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| import streamlit as st | |
| from transformers import T5ForConditionalGeneration, T5Tokenizer | |
| import torch | |
| import spacy | |
| import nltk | |
| #nltk.download('punkt') | |
| from nltk.tokenize import sent_tokenize | |
| # Load spaCy model | |
| nlp = spacy.load("en_core_web_sm") | |
| # Load T5 model and tokenizer | |
| model_name = "DevBM/t5-large-squad" | |
| model = T5ForConditionalGeneration.from_pretrained(model_name) | |
| tokenizer = T5Tokenizer.from_pretrained(model_name) | |
| # Function to extract keywords using spaCy | |
| def extract_keywords(text): | |
| doc = nlp(text) | |
| keywords = set() | |
| # Extract named entities | |
| for entity in doc.ents: | |
| keywords.add(entity.text) | |
| # Extract nouns and proper nouns | |
| for token in doc: | |
| if token.pos_ in ["NOUN", "PROPN"]: | |
| keywords.add(token.text) | |
| return list(keywords) | |
| # Function to map keywords to sentences | |
| def map_keywords_to_sentences(text, keywords): | |
| sentences = sent_tokenize(text) | |
| keyword_sentence_mapping = {} | |
| for keyword in keywords: | |
| for i, sentence in enumerate(sentences): | |
| if keyword in sentence: | |
| # Combine current sentence with surrounding sentences for context | |
| start = max(0, i-1) | |
| end = min(len(sentences), i+2) | |
| context = ' '.join(sentences[start:end]) | |
| if keyword not in keyword_sentence_mapping: | |
| keyword_sentence_mapping[keyword] = context | |
| else: | |
| keyword_sentence_mapping[keyword] += ' ' + context | |
| return keyword_sentence_mapping | |
| # Function to generate questions | |
| def generate_question(context, answer): | |
| input_text = f"<context> {context} <answer> {answer}" | |
| input_ids = tokenizer.encode(input_text, return_tensors='pt') | |
| outputs = model.generate(input_ids) | |
| question = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return question | |
| # Streamlit interface | |
| st.title("Question Generator from Text") | |
| text = st.text_area("Enter text here:") | |
| if st.button("Generate Questions"): | |
| if text: | |
| keywords = extract_keywords(text) | |
| keyword_sentence_mapping = map_keywords_to_sentences(text, keywords) | |
| st.subheader("Generated Questions:") | |
| for keyword, context in keyword_sentence_mapping.items(): | |
| question = generate_question(context, keyword) | |
| st.write(f"**Context:** {context}") | |
| st.write(f"**Answer:** {keyword}") | |
| st.write(f"**Question:** {question}") | |
| st.write("---") | |
| else: | |
| st.write("Please enter some text to generate questions.") | |