Create app.py

app.py

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+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))