import streamlit as st
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
import pandas as pd
from gtts import gTTS
from io import BytesIO

mdl = ModelPipeLine()
final_chain = mdl.create_final_chain()

st.set_page_config(page_title="PeacePal")

st.title('PeacePal 🌱')

states = [
    "Negative",
    "Neutral",
    "Positive",
]


def display_q_table(states):
    values = [0,1,2]
    q_table_dict = {"State": states,
                    "values":values}
    q_table_df = pd.DataFrame(q_table_dict)
    return q_table_df

## 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']

if "user_sentiment" not in st.session_state:
    st.session_state.user_sentiment = "Neutral"

# 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):
    sentiment = mdl.predict_classification(prompt)
    response = mdl.call_conversational_rag(prompt,final_chain)
    return response['answer'],sentiment

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


input_mode = st.sidebar.radio("Select input mode:", ["Text", "Speech"])
## Applying the user input box  
query = None      
with input_container:
    detected_sentiment = 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
            query = st.session_state.speech_input
            with st.spinner("processing....."):
                response,detected_sentiment = generate_response(query)
                st.session_state.past.append(query)
                st.session_state.generated.append(response)
                st.session_state.speech_input = None
                # 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("processing....."):
                response,detected_sentiment = generate_response(query)
                st.session_state.past.append(query)
                st.session_state.generated.append(response)
                query = None
                # Convert the response to speech
                speech_fp = text_to_speech(response)
                # Play the speech
                st.audio(speech_fp, format='audio/mp3')
    if detected_sentiment == 0:
        st.session_state.user_sentiment = 'Negative'
    elif detected_sentiment == 1:
        st.session_state.user_sentiment = 'Neutral'
    elif detected_sentiment == 1:
        st.session_state.user_sentiment = 'Positive'
    else:
        st.session_state.user_sentiment = 'Neutral'


## 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))

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