app.py

import streamlit as st
import os
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_pipeline import ModelPipeLine
from q_learning_chatbot import QLearningChatbot
from retriever import create_vectorstore

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 = ""
if "show_question" not in st.session_state:
    st.session_state.show_question = False
if "asked_questions" not in st.session_state:
    st.session_state.asked_questions = []

# 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']
        
     
## Applying the user input box        
with input_container:
    # Add a radio button to choose input mode
    input_mode = st.radio("Select input mode:", ["Text", "Speech"])

    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
            query = st.session_state.speech_input
            with st.spinner("processing....."):
                response = generate_response(query)
                st.session_state.past.append(query)
                st.session_state.generated.append(response)
                # Detect sentiment
                user_sentiment = chatbot.detect_sentiment(query)
    
                # Retrieve question
                if user_sentiment in ["Negative", "Moderately Negative", "Neutral"]:
                    question = retriever.get_response(
                        user_message
                    )
                    st.session_state.asked_questions.append(question)
                    show_question = True
                else:
                    show_question = False
                    question = ""
                
                # 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
                )

                # Convert the response to speech
                speech_fp = text_to_speech(response)
                # Play the speech
                st.audio(speech_fp, format='audio/mp3')

    else:
        # Add a text input field for query
        query = st.text_input("Query: ", key="input")

        # Process the query if it's not empty
        if query:
            with st.spinner("typing....."):
                response = generate_response(query)
                st.session_state.past.append(query)
                st.session_state.generated.append(response)
                # Detect sentiment
                user_sentiment = chatbot.detect_sentiment(query)
    
                # Retrieve question
                if user_sentiment in ["Negative", "Moderately Negative", "Neutral"]:
                    question = retriever.get_response(
                        user_message
                    )
                    st.session_state.asked_questions.append(question)
                    show_question = True
                else:
                    show_question = False
                    question = ""
                # Convert the response to speech
                speech_fp = text_to_speech(response)
                # Play the speech
                st.audio(speech_fp, format='audio/mp3')
                
                # 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
                )

                # Convert the response to speech
                speech_fp = text_to_speech(response)
                # Play the speech
                st.audio(speech_fp, format='audio/mp3')

## Conditional display of AI generated responses as a function of user provided prompts
with response_container:        
    if st.session_state['generated']:
        for i in range(len(st.session_state['generated'])):
            message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')
            message(st.session_state["generated"][i], key=str(i))