import gradio as gr
import pandas as pd
import uuid
import shutil
import edge_tts
import re
from sentence_transformers import SentenceTransformer, util
import asyncio

# Load Gita dataset
df = pd.read_csv("bhagavad_gita.csv")

# Load model
model = SentenceTransformer("all-MiniLM-L6-v2")
verse_embeddings = model.encode(df['meaning_in_english'].tolist(), convert_to_tensor=True)

# Background music path
bg_music_path = "krishna_bg_music.mp3"

def shorten_explanation(text, max_sentences=2):
    return '. '.join(text.split('. ')[:max_sentences]).strip() + "."

def clean_english(text):
    return re.sub(r'[^\x00-\x7F]+', ' ', text)

def generate_voice_sync(text):
    voice = "en-IN-PrabhatNeural"
    filename = f"{uuid.uuid4()}.mp3"
    communicate = edge_tts.Communicate(text, voice=voice)
    loop = asyncio.new_event_loop()
    asyncio.set_event_loop(loop)
    loop.run_until_complete(communicate.save(filename))
    loop.close()
    return filename

def get_unique_bgm():
    unique_path = f"bgm_{uuid.uuid4()}.mp3"
    shutil.copy(bg_music_path, unique_path)
    return unique_path

def versewise_bot(question, play_music):
    if not question.strip():
        return "Please ask a question.", None, None

    query_embedding = model.encode(question, convert_to_tensor=True)
    similarity_scores = util.pytorch_cos_sim(query_embedding, verse_embeddings)[0]
    idx = similarity_scores.argmax().item()
    verse = df.iloc[idx]

    sanskrit = verse['verse_in_sanskrit']
    translation = verse['translation_in_english']
    explanation = shorten_explanation(verse['meaning_in_english'])
    verse_number = verse['verse_number']

    reply = f"""📖 *Bhagavad Gita {verse_number}*

🕉 "{sanskrit[:60]}..."

*"{translation}"*

🕊 {explanation}

🌼 Stay strong — Krishna walks with you."""

    voice_text = f"{clean_english(translation)}. {clean_english(explanation)}"
    audio_path = generate_voice_sync(voice_text)

    music = get_unique_bgm() if play_music else None
    return reply, audio_path, music

def get_quote_of_the_day():
    verse = df.sample(1).iloc[0]
    sanskrit = verse['verse_in_sanskrit']
    translation = verse['translation_in_english']
    verse_number = verse['verse_number']
    return f"""<div style="font-size:1.1em;padding:10px 0; color:#000;"><b>Quote of the Day (Gita {verse_number}):</b><br>
    <span style="color:#000;"><i>{sanskrit[:60]}...</i></span><br>
    <span style="color:#000;">"{translation}"</span></div>"""

custom_css = """
body, .gradio-container {
  background-image: url('https://static.vecteezy.com/system/resources/previews/022/592/272/non_2x/image-of-divine-beautiful-closed-eyes-blue-colored-krishna-generative-ai-free-photo.jpeg');
  background-size: cover;
  background-repeat: no-repeat;
  background-position: center;
}
.gradio-container, .gradio-interface {
  background-color: rgba(255,255,255,0.90) !important;
  border-radius: 18px;
  padding: 25px;
  max-width: 760px;
  box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2);
}
.gradio-container h1, .gradio-container .prose, .gradio-container .prose span, .gradio-container .prose p, .gradio-container b, .gradio-container i {
  color: #000 !important;
}
@media only screen and (max-width: 768px) {
  .gradio-container {
    width: 99% !important;
    padding: 10px !important;
    max-width: 100vw !important;
  }
}
input[type="text"], textarea {
  font-size: 16px !important;
}
"""

interface = gr.Interface(
    fn=versewise_bot,
    inputs=[
        gr.Textbox(label="Ask Krishna", placeholder="Why am I struggling in life?", lines=2),
        gr.Checkbox(label="Play Background Music", value=True)
    ],
    outputs=[
        gr.Textbox(label="🧘‍♂ Krishna's Answer"),
        gr.Audio(label="🔊 Listen to Krishna's Voice"),
        gr.Audio(label="🎶 Background Music"),
    ],
    title="🕉 VerseWise - Divine Wisdom from the Gita",
    description='<span style="color:#000;">Ask any question, and receive a Gita verse with Krishna’s loving wisdom.</span>',
    article=get_quote_of_the_day(),
    allow_flagging="never",
    theme="soft",
    css=custom_css,
    fill_width=True
)

if __name__ == "__main__":
    interface.launch(show_error=True)