import streamlit as st
from llama_cpp import Llama
from gtts import gTTS
from io import BytesIO
import random
import requests
import yt_dlp

# Initialize the empathy bot using llama_cpp
llm = Llama.from_pretrained(
    repo_id="featherless-ai-quants/HyunCello-KULLM3-empathy-v1.0-GGUF",
    filename="HyunCello-KULLM3-empathy-v1.0-IQ4_XS.gguf",
)

# Function to process the user's input and generate a dynamic response
def generate_dynamic_response(user_input):
    # Generate an empathetic response using the Llama model
    response = llm.create_chat_completion(
        messages=[{"role": "user", "content": user_input}]
    )
    empathetic_response = response['choices'][0]['message']['content']

    # Possible activities to suggest
    activities = {
        "relaxation": [
            "Deep Breathing Exercises",
            "Guided Meditation",
            "Mindfulness Breathing"
        ],
        "creative": [
            "Journaling your thoughts",
            "Drawing or doodling",
            "Writing a poem"
        ],
        "exercise": [
            "Try some yoga",
            "Go for a walk outside",
            "Stretching exercises to release tension"
        ],
        "self-care": [
            "Take a warm bath",
            "Listen to calming music",
            "Treat yourself with some relaxation time"
        ]
    }

    # Logic to determine context of user input (e.g., stress, sadness, etc.)
    if "sad" in user_input or "overwhelmed" in user_input:
        suggested_activity = random.choice(activities["relaxation"])
    elif "bored" in user_input or "unmotivated" in user_input:
        suggested_activity = random.choice(activities["creative"])
    elif "stressed" in user_input or "tired" in user_input:
        suggested_activity = random.choice(activities["exercise"])
    else:
        suggested_activity = random.choice(activities["self-care"])

    # Search YouTube for relevant videos based on the activity suggestion
    video_keywords = suggested_activity
    video_results = search_youtube_videos(video_keywords)

    return empathetic_response, suggested_activity, video_results


def search_youtube_videos(query):
    ydl_opts = {
        'quiet': True,
        'extract_flat': True,
        'noplaylist': True,
        'max_results': 3,
    }

    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        result = ydl.extract_info(f"ytsearch:{query}", download=False)
        videos = result.get("entries", [])
        video_links = [(video['title'], video['url']) for video in videos]
    
    return video_links



# Streamlit UI
st.title("Grief and Loss Support Bot 🌿")
st.markdown("Your compassionate companion in tough times 💚")
user_input = st.text_input("Share what's on your mind...")

if user_input:
    empathetic_response, suggested_activity, video_results = generate_dynamic_response(user_input)

    # Display the empathetic response
    st.write(f"**Bot's Response:**")
    st.write(empathetic_response)

    # Suggest an activity
    st.write(f"Here's a suggestion to help you cope:")
    st.write(f"**Activity to try:** {suggested_activity}")

    # Display YouTube video suggestions
    if video_results:
        st.write("Here are some YouTube videos that may help:")
        for title, link in video_results:
            st.write(f"[{title}]({link})")

    # Generate and play an audio response using gTTS
    tts = gTTS(empathetic_response, lang='en')
    audio_file = BytesIO()
    tts.save(audio_file)
    audio_file.seek(0)
    st.audio(audio_file, format="audio/mp3", use_container_width=True)