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from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
import gradio as gr
import torch
model = AutoModelForQuestionAnswering.from_pretrained("i0xs0/Fine-Tuned-XLM-Question-Answering")
tokenizer = AutoTokenizer.from_pretrained("i0xs0/Fine-Tuned-XLM-Question-Answering")
def generate_answer(question, context):
inputs = tokenizer.encode_plus(question, context, add_special_tokens=True, return_tensors="pt")
input_ids = inputs["input_ids"].tolist()[0]
outputs = model(**inputs)
answer_start_scores = outputs.start_logits
answer_end_scores = outputs.end_logits
answer_start = torch.argmax(answer_start_scores)
answer_end = torch.argmax(answer_end_scores) + 1
answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
return answer
iface = gr.Interface(fn=generate_answer,
inputs=[gr.Textbox(lines=2, placeholder="Enter Question Here..."),
gr.Textbox(lines=5, placeholder="Enter Context Here...", label="Context")],
outputs=gr.Textbox(lines=5),
title="Question Answering",
description="Type in your question and Context, and the system will provide you with an answer.")
iface.launch() |