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MindfulMedia_Mentor / app.py

jaelin215

Removed function to remove incomplete sentence

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	###
	# - Author: Jaelin Lee, Abhishek Dutta
	# - Date: Mar 23, 2024
	# - Description: Streamlit UI for mental health support chatbot using sentiment analsys, RL, BM25/ChromaDB, and LLM.

	# - Note:
	# - Updated to UI to show predicted mental health condition in behind the scence regardless of the ositive/negative sentiment
	###

	from dotenv import load_dotenv, find_dotenv
	import pandas as pd
	import streamlit as st
	from q_learning_chatbot import QLearningChatbot
	from xgb_mental_health import MentalHealthClassifier
	from bm25_retreive_question import QuestionRetriever as QuestionRetriever_bm25
	from Chromadb_storage_JyotiNigam import QuestionRetriever as QuestionRetriever_chromaDB
	from llm_response_generator import LLLResponseGenerator
	import os
	from llama_guard import moderate_chat, get_category_name

	from gtts import gTTS
	from io import BytesIO
	from streamlit_mic_recorder import speech_to_text

	import re

	# Streamlit UI
	st.title("MindfulMedia Mentor")

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

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

	# Initialize MentalHealthClassifier
	# data_path = "/Users/jaelinlee/Documents/projects/fomo/input/data.csv"
	data_path = os.path.join("data", "data.csv")
	print(data_path)

	tokenizer_model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
	mental_classifier_model_path = "mental_health_model.pkl"
	mental_classifier = MentalHealthClassifier(data_path, mental_classifier_model_path)

	if not os.path.exists(mental_classifier_model_path):
	mental_classifier.initialize_tokenizer(tokenizer_model_name)
	X, y = mental_classifier.preprocess_data()
	y_test, y_pred = mental_classifier.train_model(X, y)
	mental_classifier.save_model()
	else:
	mental_classifier.load_model()
	mental_classifier.initialize_tokenizer(tokenizer_model_name) # Ensure tokenizer is initialized if loading model from pickle
	# X, y = mental_classifier.preprocess_data() # Preprocess data again if needed
	# mental_classifier.model.fit(X, y) # Fit the loaded model to the data

	# Function to display Q-table
	def display_q_table(q_values, states, actions):
	q_table_dict = {"State": states}
	for i, action in enumerate(actions):
	q_table_dict[action] = q_values[:, i]

	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


	def remove_html_tags(text):
	# clean_text = re.sub("<.*?>", "", text)
	clean_text = re.sub(r'<.*?>\|- \|"\|\\n', '', text)
	# Remove indentation
	clean_text = clean_text.strip()
	# Remove new lines
	clean_text = clean_text.replace('\n', ' ')
	return clean_text


	def remove_incomplete_sentence(text):
	# Split the text into sentences using punctuation marks as delimiters
	sentences = re.split(r'(?<=[.!?])\s', text)

	# Check if the last sentence is complete
	last_sentence = sentences[-1]
	if not re.match(r'^\w.*[.!?]$', last_sentence):
	del sentences[-1]

	# Join the sentences back into a single string
	cleaned_text = ' '.join(sentences)

	return cleaned_text


	# 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 "predicted_mental_category" not in st.session_state:
	st.session_state.predicted_mental_category = ""
	if "ai_tone" not in st.session_state:
	st.session_state.ai_tone = "Empathy"
	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 = []
	# Check if 'llama_guard_enabled' is already in session state, otherwise initialize it
	if "llama_guard_enabled" not in st.session_state:
	st.session_state["llama_guard_enabled"] = False # Default value to False

	# Select Question Retriever
	selected_retriever_option = st.sidebar.selectbox(
	"Choose Question Retriever", ("BM25", "ChromaDB")
	)
	if selected_retriever_option == "BM25":
	retriever = QuestionRetriever_bm25()
	if selected_retriever_option == "ChromaDB":
	retriever = QuestionRetriever_chromaDB()

	for message in st.session_state.messages:
	with st.chat_message(message.get("role")):
	st.write(message.get("content"))

	section_visible = True

	# 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:")


	# Modify the checkbox call to include a unique key parameter
	llama_guard_enabled = st.sidebar.checkbox(
	"Enable LlamaGuard",
	value=st.session_state["llama_guard_enabled"],
	key="llama_guard_toggle",
	)

	# Update the session state based on the checkbox interaction
	st.session_state["llama_guard_enabled"] = llama_guard_enabled

	# Take user input
	if user_message:
	st.session_state.entered_text.append(user_message)

	st.session_state.messages.append({"role": "user", "content": user_message})
	with st.chat_message("user"):
	st.write(user_message)

	is_safe = True
	if st.session_state["llama_guard_enabled"]:
	# guard_status = moderate_chat(user_prompt)
	guard_status, error = moderate_chat(user_message)
	if error:
	st.error(f"Failed to retrieve data from Llama Guard: {error}")
	else:
	if "unsafe" in guard_status[0]["generated_text"]:
	is_safe = False
	# added on March 24th
	unsafe_category_name = get_category_name(
	guard_status[0]["generated_text"]
	)

	if is_safe == False:
	response = f"I see you are asking something about {unsafe_category_name} Due to eithical and safety reasons, I can't provide the help you need. Please reach out to someone who can, like a family member, friend, or therapist. In urgent situations, contact emergency services or a crisis hotline. Remember, asking for help is brave, and you're not alone."
	st.session_state.messages.append({"role": "ai", "content": response})
	with st.chat_message("ai"):
	st.markdown(response)
	speech_fp = text_to_speech(response)
	# Play the speech
	st.audio(speech_fp, format="audio/mp3")
	else:
	# Detect mental condition
	with st.spinner("Processing..."):
	mental_classifier.initialize_tokenizer(tokenizer_model_name)
	mental_classifier.preprocess_data()
	predicted_mental_category = mental_classifier.predict_category(user_message)
	print("Predicted mental health condition:", predicted_mental_category)

	# Detect sentiment
	user_sentiment = chatbot.detect_sentiment(user_message)

	# Retrieve question
	if user_sentiment in ["Negative", "Moderately Negative", "Neutral"]:
	question = retriever.get_response(
	user_message, predicted_mental_category
	)
	st.session_state.asked_questions.append(question)
	show_question = True
	else:
	show_question = False
	question = ""
	# predicted_mental_category = ""

	# 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, chatbot.actions[0], reward, user_sentiment
	)

	# Get recommended action based on the updated Q-values
	ai_tone = chatbot.get_action(user_sentiment)
	print(ai_tone)

	print(st.session_state.messages)

	# LLM Response Generator
	load_dotenv(find_dotenv())

	llm_model = LLLResponseGenerator()
	temperature = 0.5
	max_length = None #128 * 4

	# Collect all messages exchanged so far into a single text string
	all_messages = "\n".join(
	[message.get("content") for message in st.session_state.messages]
	)

	# Question asked to the user: {question}

	template = """INSTRUCTIONS: {context}

	Respond to the user with a tone of {ai_tone}.

	Response by the user: {user_text}
	Response;
	"""
	context = f"You are a mental health supporting non-medical assistant. Provide brief advice. DO NOT ASK ANY QUESTION. DO NOT REPEAT YOURSELF. {all_messages}" # and ask a relevant question back to the user
	# context = f"You are a Mindful Media Mentor, dedicated to providing compassionate support and guidance to users facing mental health challenges. Your goal is to foster a safe and understanding environment where users feel heard and supported. Draw from your expertise to offer practical advice and resources, and encourage users to explore their feelings and experiences openly. Your responses should aim to empower users to take positive steps towards their well-being. {all_messages}"

	llm_response = llm_model.llm_inference(
	model_type="huggingface",
	question=question,
	prompt_template=template,
	context=context,
	ai_tone=ai_tone,
	questionnaire=predicted_mental_category,
	user_text=user_message,
	temperature=temperature,
	max_length=max_length,
	)

	llm_response = remove_html_tags(llm_response)
	# llm_response = remove_incomplete_sentence(llm_response)

	if show_question:
	llm_reponse_with_quesiton = f"{llm_response}\n\n{question}"
	else:
	llm_reponse_with_quesiton = llm_response

	# Append the user and AI responses to the chat history
	st.session_state.messages.append(
	{"role": "ai", "content": llm_reponse_with_quesiton}
	)

	with st.chat_message("ai"):
	st.markdown(llm_reponse_with_quesiton)


	# st.write(f"{llm_response}")
	# if show_question:
	# st.write(f"{question}")
	# else:
	# user doesn't feel negative.
	# get question to ecourage even more positive behaviour

	# Update data to memory
	st.session_state.user_sentiment = user_sentiment
	st.session_state.mood_trend = mood_trend
	st.session_state.predicted_mental_category = predicted_mental_category
	st.session_state.ai_tone = ai_tone
	st.session_state.mood_trend_symbol = mood_trend_symbol
	st.session_state.show_question = show_question

	#if input_mode == "Speech":
	# Convert the response to speech
	speech_fp = text_to_speech(llm_reponse_with_quesiton)
	# Play the speech
	st.audio(speech_fp, format="audio/mp3")

	# Show/hide "Behind the Scene" section
	# section_visible = st.sidebar.button('Show/Hide Behind the Scene')

	with st.sidebar.expander("Behind the Scene", expanded=section_visible):
	st.subheader("What AI is doing:")
	# 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})"
	)

	if st.session_state.show_question:
	st.write(
	f"- Possible Mental Condition: {st.session_state.predicted_mental_category.capitalize()}"
	)
	st.write(f"- AI Tone: {st.session_state.ai_tone.capitalize()}")

	# Display Q-table
	st.dataframe(display_q_table(chatbot.q_values, states, actions))
	st.write("-----------------------")
	st.write(
	f"- Above q-table is continuously updated after each interaction with the user. If the user's mood increases, AI gets a reward. Else, AI gets a punishment."
	)
	st.write(f"- Question retrieved from: {selected_retriever_option}")
	st.write(
	f"- If the user feels negative, moderately negative, or neutral, at the end of the AI response, it adds a mental health condition related question. The question is retrieved from DB. The categories of questions are limited to Depression, Anxiety, ADHD, Social Media Addiction, Social Isolation, and Cyberbullying which are most associated with FOMO related to excessive social media usage."
	)