app.py

import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from gtts import gTTS
from pytube import Search
import random
import os
import time

# Load pretrained models
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
emotion_classifier = pipeline("text-classification", model="bhadresh-savani/distilbert-base-uncased-emotion", return_all_scores=True)

# Streamlit page configuration
st.set_page_config(page_title="Grief and Loss Support Bot 🌿", page_icon="🌿", layout="centered")
st.markdown("<style>.css-1d391kg { background-color: #F3F7F6; }</style>", unsafe_allow_html=True)

# Title and welcome text
st.title("Grief and Loss Support Bot 🌿")
st.subheader("Your compassionate companion in tough times 💚")

# Sidebar for additional features
with st.sidebar:
    st.header("🧘 Guided Meditation")
    if st.button("Play Meditation"):
        meditation_audio = "meditation.mp3"
        if not os.path.exists(meditation_audio):
            tts = gTTS("Take a deep breath. Relax and let go of any tension...", lang='en')
            tts.save(meditation_audio)
        st.audio(meditation_audio, format="audio/mp3")
    
    st.header("📖 Short Comforting Story")
    story_theme = st.selectbox("Choose a theme for your story:", ["courage", "healing", "hope"])
    if st.button("Generate Story"):
        story_prompt = f"Tell me a comforting story about {story_theme}."
        input_ids = tokenizer.encode(story_prompt, return_tensors='pt')
        story_ids = model.generate(input_ids, max_length=150, temperature=0.8, repetition_penalty=1.1)
        story = tokenizer.decode(story_ids[0], skip_special_tokens=True)
        st.text_area("Here's your story:", story, height=200)

# User input section
user_input = st.text_input("Share what's on your mind...", placeholder="Type here...", max_chars=500)

if 'previous_responses' not in st.session_state:
    st.session_state.previous_responses = []
if 'badges' not in st.session_state:
    st.session_state.badges = []

# Generate empathetic response
def generate_response(user_input):
    input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
    chat_history_ids = model.generate(input_ids, max_length=150, temperature=0.7, top_k=50, repetition_penalty=1.2)
    response = tokenizer.decode(chat_history_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
    return response

# Analyze user input for emotional tone
def get_emotion(user_input):
    emotions = emotion_classifier(user_input)
    emotions_sorted = sorted(emotions[0], key=lambda x: x['score'], reverse=True)
    return emotions_sorted[0]['label']

# Provide a response if user input is provided
if user_input:
    emotion = get_emotion(user_input)
    response = generate_response(user_input)
    
    # Display the bot's response
    st.session_state.previous_responses.append(response)
    st.text_area("Bot's Response:", response, height=250)
    
    # Assign motivational badges
    if emotion in ["joy", "optimism"]:
        badge = "🌟 Positivity Badge"
        if badge not in st.session_state.badges:
            st.session_state.badges.append(badge)
            st.success(f"Congratulations! You've earned a {badge}!")
    
    # Suggest coping activities
    st.info("🎨 Try a New Activity")
    activities = ["exercise", "yoga", "journaling", "painting", "meditation"]
    selected_activity = st.selectbox("Pick an activity:", activities)
    
    # Fetch YouTube video suggestions
    if st.button("Find Videos"):
        search = Search(selected_activity)
        search_results = search.results[:3]
        if not search_results:
            st.write(f"No results found for '{selected_activity}'.")
        else:
            for video in search_results:
                st.write(f"[{video.title}]({video.watch_url})")

    # Crisis resources
    if any(word in user_input.lower() for word in ["suicide", "help", "depressed"]):
        st.warning("Please reach out to a crisis hotline for immediate support.")
        st.write("[Find emergency resources here](https://www.helpguide.org/find-help.htm)")

    # Generate audio response
    tts = gTTS(response, lang='en')
    audio_file = "response.mp3"
    tts.save(audio_file)
    st.audio(audio_file, format="audio/mp3")

# Display badges and achievements
if st.session_state.badges:
    st.sidebar.header("🏅 Achievements")
    for badge in st.session_state.badges:
        st.sidebar.write(badge)