Spaces:
Running
Running
File size: 2,784 Bytes
f7842f6 |
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 74 75 76 |
import streamlit as st
import uuid
from load_models import initialize_wikiapi
from functools import lru_cache
class QuestionGenerationError(Exception):
"""Custom exception for question generation errors."""
pass
def get_session_id():
if 'session_id' not in st.session_state:
st.session_state.session_id = str(uuid.uuid4())
return st.session_state.session_id
def initialize_state(session_id):
if 'session_states' not in st.session_state:
st.session_state.session_states = {}
if session_id not in st.session_state.session_states:
st.session_state.session_states[session_id] = {
'generated_questions': [],
# add other state variables as needed
}
return st.session_state.session_states[session_id]
def get_state(session_id):
return st.session_state.session_states[session_id]
def set_state(session_id, key, value):
st.session_state.session_states[session_id][key] = value
# Info Section
def display_info():
st.sidebar.title("Information")
st.sidebar.markdown("""
### Question Generator System
This system is designed to generate questions based on the provided context. It uses various NLP techniques and models to:
- Extract keywords from the text
- Map keywords to sentences
- Generate questions
- Provide multiple choice options
- Assess the quality of generated questions
#### Key Features:
- **Keyword Extraction:** Combines RAKE, TF-IDF, and spaCy for comprehensive keyword extraction.
- **Question Generation:** Utilizes a pre-trained T5 model for generating questions.
- **Options Generation:** Creates contextually relevant multiple-choice options.
- **Question Assessment:** Scores questions based on relevance, complexity, and spelling correctness.
- **Feedback Collection:** Allows users to rate the generated questions and provides statistics on feedback.
#### Customization Options:
- Number of beams for question generation
- Context window size for mapping keywords to sentences
- Number of questions to generate
- Additional display elements (context, answer, options, entity link, QA scores)
#### Outputs:
- Generated questions with multiple-choice options
- Download options for CSV and PDF formats
- Visualization of overall scores
""")
# Function to perform entity linking using Wikipedia API
@lru_cache(maxsize=128)
def entity_linking(keyword):
user_agent, wiki_wiki = initialize_wikiapi()
page = wiki_wiki.page(keyword)
if page.exists():
return page.fullurl
return None
|