Create app.py

app.py

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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)