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# app.py
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()
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