app.py

# app.py
!pip install transformers

import streamlit as st
from transformers import pipeline

# Function to load Hugging Face models
def load_model():
    return pipeline("conversational", model="alpindale/goliath-120b")

# Page 1: Welcome and Chatbot
def page_welcome():
    st.title("Welcome to Your Virtual Therapist")
    st.write("Feel free to chat with our virtual therapist!")

    # Load the provided Hugging Face chatbot model
    chatbot_model = load_model()

    user_input = st.text_input("You: ")
    if user_input:
        response = chatbot_model(user_input, max_length=50, num_return_sequences=1)[0]['generated_text']
        st.text_area("Therapist:", response, height=100)

# Page 2: Journaling
def page_journaling():
    st.title("Journaling Session")
    st.write("Answer the following questions based on your preferences:")

    # Add your journaling questions here
    journaling_question = st.text_area("Question:", "How was your day?")

    # Process the user's response as needed

# Page 3: Breathing Exercises
def page_breathing_exercises():
    st.title("Breathing Exercises")
    st.write("Start your meditation with the breathing exercise timer:")

    # Add a timer or interactive element for breathing exercises

# Main App
def main():
    st.sidebar.title("Navigation")
    selection = st.sidebar.radio("Go to", ["Welcome & Chatbot", "Journaling", "Breathing Exercises"])

    if selection == "Welcome & Chatbot":
        page_welcome()
    elif selection == "Journaling":
        page_journaling()
    elif selection == "Breathing Exercises":
        page_breathing_exercises()

if __name__ == "__main__":
    main()