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from transformers import AutoTokenizer, AutoModelForQuestionAnswering
import gradio as gr
import torch, torchvision
# Load model and tokenizer
model_name = "mbwolff/distilbert-base-uncased-finetuned-squad"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
def answer_question(question, context):
"""
Answers a question based on a given 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
# Define Gradio interface
iface = gr.Interface(
fn=answer_question,
inputs=[
# gr.inputs.Textbox(lines=2, placeholder="Enter your question here..."),
# gr.inputs.Textbox(lines=5, placeholder="Enter the context here...")
gr.Textbox(label="Enter your question here...", lines=2),
gr.Textbox(label="Enter the context here...", lines=5)
],
outputs="text",
title="Question Answering Chatbot",
description="Ask a question and provide a context, and the chatbot will try to answer it."
)
if __name__ == "__main__":
iface.launch() |