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| import torch | |
| from transformers import AutoTokenizer, AutoModel, AutoModelForSeq2SeqLM | |
| import gradio as gr | |
| import faiss | |
| import numpy as np | |
| import pandas as pd | |
| from transformers import pipeline | |
| from sentence_transformers import SentenceTransformer | |
| model_name = "9wimu9/lfqa-mt5-large-sin-v1" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| question_answerer_seq_2seq = model.to(device) | |
| retriever_model = SentenceTransformer('9wimu9/retriever-model-sinhala-v2') | |
| question_answerer = pipeline("question-answering", model='9wimu9/xlm-roberta-large-en-si-only-finetuned-sinquad-v12') | |
| def srq2seq_find_answer(query,context): | |
| conditioned_doc = "<P> " + " <P> ".join([d for d in context]) | |
| query_and_docs = "question: {} context: {}".format(query, conditioned_doc) | |
| model_input = tokenizer(query_and_docs, truncation=True, padding=True, return_tensors="pt") | |
| generated_answers_encoded = model.generate(input_ids=model_input["input_ids"].to(device), | |
| attention_mask=model_input["attention_mask"].to(device), | |
| min_length=2, | |
| max_length=120, | |
| early_stopping=True, | |
| num_beams=9, | |
| temperature=0.9, | |
| do_sample=False, | |
| top_k=None, | |
| top_p=None, | |
| eos_token_id=tokenizer.eos_token_id, | |
| no_repeat_ngram_size=8, | |
| num_return_sequences=1) | |
| return tokenizer.batch_decode(generated_answers_encoded, skip_special_tokens=True,clean_up_tokenization_spaces=True)[0] | |
| def encode_file(file_path): | |
| passages = [] | |
| with open(file_path) as file: | |
| for item in file: | |
| passages.append([item]) | |
| df = pd.DataFrame(passages, columns = ['text']) | |
| text = df['text'] | |
| vectors = retriever_model.encode(text) | |
| return vectors,passages | |
| def upload_file(files): | |
| global index | |
| global passages | |
| file_paths = [file.name for file in files] | |
| vectors,passages = encode_file(file_paths[0]) | |
| vector_dimension = vectors.shape[1] | |
| index = faiss.IndexFlatL2(vector_dimension) | |
| faiss.normalize_L2(vectors) | |
| index.add(vectors) | |
| return file_paths | |
| def question_answer(search_text): | |
| search_vector = retriever_model.encode(search_text) | |
| print(search_vector) | |
| _vector = np.array([search_vector]) | |
| faiss.normalize_L2(_vector) | |
| k = index.ntotal | |
| distances, ann = index.search(_vector, k=k) | |
| context = passages[ann[0][0]][0] | |
| result = question_answerer(question=search_text, context=context) | |
| print(result) | |
| return result['answer'] | |
| def question_answer_generated(search_text): | |
| search_vector = retriever_model.encode(search_text) | |
| print(search_vector) | |
| _vector = np.array([search_vector]) | |
| faiss.normalize_L2(_vector) | |
| k = index.ntotal | |
| distances, ann = index.search(_vector, k=k) | |
| context = passages[ann[0][0]][0] | |
| return srq2seq_find_answer(search_text,[context]) | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| file_output = gr.File() | |
| upload_button = gr.UploadButton("Click to Upload a File", file_types=["txt"], file_count="1") | |
| upload_button.upload(upload_file, upload_button, file_output) | |
| with gr.Row(): | |
| with gr.Column(): | |
| name = gr.Textbox(label="question") | |
| output = gr.Textbox(label="answer") | |
| greet_btn = gr.Button("get Answer - extraction QA") | |
| greet_btn.click(fn=question_answer, inputs=name, outputs=output, api_name="greet") | |
| with gr.Row(): | |
| with gr.Column(): | |
| name = gr.Textbox(label="question") | |
| output = gr.Textbox(label="answer") | |
| greet_btn = gr.Button("get Answer - Generated QA") | |
| greet_btn.click(fn=question_answer_generated, inputs=name, outputs=output, api_name="greet") | |
| demo.launch(debug=True) | |