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import streamlit as st
import wikipedia
from haystack.document_stores import InMemoryDocumentStore
from haystack.utils import clean_wiki_text, convert_files_to_docs
from haystack.nodes import TfidfRetriever, FARMReader
from haystack.pipelines import ExtractiveQAPipeline
from main import print_qa, QuestionGenerator
def main():
# Set the Streamlit app title
st.title("Question Generation using Haystack and Streamlit")
# Select the input type
inputs = ["Input Paragraph", "Wikipedia Examples"]
input_type = st.selectbox("Select an input type:", inputs)
# Initialize wiki_text as an empty string
wiki_text = ""
# Handle different input types
if input_type == "Input Paragraph":
# Allow user to input text paragraph
wiki_text = st.text_area("Input paragraph:", height=200)
elif input_type == "Wikipedia Examples":
# Define topics for selection
topics = ["Deep Learning", "Machine Learning"]
selected_topic = st.selectbox("Select a topic:", topics)
# Retrieve Wikipedia content based on the selected topic
if selected_topic:
wiki = wikipedia.page(selected_topic)
wiki_text = wiki.content
# Display the retrieved Wikipedia content (optional)
st.text_area("Retrieved Wikipedia content:", wiki_text, height=200)
# Preprocess the input text
wiki_text = clean_wiki_text(wiki_text)
# Allow user to specify the number of questions to generate
num_questions = st.slider("Number of questions to generate:", min_value=1, max_value=20, value=5)
# Allow user to specify the model to use
model_options = ["deepset/roberta-base-squad2", "deepset/roberta-base-squad2-distilled", "bert-large-uncased-whole-word-masking-squad2", "deepset/flan-t5-xl-squad2"]
model_name = st.selectbox("Select model:", model_options)
# Button to generate questions
if st.button("Generate Questions"):
document_store = InMemoryDocumentStore()
# Convert the preprocessed text into a document
document = {"content": wiki_text}
document_store.write_documents([document])
# Initialize a TfidfRetriever
retriever = TfidfRetriever(document_store=document_store)
# Initialize a FARMReader with the selected model
reader = FARMReader(model_name_or_path=model_name, use_gpu=False)
# Initialize the question generation pipeline
pipe = ExtractiveQAPipeline(reader, retriever)
# Initialize the QuestionGenerator
qg = QuestionGenerator()
# Generate multiple-choice questions
qa_list = qg.generate(
wiki_text,
num_questions=num_questions,
answer_style='multiple_choice'
)
# Display the generated questions and answers
st.header("Generated Questions and Answers:")
for idx, qa in enumerate(qa_list):
# Display the question
st.write(f"Question {idx + 1}: {qa['question']}")
# Display the answer options
if 'answer' in qa:
for i, option in enumerate(qa['answer']):
correct_marker = "(correct)" if option["correct"] else ""
st.write(f"Option {i + 1}: {option['answer']} {correct_marker}")
# Add a separator after each question-answer pair
st.write("-" * 40)
# Run the Streamlit app
if __name__ == "__main__":
main()
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