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| import gradio as gr | |
| from transformers import pipeline | |
| import nltk | |
| nltk.download('punkt') | |
| from nltk import sent_tokenize | |
| import torch | |
| from transformers import ( | |
| pipeline, | |
| AutoModelForSeq2SeqLM, | |
| AutoTokenizer | |
| ) | |
| import re | |
| device = [0 if torch.cuda.is_available() else 'cpu'][0] | |
| def _generate(query, context, model, device): | |
| FT_MODEL = AutoModelForSeq2SeqLM.from_pretrained(model).to(device) | |
| FT_MODEL_TOKENIZER = AutoTokenizer.from_pretrained(model) | |
| input_text = "question: " + query + "</s> question_context: " + context | |
| input_tokenized = FT_MODEL_TOKENIZER.encode(input_text, return_tensors='pt', truncation=True, padding='max_length', max_length=1024).to(device) | |
| _tok_count_assessment = FT_MODEL_TOKENIZER.encode(input_text, return_tensors='pt', truncation=True).to(device) | |
| summary_ids = FT_MODEL.generate(input_tokenized, | |
| max_length=30, | |
| min_length=3, | |
| length_penalty=1.0, | |
| num_beams=2, | |
| early_stopping=True, | |
| ) | |
| output = [FT_MODEL_TOKENIZER.decode(id, clean_up_tokenization_spaces=True, skip_special_tokens=True) for id in summary_ids] | |
| return str(output[0]) | |
| def predict(query, context): | |
| context = context.encode("ascii", "ignore") | |
| context = context.decode() | |
| #Custom1 | |
| cust_model_name = "consciousAI/question-answering-roberta-base-s" | |
| cust_question_answerer = pipeline('question-answering', model=cust_model_name, tokenizer=cust_model_name, device=device) | |
| cust_output = cust_question_answerer(question=query, context=context) | |
| cust_answer = cust_output['answer'] | |
| cust_answer_span = "[" + str(cust_output['start']) + "," + str(cust_output['end']) + "]" | |
| cust_confidence = cust_output['score'] | |
| cust_answer_sentence = [_sent for _sent in sent_tokenize(context) if cust_answer in _sent] | |
| if len(cust_answer_sentence) > 0: | |
| cust_answer_sentence = cust_answer_sentence[0] | |
| else: | |
| cust_answer_sentence = "Failed matching sentence (answer may be split in multiple sentences)" | |
| #Custom3 | |
| cust_model_name_3 = "consciousAI/question-answering-roberta-base-s-v2" | |
| cust_question_answerer_3 = pipeline('question-answering', model=cust_model_name_3, tokenizer=cust_model_name_3, device=device) | |
| cust_output_3 = cust_question_answerer_3(question=query, context=context) | |
| cust_answer_3 = cust_output_3['answer'] | |
| cust_answer_span_3 = "[" + str(cust_output_3['start']) + "," + str(cust_output_3['end']) + "]" | |
| cust_confidence_3 = cust_output_3['score'] | |
| cust_answer_sentence_3 = [_sent for _sent in sent_tokenize(context) if cust_answer_3 in _sent] | |
| if len(cust_answer_sentence_3) > 0: | |
| cust_answer_sentence_3 = cust_answer_sentence_3[0] | |
| else: | |
| cust_answer_sentence_3 = "Failed matching sentence (answer may be split in multiple sentences)" | |
| #Custom2 | |
| cust_answer_2 = _generate(query, context, model="consciousAI/question-answering-generative-t5-v1-base-s-q-c", device=device) | |
| cust_answer_sentence_2 = [_sent for _sent in sent_tokenize(context) if cust_answer_2 in _sent] | |
| if len(cust_answer_sentence_2) > 0: | |
| cust_answer_sentence_2 = cust_answer_sentence_2[0] | |
| else: | |
| cust_answer_sentence_2 = "Failed matching sentence (answer may be split in multiple sentences)" | |
| cust_answer_span_2 = re.search(cust_answer_2, contextDefault).span() | |
| return cust_answer, cust_answer_sentence, cust_answer_span, cust_confidence, cust_answer_2, cust_answer_sentence_2, cust_answer_span_2, cust_answer_sentence_3, cust_answer_3, cust_answer_span_3, cust_confidence_3 | |
| with gr.Blocks() as demo: | |
| gr.Markdown(value="# Question Answering Encoders vs Generative\n [Question Answering Leveraging Encoders V1](https://huggingface.co/anshoomehra/question-answering-roberta-base-s)\n\n[Question Answering Leveraging Encoders V2](https://huggingface.co/anshoomehra/question-answering-roberta-base-s-v2)\n\n[Generative Question Answering](https://huggingface.co/anshoomehra/question-answering-generative-t5-v1-base-s-q-c)") | |
| with gr.Accordion(variant='compact', label='Input Values'): | |
| with gr.Row(variant='compact'): | |
| queryDefault = "Which company alongside Amazon, Apple, Meta, and Microsoft is considered part of Big Five?" | |
| contextDefault = "Google LLC is an American multinational technology company focusing on search engine technology, online advertising, cloud computing, computer software, quantum computing, e-commerce, artificial intelligence, and consumer electronics. It has been referred to as 'the most powerful company in the world' and one of the world's most valuable brands due to its market dominance, data collection, and technological advantages in the area of artificial intelligence. Its parent company Alphabet is considered one of the Big Five American information technology companies, alongside Amazon, Apple, Meta, and Microsoft." | |
| query = gr.Textbox(queryDefault, label="Query", placeholder="Dummy Query", lines=2) | |
| context = gr.Textbox(contextDefault, label="Context", placeholder="Dummy Context", lines=5, max_lines = 6) | |
| with gr.Accordion(variant='compact', label='Q&A Model(s) Output'): | |
| with gr.Row(variant='compact'): | |
| with gr.Column(variant='compact'): | |
| _predictionM6 = gr.Textbox(label="question-answering-roberta-base-s: Answer Sentence") | |
| _predictionM5 = gr.Textbox(label="question-answering-roberta-base-s: Answer") | |
| _predictionM7 = gr.Textbox(label="question-answering-roberta-base-s: Q&A Answer Span") | |
| _predictionM8 = gr.Textbox(label="question-answering-roberta-base-s: Answer Confidence") | |
| with gr.Column(variant='compact'): | |
| _predictionM12 = gr.Textbox(label="question-answering-roberta-base-s-v2: Answer Sentence") | |
| _predictionM13 = gr.Textbox(label="question-answering-roberta-base-s-v2: Answer") | |
| _predictionM14 = gr.Textbox(label="question-answering-roberta-base-s-v2: Q&A Answer Span") | |
| _predictionM15 = gr.Textbox(label="question-answering-roberta-base-s-v2: Answer Confidence") | |
| with gr.Column(variant='compact'): | |
| _predictionM10 = gr.Textbox(label="question-answering-generative-t5-v1-base-s-q-c: Sentence") | |
| _predictionM9 = gr.Textbox(label="question-answering-generative-t5-v1-base-s-q-c: Answer") | |
| _predictionM11 = gr.Textbox(label="question-answering-generative-t5-v1-base-s-q-c: Answer Span") | |
| with gr.Row(): | |
| gen_btn = gr.Button("Generate Answers") | |
| gen_btn.click(fn=predict, | |
| inputs=[query, context], | |
| outputs=[_predictionM5, _predictionM6, _predictionM7, _predictionM8, _predictionM9, _predictionM10, _predictionM11, _predictionM12, _predictionM13, _predictionM14, _predictionM15] | |
| ) | |
| demo.launch(show_error=True) |