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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 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)
# 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)
# 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))
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