import streamlit as st
import pandas as pd
from sentence_transformers import SentenceTransformer, util
from gtts import gTTS
import tempfile
import os

# Load the Gita dataset
@st.cache_data
def load_data():
    return pd.read_csv("Bhagwad_Gita.csv")

data = load_data()

# Load the embedding model
@st.cache_resource
def load_model():
    return SentenceTransformer('all-MiniLM-L6-v2')

model = load_model()

# Preprocess verses and create embeddings
@st.cache_data
def get_embeddings(data):
    verses = data['Verse'].astype(str).tolist()
    embeddings = model.encode(verses, convert_to_tensor=True)
    return verses, embeddings

verses, verse_embeddings = get_embeddings(data)

# App Title
st.title("🙏 GeetaGPT – Divine Wisdom from the Bhagavad Gita")

# User Input
user_question = st.text_input("Ask your question to Lord Krishna:")

if user_question:
    # Embed the question
    question_embedding = model.encode(user_question, convert_to_tensor=True)

    # Compute cosine similarities
    scores = util.pytorch_cos_sim(question_embedding, verse_embeddings)[0]
    best_idx = scores.argmax().item()

    # Get the best matching verse
    matched_verse = verses[best_idx]
    chapter = data.iloc[best_idx]['Chapter']
    verse_number = data.iloc[best_idx]['Verse Number']

    # Greet and show result
    greeting = "🕉️ Jai Shri Krishna!\n\n"
    st.markdown(f"{greeting}**Chapter {chapter}, Verse {verse_number}:**\n\n*{matched_verse}*")

    # Optional: Generate audio
    if st.checkbox("🔊 Hear it aloud"):
        tts = gTTS(text=matched_verse, lang='en')
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
            tts.save(fp.name)
            st.audio(fp.name, format="audio/mp3")
            os.remove(fp.name)