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| import gradio as gr | |
| import pandas as pd | |
| import torch | |
| from googletrans import Translator | |
| from transformers import T5Tokenizer | |
| from transformers import T5ForConditionalGeneration | |
| from transformers import BartForConditionalGeneration | |
| from transformers import BartTokenizer | |
| from transformers import pipeline | |
| tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-cnn') | |
| model = BartForConditionalGeneration.from_pretrained('facebook/bart-large-cnn') | |
| from transformers import PreTrainedModel | |
| from transformers import PreTrainedTokenizer | |
| # Question launcher | |
| class E2EQGPipeline: | |
| def __init__( | |
| self, | |
| model: PreTrainedModel, | |
| tokenizer: PreTrainedTokenizer | |
| ): | |
| self.device = "cuda" if torch.cuda.is_available() else "cpu" | |
| self.model = model | |
| self.tokenizer = tokenizer | |
| self.model_type = "t5" | |
| self.kwargs = { | |
| "max_length": 256, | |
| "num_beams": 4, | |
| "length_penalty": 1.5, | |
| "no_repeat_ngram_size": 3, | |
| "early_stopping": True, | |
| } | |
| def generate_questions(self, context: str): | |
| inputs = self._prepare_inputs_for_e2e_qg(context) | |
| outs = self.model.generate( | |
| input_ids=inputs['input_ids'].to(self.device), | |
| attention_mask=inputs['attention_mask'].to(self.device), | |
| **self.kwargs | |
| ) | |
| prediction = self.tokenizer.decode(outs[0], skip_special_tokens=True) | |
| questions = prediction.split("<sep>") | |
| questions = [question.strip() for question in questions[:-1]] | |
| return questions | |
| def _prepare_inputs_for_e2e_qg(self, context): | |
| source_text = f"generate questions: {context}" | |
| inputs = self._tokenize([source_text], padding=False) | |
| return inputs | |
| def _tokenize( | |
| self, | |
| inputs, | |
| padding=True, | |
| truncation=True, | |
| add_special_tokens=True, | |
| max_length=512 | |
| ): | |
| inputs = self.tokenizer.batch_encode_plus( | |
| inputs, | |
| max_length=max_length, | |
| add_special_tokens=add_special_tokens, | |
| truncation=truncation, | |
| padding="max_length" if padding else False, | |
| pad_to_max_length=padding, | |
| return_tensors="pt" | |
| ) | |
| return inputs | |
| def generate_questions(text): | |
| qg_model = T5ForConditionalGeneration.from_pretrained('valhalla/t5-base-e2e-qg') | |
| qg_tokenizer = T5Tokenizer.from_pretrained('valhalla/t5-base-e2e-qg') | |
| qg_final_model = E2EQGPipeline(qg_model, qg_tokenizer) | |
| questions = qg_final_model.generate_questions(text) | |
| translator = Translator() | |
| translated_questions = [translator.translate(question, dest='es').text for question in questions] | |
| return translated_questions | |
| def generate_summary(text): | |
| inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=1024, truncation=True) | |
| summary_ids = model.generate(inputs, max_length=150, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True) | |
| summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| return summary | |
| def process(text): | |
| return generate_summary(text), generate_questions(text) | |
| textbox = gr.Textbox(label="Pega el text aca:", placeholder="Texto...", lines=15) | |
| demo = gr.Interface(fn=process, inputs=textbox, outputs=["text", "text"]) | |
| demo.launch() |