Update app.py

app.py

@@ -9,12 +9,16 @@ 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()
 final_chain = mdl.create_final_chain()
 
-st.set_page_config(page_title="PeacePal")
-
 # Define states and actions
 states = [
     "Negative",
@@ -24,15 +28,35 @@ states = [
     "Positive",
 ]
 
-# Add logo to the sidebar
-#logo_path = os.path.join('images', 'logo.jpeg')
-#st.sidebar.image(logo_path, use_column_width=True)
+# Initialize Q-learning chatbot and mental health classifier
+chatbot = QLearningChatbot(states)
 
-# Add image to the sidebar
-image_path = os.path.join('images', 'sidebar.jpg')
-st.sidebar.image(image_path, use_column_width=True)
+# 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
 
-st.title('PeacePal 🌱')
+
+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:
@@ -41,6 +65,24 @@ if 'generated' not in st.session_state:
 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')
@@ -56,28 +98,7 @@ def get_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
@@ -101,11 +122,60 @@ with input_container:
                 response = generate_response(query)
                 st.session_state.past.append(query)
                 st.session_state.generated.append(response)
+                # 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 = ""
                 
+                # 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")
@@ -116,7 +186,59 @@ with input_container:
                 response = generate_response(query)
                 st.session_state.past.append(query)
                 st.session_state.generated.append(response)
+                # 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 = ""
+                # 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