This app allows you to fetch, summarize, translate, and classify YouTube video transcripts. Errors are handled and displayed." | |
| ) | |
| def main(): | |
| iface.launch() | |
| if __name__ == '__main__': | |
| main() |