import streamlit as st
from langchain_community.vectorstores import FAISS
from langchain_huggingface import HuggingFaceEmbeddings
from transformers import pipeline

# --------------------------------------------------

# PAGE CONFIG

# --------------------------------------------------

st.set_page_config(
page_title="BibleGuard AI",
page_icon="✝️",
layout="wide"
)

# --------------------------------------------------

# EMBEDDINGS

# --------------------------------------------------

@st.cache_resource
def load_embeddings():

    return HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-MiniLM-L6-v2"
    )

# --------------------------------------------------

# FAISS

# --------------------------------------------------

@st.cache_resource
def load_vector_db():

```
embeddings = load_embeddings()

db = FAISS.load_local(
    "bible_faiss",
    embeddings,
    allow_dangerous_deserialization=True
)

return db
```

# --------------------------------------------------

# MODEL

# --------------------------------------------------

@st.cache_resource
def load_llm():

```
return pipeline(
    "text-generation",
    model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
    device_map="auto"
)
```

# --------------------------------------------------

# SAFETY

# --------------------------------------------------

def moderate_query(query):

```
blocked = [
    "rewrite bible to support racism",
    "christian terrorism",
    "religious violence",
    "hate muslims",
    "kill unbelievers",
    "religious supremacy"
]

q = query.lower()

for item in blocked:

    if item in q:
        return False

return True
```

# --------------------------------------------------

# QUERY EXPANSION

# --------------------------------------------------

def expand_query(question):

```
mapping = {

    "who was moses":
    "Moses prophet Israel Exodus Sinai",

    "who was jesus":
    "Jesus Christ Messiah Son of God",

    "forgiveness":
    "forgiveness mercy repentance sin",

    "salvation":
    "saved eternal life salvation",

    "faith":
    "faith trust God belief",

    "love":
    "love God neighbor charity"
}

q = question.lower()

for key in mapping:

    if key in q:
        return mapping[key]

return question
```

# --------------------------------------------------

# RETRIEVAL

# --------------------------------------------------

def retrieve_context(question):

```
db = load_vector_db()

retriever = db.as_retriever(
    search_type="mmr",
    search_kwargs={
        "k": 8,
        "fetch_k": 20
    }
)

docs = retriever.invoke(
    expand_query(question)
)

context = []
citations = []

for doc in docs:

    context.append(
        f"{doc.metadata['reference']}: {doc.page_content}"
    )

    citations.append(
        doc.metadata["reference"]
    )

return "\n\n".join(context), citations
```

# --------------------------------------------------

# PROMPT

# --------------------------------------------------

def build_prompt(
question,
context,
denomination
):

```
return f"""
```

You are a Christianity-focused AI Assistant.

Denomination:
{denomination}

Rules:

1. Use ONLY supplied scripture.
2. Never invent Bible verses.
3. Cite references.
4. Stay aligned with Biblical context.
5. Mention differing viewpoints when appropriate.
6. If uncertain say:
   'I cannot verify this from scripture.'

Scripture:

{context}

Question:

{question}

Answer:
"""

# --------------------------------------------------

# BIBLE QA

# --------------------------------------------------

def christian_assistant(
question,
denomination
):

```
if not moderate_query(question):

    return (
        "I cannot assist with hateful, violent, or harmful religious content.",
        []
    )

context, citations = retrieve_context(
    question
)

prompt = build_prompt(
    question,
    context,
    denomination
)

generator = load_llm()

response = generator(
    prompt,
    max_new_tokens=200,
    do_sample=False
)

answer = response[0]["generated_text"]

if "Answer:" in answer:
    answer = answer.split("Answer:")[-1].strip()

return answer, citations
```

# --------------------------------------------------

# PRAYER

# --------------------------------------------------

def generate_prayer(topic):

```
generator = load_llm()

prompt = f"""
```

Write a Christian prayer about:

{topic}

Keep it biblically sound.
"""

```
response = generator(
    prompt,
    max_new_tokens=200,
    do_sample=False
)

return response[0]["generated_text"]
```

# --------------------------------------------------

# SERMON

# --------------------------------------------------

def generate_sermon(topic):

```
generator = load_llm()

prompt = f"""
```

Write a Christian sermon about:

{topic}

Use biblical principles.
"""

```
response = generator(
    prompt,
    max_new_tokens=300,
    do_sample=False
)

return response[0]["generated_text"]
```

# --------------------------------------------------

# DEVOTIONAL

# --------------------------------------------------

def generate_devotional(topic):

```
generator = load_llm()

prompt = f"""
```

Write a Christian devotional about:

{topic}

Include:

1. Reflection
2. Encouragement
3. Prayer
   """

   response = generator(
   prompt,
   max_new_tokens=300,
   do_sample=False
   )

   return response[0]["generated_text"]

# --------------------------------------------------

# MEMORY

# --------------------------------------------------

if "messages" not in st.session_state:
st.session_state.messages = []

# --------------------------------------------------

# SIDEBAR

# --------------------------------------------------

with st.sidebar:

```
st.title("✝️ BibleGuard AI")

denomination = st.selectbox(
    "Denomination",
    [
        "Catholic",
        "Protestant",
        "Orthodox",
        "Non-Denominational"
    ]
)

st.markdown("---")

st.markdown("""
```

### Features

✅ Bible QA

✅ Scripture Citations

✅ Prayer Generator

✅ Sermon Generator

✅ Devotional Generator

✅ Safety Layer

✅ Denomination Awareness
""")

# --------------------------------------------------

# MAIN UI

# --------------------------------------------------

st.title("✝️ BibleGuard AI")

st.caption(
"Bible-grounded Christian AI Assistant powered by RAG"
)

tab1, tab2, tab3, tab4 = st.tabs(
[
"Bible QA",
"Prayer",
"Sermon",
"Devotional"
]
)

# --------------------------------------------------

# TAB 1

# --------------------------------------------------

with tab1:

```
question = st.chat_input(
    "Ask a Bible question..."
)

if question:

    answer, citations = christian_assistant(
        question,
        denomination
    )

    st.session_state.messages.append(
        {
            "question": question,
            "answer": answer
        }
    )

    st.markdown("### Answer")
    st.write(answer)

    st.markdown("### Citations")
    st.write(citations)
```

# --------------------------------------------------

# TAB 2

# --------------------------------------------------

with tab2:

```
prayer_topic = st.text_input(
    "Prayer Topic"
)

if st.button(
    "Generate Prayer"
):

    st.write(
        generate_prayer(
            prayer_topic
        )
    )
```

# --------------------------------------------------

# TAB 3

# --------------------------------------------------

with tab3:

```
sermon_topic = st.text_input(
    "Sermon Topic"
)

if st.button(
    "Generate Sermon"
):

    st.write(
        generate_sermon(
            sermon_topic
        )
    )
```

# --------------------------------------------------

# TAB 4

# --------------------------------------------------

with tab4:

```
devotional_topic = st.text_input(
    "Devotional Topic"
)

if st.button(
    "Generate Devotional"
):

    st.write(
        generate_devotional(
            devotional_topic
        )
    )
```

# --------------------------------------------------

# HISTORY

# --------------------------------------------------

st.markdown("---")
st.subheader("Conversation History")

for msg in st.session_state.messages:

```
st.markdown(
    f"**Q:** {msg['question']}"
)

st.markdown(
    f"**A:** {msg['answer']}"
)
```