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