# Install necessary libraries
!pip install streamlit groq deep-translator

import streamlit as st
from groq import Groq
from deep_translator import GoogleTranslator

import subprocess
import threading

# Insert your Groq API key here
API_KEY = "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"

# Initialize Groq client
client = Groq(api_key=API_KEY)

st.set_page_config(page_title="Krishna", page_icon="https://res.cloudinary.com/dvlgixtg8/image/upload/v1739472351/Krishna-avatar.png")

# Define a system prompt for fine-tuning responses
system_prompt = """
Bot Identity
Namaste! I am Neetu, your celestial guide to Hindu astrology and mythology. My purpose is to illuminate the divine wisdom of the cosmos, revealing how planetary movements and ancient scriptures shape our destiny, karma, and dharma.
"""

# Initialize session state for chat history
if "messages" not in st.session_state:
    st.session_state.messages = [
        {"role": "system", "content": system_prompt}  # Add system prompt at the start
    ]

# Translate text function
def translate_text(text, lang='en'):
    """Translate text to the desired language."""
    try:
        if lang == 'hi':  # Translate to Hinglish (Hindi + English)
            translated = GoogleTranslator(source='auto', target='hi').translate(text)
        else:  # Default to English
            translated = GoogleTranslator(source='auto', target='en').translate(text)
        return translated
    except Exception as e:
        st.error(f"Error in translation: {e}")
        return text

# Display chat messages from session
st.title("Neetu – Unlock the Secrets of Hindu Astrology & Mythology ✨🔱")  

# Description  
st.write(  
    "Namaste! I am your celestial guide to **Hindu astrology (Jyotish) and ancient mythology**. "
    "Ask me anything about **planetary influences, zodiac signs, karmic lessons, and divine stories from sacred texts**.\n\n"  
    "Let us explore the cosmic dance of planets and the wisdom of the Vedas, Puranas, and epics – speak to me as a seeker of truth! 🔮✨"  
)  

# Language selector
language = st.radio("Choose your language", ('English', 'Hinglish'))

# User input field
user_input = st.chat_input("Type your message...")

if user_input:
    # Translate user input to the selected language (English or Hinglish)
    translated_input = translate_text(user_input, lang='hi' if language == 'Hinglish' else 'en')

    # Append translated user message to session
    st.session_state.messages.append({"role": "user", "content": translated_input})

    with st.chat_message("user", avatar="🔮"):
        st.write(translated_input)

    # Get AI response (in the same language as input)
    with st.chat_message("assistant", avatar="https://static.vecteezy.com/system/resources/previews/050/754/028/non_2x/om-ohm-buddhist-and-hindu-religions-symbol-of-god-creation-black-icon-isolated-on-white-background-eps10-graphic-design-element-vector.jpg"):
        message_placeholder = st.empty()
        response = ""

        completion = client.chat.completions.create(
            model="llama-3.3-70b-versatile",
            messages=st.session_state.messages,  # Send full chat history, including system prompt
            temperature=1,
            max_completion_tokens=1024,
            top_p=1,
            stream=True,
            stop=None,
        )

        for chunk in completion:
            response_chunk = chunk.choices[0].delta.content or ""
            response += response_chunk
            message_placeholder.write(response)

        # Translate the assistant response back to the selected language
        translated_response = translate_text(response, lang='hi' if language == 'Hinglish' else 'en')

        # Append assistant response to session
        st.session_state.messages.append({"role": "assistant", "content": translated_response})

        with st.chat_message("assistant", avatar="https://static.vecteezy.com/system/resources/previews/050/754/028/non_2x/om-ohm-buddhist-and-hindu-religions-symbol-of-god-creation-black-icon-isolated-on-white-background-eps10-graphic-design-element-vector.jpg"):
            st.write(translated_response)

# Start Streamlit app using subprocess
def start_streamlit():
    subprocess.run(["streamlit", "run", "your_streamlit_file.py", "--server.headless=true", "--server.port=8501"])

# Start Streamlit app in background
streamlit_thread = threading.Thread(target=start_streamlit)
streamlit_thread.start()