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| import streamlit as st | |
| from datasets import load_dataset | |
| # from transformers import T5Tokenizer, T5ForConditionalGeneration | |
| from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline, AutoModelForQuestionAnswering | |
| import torch | |
| # model_path = "./kaggle-3/working/bert_qa" | |
| model_path = "./flan_t5_qa_60" | |
| tokenizer_new = AutoTokenizer.from_pretrained(model_path) | |
| model_new = AutoModelForQuestionAnswering.from_pretrained(model_path) | |
| def ask(question: str, context: str) -> str: | |
| inputs = tokenizer_new(question, context, max_length=384, | |
| truncation="only_second", padding="max_length", return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model_new(**inputs) | |
| answer_start_index = outputs.start_logits.argmax() | |
| answer_end_index = outputs.end_logits.argmax() | |
| predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] | |
| answer = tokenizer_new.decode(predict_answer_tokens) | |
| return answer | |
| return f"Question: '{question}'\nAnswer: {answer}" | |
| # print(ask('What God created at first', 'Genesis 1:1 In the beginning God created the heaven and the earth.')) | |
| # Streamlit App | |
| st.set_page_config(page_title="Bible Q&A Bot", page_icon="📖", layout="centered") | |
| st.title("📖 Bible Q&A Bot") | |
| st.markdown("## Ask any question about the Bible and get scripturally grounded answers.") | |
| st.write("‼️Only english🇺🇸 language provided‼️") | |
| # User input | |
| query = st.text_input("Enter your question:") | |
| clear_button = st.button("Clear") | |
| if clear_button: | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| st.markdown('### Choose option to provide context') | |
| option = st.radio("Choose how to provide context:", ("Manually", "Select Bible Verse"), label_visibility="collapsed") | |
| def print_answer(question, context, answer): | |
| if context.isascii() and question.isascii(): | |
| st.markdown("### ❓Question❓") | |
| st.write(question) | |
| st.markdown("### 📖Context📖") | |
| st.write(context) | |
| st.markdown("### ✅Answer✅") | |
| st.write(answer) | |
| else: | |
| st.error("Please ensure both the question and context are in English.") | |
| import pandas as pd | |
| # bible = pd.read_json("bible-dpo.json") | |
| bible = pd.read_json("hf://datasets/nbeerbower/bible-dpo/bible-dpo.json") | |
| books = list(bible['book'].unique()) | |
| v_by_b_c = bible.groupby(by=['book', 'chapter']).size().to_dict() | |
| if option == "Manually": | |
| context = st.text_area("Enter the context (optional):") | |
| submit_button = st.button("Get Answer") | |
| if submit_button and query: | |
| with st.spinner("Searching Scripture..."): | |
| answer = ask(query, context) | |
| print_answer(query, context, answer) | |
| elif option == "Select Bible Verse": | |
| book_name = st.selectbox("Select the book name:", [""] + books) | |
| if book_name: | |
| max_chapter = len(bible[bible["book"] == book_name].groupby('chapter')) | |
| chapter = st.selectbox("Select the chapter:", [""] + list(range(1, max_chapter + 1))) | |
| else: | |
| chapter = st.selectbox("Select the chapter:", [""]) | |
| if chapter: | |
| max_verse = v_by_b_c.get((book_name, int(chapter)), 0) | |
| verse = st.selectbox("Enter the verse:", [""] + list(range(1, max_verse + 1))) | |
| else: | |
| verse = st.selectbox("Enter the verse:", [""]) | |
| fetch_context_button = st.button("Fetch Verse") | |
| if query and fetch_context_button and book_name and chapter and verse: | |
| context = bible[ | |
| (bible["book"] == book_name) & | |
| (bible['chapter'] == chapter) & | |
| (bible['verse'] == verse) | |
| ]['text'].values[0] | |
| with st.spinner("Searching Scripture..."): | |
| answer = ask(query, context) | |
| print_answer(query, context, answer) |