| from transformers import T5Tokenizer, T5Model, T5ForConditionalGeneration, T5TokenizerFast, TFT5ForConditionalGeneration, FlaxT5ForConditionalGeneration |
| import evaluate |
| import torch |
| import torch.nn as nn |
| import pandas as pd |
| import gradio as gr |
| import requests |
|
|
| Q_LEN = 256 |
|
|
| model_name = 'PRAli22/t5-base-question-answering-system' |
| tokenizer = T5TokenizerFast.from_pretrained(model_name) |
| model = T5ForConditionalGeneration.from_pretrained(model_name) |
|
|
| def predict_answer(context, question, ref_answer=None): |
| inputs = tokenizer(question, context, max_length=Q_LEN, padding="max_length", truncation=True, add_special_tokens=True) |
|
|
| input_ids = torch.tensor(inputs["input_ids"], dtype=torch.long).unsqueeze(0) |
| attention_mask = torch.tensor(inputs["attention_mask"], dtype=torch.long).unsqueeze(0) |
|
|
| outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask) |
|
|
| predicted_answer = tokenizer.decode(outputs.flatten(), skip_special_tokens=True) |
|
|
| if ref_answer: |
| |
| bleu = evaluate.load("google_bleu") |
| score = bleu.compute(predictions=[predicted_answer], |
| references=[ref_answer]) |
|
|
| print("Context: \n", context) |
| print("\n") |
| print("Question: \n", question) |
| return { |
| "Reference Answer: ": ref_answer, |
| "Predicted Answer: ": predicted_answer, |
| "BLEU Score: ": score |
| } |
| else: |
| return predicted_answer |
|
|
| css_code='body{background-image:url("https://media.istockphoto.com/id/1256252051/vector/people-using-online-translation-app.jpg?s=612x612&w=0&k=20&c=aa6ykHXnSwqKu31fFR6r6Y1bYMS5FMAU9yHqwwylA94=");}' |
|
|
| demo = gr.Interface( |
| fn=predict_answer, |
| inputs=[ |
| gr.Textbox(label="text", placeholder="Enter the text "), |
| gr.Textbox(label="question", placeholder="Enter the question") |
| ], |
| outputs=gr.Textbox(label="answer"), |
| title="Question Answering System", |
| description= "This is Question Answering System, it takes a text and question in English as inputs and returns it's answer", |
| css = css_code |
| ) |
|
|
| demo.launch() |
