app.py

import streamlit as st
import os
import pandas as pd
from streamlit_chat import message
from streamlit_extras.colored_header import colored_header
from streamlit_extras.add_vertical_space import add_vertical_space
from streamlit_mic_recorder import speech_to_text
from model_pipelineV2 import ModelPipeLine
from q_learning_chatbot import QLearningChatbot

from gtts import gTTS
from io import BytesIO
st.set_page_config(page_title="PeacePal") 
#image to the sidebar
#image_path = os.path.join('images', 'sidebar.jpg')
#st.sidebar.image(image_path, use_column_width=True)

st.title('PeacePal 🌱')

mdl = ModelPipeLine()
# Now you can access the retriever attribute of the ModelPipeLine instance
retriever = mdl.retriever

final_chain = mdl.create_final_chain()

# Define states and actions
states = [
    "Negative",
    "Moderately Negative",
    "Neutral",
    "Moderately Positive",
    "Positive",
]

# Initialize Q-learning chatbot and mental health classifier
chatbot = QLearningChatbot(states)

# Function to display Q-table
def display_q_table(q_values, states):
    q_table_dict = {"State": states}
    q_table_df = pd.DataFrame(q_table_dict)
    return q_table_df

def text_to_speech(text):
    # Use gTTS to convert text to speech
    tts = gTTS(text=text, lang="en")
    # Save the speech as bytes in memory
    fp = BytesIO()
    tts.write_to_fp(fp)
    return fp


def speech_recognition_callback():
    # Ensure that speech output is available
    if st.session_state.my_stt_output is None:
        st.session_state.p01_error_message = "Please record your response again."
        return

    # Clear any previous error messages
    st.session_state.p01_error_message = None

    # Store the speech output in the session state
    st.session_state.speech_input = st.session_state.my_stt_output 

## generated stores AI generated responses
if 'generated' not in st.session_state:
    st.session_state['generated'] = ["I'm your Mental health Assistant, How may I help you?"]
## past stores User's questions
if 'past' not in st.session_state:
    st.session_state['past'] = ['Hi!']

# Initialize memory
if "entered_text" not in st.session_state:
    st.session_state.entered_text = []
if "entered_mood" not in st.session_state:
    st.session_state.entered_mood = []
if "messages" not in st.session_state:
    st.session_state.messages = []
if "user_sentiment" not in st.session_state:
    st.session_state.user_sentiment = "Neutral"
if "mood_trend" not in st.session_state:
    st.session_state.mood_trend = "Unchanged"
if "mood_trend_symbol" not in st.session_state:
    st.session_state.mood_trend_symbol = ""

# Layout of input/response containers

colored_header(label='', description='', color_name='blue-30')
response_container = st.container()
input_container = st.container()

# User input
## Function for taking user provided prompt as input
def get_text():
    input_text = st.text_input("You: ", "", key="input")
    return input_text

def generate_response(prompt):
    response = mdl.call_conversational_rag(prompt,final_chain)
    return response['answer']

# Collect user input
# Add a radio button to choose input mode
input_mode = st.sidebar.radio("Select input mode:", ["Text", "Speech"])
user_message = None
if input_mode == "Speech":
    # Use the speech_to_text function to capture speech input
    speech_input = speech_to_text(key="my_stt", callback=speech_recognition_callback)
    # Check if speech input is available
    if "speech_input" in st.session_state and st.session_state.speech_input:
        # Display the speech input
        # st.text(f"Speech Input: {st.session_state.speech_input}")

        # Process the speech input as a query
        user_message = st.session_state.speech_input
        st.session_state.speech_input = None
else:
    user_message = st.chat_input("Type your message here:")

## Applying the user input box        
with input_container:
    if user_message:
        st.session_state.entered_text.append(user_message)
        st.session_state.messages.append({"role": "user", "content": user_message})
        
        # Display the user's message
        with st.chat_message("user"):
            st.write(user_message)
            
        # Process the user's message and generate a response
        with st.spinner("Processing..."):
            response = generate_response(user_message)
            st.session_state.past.append(user_message)
            st.session_state.messages.append({"role": "ai", "content": response})
            
            # Detect sentiment
            user_sentiment = chatbot.detect_sentiment(user_message)
            
            # Update mood history / mood_trend
            chatbot.update_mood_history()
            mood_trend = chatbot.check_mood_trend()

            # Define rewards
            if user_sentiment in ["Positive", "Moderately Positive"]:
                if mood_trend == "increased":
                        reward = +1
                        mood_trend_symbol = " ⬆️"
                elif mood_trend == "unchanged":
                        reward = +0.8
                        mood_trend_symbol = ""    
                else:  # decreased
                        reward = -0.2
                        mood_trend_symbol = " ⬇️"            
            else:
                 if mood_trend == "increased":
                        reward = +1
                        mood_trend_symbol = " ⬆️"
                 elif mood_trend == "unchanged":
                        reward = -0.2
                        mood_trend_symbol = ""   
                 else:  # decreased
                        reward = -1
                        mood_trend_symbol = " ⬇️"      
    
            print(f"mood_trend - sentiment - reward: {mood_trend} - {user_sentiment} - 🛑{reward}🛑")    
    

            # Update Q-values
            chatbot.update_q_values(user_sentiment, reward, user_sentiment)
            
            # Display the AI's response
            with st.chat_message("ai"):
                st.markdown(response)
                st.session_state.user_sentiment = user_sentiment
                st.session_state.mood_trend = mood_trend
                st.session_state.mood_trend_symbol = mood_trend_symbol
                
                # Convert the response to speech
                speech_fp = text_to_speech(response)
                # Play the speech
                st.audio(speech_fp, format='audio/mp3')


with st.sidebar.expander("Sentiment Analysis"):
        # Use the values stored in session state
        st.write(
            f"- Detected User Tone: {st.session_state.user_sentiment} ({st.session_state.mood_trend.capitalize()}{st.session_state.mood_trend_symbol})"
        )
            
        # Display Q-table
        st.dataframe(display_q_table(chatbot.q_values, states))