Spaces:
Sleeping
Sleeping
File size: 2,588 Bytes
99220ed |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
import streamlit as st
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
import spacy
import nltk
from b import b
#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.")
|