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import streamlit as st
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import pyttsx3
# Set up the page configuration for a welcoming appearance
st.set_page_config(page_title="Grief and Loss Support Bot", page_icon="🕊️", layout="centered")
# Customizing the app style for a soothing and modern look
st.markdown("""
<style>
.css-1d391kg {
background-color: #F3F7F6;
}
.css-ffhzg2 {
font-size: 1.5em;
font-weight: 500;
color: #4C6D7D;
}
.stTextInput>div>div>input {
background-color: #D8E3E2;
}
.stButton>button {
background-color: #A9D0B6;
color: white;
border-radius: 5px;
border: none;
}
.stButton>button:hover {
background-color: #8FB79A;
}
.stTextInput>div>label {
color: #4C6D7D;
}
</style>
""", unsafe_allow_html=True)
# Title and introduction
st.title("Grief and Loss Support Bot 🕊️")
st.subheader("We are here for you. 💚 Your companion in tough times")
# Load the model and tokenizer for text generation
model_name = "microsoft/DialoGPT-medium"
try:
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
text_gen_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)
except Exception as e:
st.error(f"Error loading the conversational model: {e}")
# Initialize the TTS engine
try:
tts_engine = pyttsx3.init()
tts_engine.setProperty('rate', 150) # Adjust the speed of speech
tts_engine.setProperty('voice', tts_engine.getProperty('voices')[0].id) # Choose the first voice option
except Exception as e:
st.error(f"Error initializing the TTS engine: {e}")
# User input for conversation
user_input = st.text_input("Share what's on your mind...", placeholder="Type here...", max_chars=500)
if user_input:
# Generate a conversational response
try:
response = text_gen_pipeline(user_input, max_length=100, num_return_sequences=1)
response_text = response[0]['generated_text']
st.write("Bot's Response:")
st.write(response_text)
# Convert the response text to speech
if st.button("Play Response Audio"):
tts_engine.say(response_text)
tts_engine.runAndWait()
except Exception as e:
st.error(f"Error generating response: {e}")
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