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 mdl = ModelPipeLine() final_chain = mdl.create_final_chain() st.set_page_config(page_title="PeacePal") # Define states and actions states = [ "Negative", "Moderately Negative", "Neutral", "Moderately Positive", "Positive", ] # Add logo to the sidebar #logo_path = os.path.join('images', 'logo.jpeg') #st.sidebar.image(logo_path, use_column_width=True) # Add image to the sidebar image_path = os.path.join('images', 'sidebar.jpg') st.sidebar.image(image_path, use_column_width=True) st.title('PeacePal 🌱') ## 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!'] # 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'] 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 ## 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) # 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) # 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))