| import streamlit as st | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForQuestionAnswering | |
| tokenizer = AutoTokenizer.from_pretrained("https://huggingface.co/spaces/highdeff/highdeffrepo/tree/main") | |
| model = AutoModelForQuestionAnswering.from_pretrained("./trained.pt") | |
| def get_answer(context, question): | |
| encoding = tokenizer.encode_plus(question, context, return_tensors='pt') | |
| input_ids = encoding['input_ids'] | |
| attention_mask = encoding['attention_mask'] | |
| start_scores, end_scores = model(input_ids, attention_mask=attention_mask) | |
| start_index = torch.argmax(start_scores) | |
| end_index = torch.argmax(end_scores) | |
| answer_tokens = input_ids[0][start_index:end_index+1] | |
| answer = tokenizer.decode(answer_tokens) | |
| return answer | |
| st.title("Question Answering with Transformers") | |
| context = st.text_area("Context:", "Enter the context here...") | |
| question = st.text_input("Question:", "Enter your question here...") | |
| if st.button("Answer"): | |
| if not context or not question: | |
| st.error("Please provide both a context and a question.") | |
| else: | |
| answer = get_answer(context, question) | |
| st.success(f"Answer: {answer}") | |