Update app.py

app.py

@@ -45,9 +45,22 @@ 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 = []
 
 # Select Question Retriever
 selected_retriever_option = st.sidebar.selectbox(
@@ -62,6 +75,9 @@ for message in st.session_state.messages:
     with st.chat_message(message.get("role")):
         st.write(message.get("content"))
 
+section_visible = False
+
+
 # Collect user input
 user_message = st.chat_input("Type your message here:")
 
@@ -72,8 +88,8 @@ if user_message:
     st.session_state.messages.append({"role": "user", "content": user_message})
     with st.chat_message("user"):
         st.write(user_message)
-    # Detect mental condition
 
+    # Detect mental condition
     with st.spinner("Processing..."):
         mental_classifier.initialize_tokenizer(tokenizer_model_name)
         mental_classifier.preprocess_data()
@@ -84,7 +100,7 @@ if user_message:
         user_sentiment = chatbot.detect_sentiment(user_message)
 
         # Retrieve question
-        if user_sentiment in ["Negative", "Moderately Negative"]:
+        if user_sentiment in ["Negative", "Moderately Negative", "Neutral"]:
             question = retriever.get_response(user_message, predicted_mental_category)
             show_question = True
         else:
@@ -92,7 +108,7 @@ if user_message:
             question = ""
             predicted_mental_category = ""
 
-        # Update mood history / moode_trend
+        # Update mood history / mood_trend
         chatbot.update_mood_history()
         mood_trend = chatbot.check_mood_trend()
 
@@ -104,10 +120,9 @@ if user_message:
             elif mood_trend == "unchanged":
                 reward = +0.8
                 mood_trend_symbol = ""
-            else:  # decresed
+            else:  # decreased
                 reward = -0.2
                 mood_trend_symbol = " ⬇️"
-
         else:
             if mood_trend == "increased":
                 reward = +1
@@ -120,7 +135,7 @@ if user_message:
                 mood_trend_symbol = " ⬇️"
 
         print(
-            f"mood_trend - sentiment - reward: {mood_trend} - {user_sentiment} - 🛑{reward}🛑 -- (a)"
+            f"mood_trend - sentiment - reward: {mood_trend} - {user_sentiment} - 🛑{reward}🛑"
         )
 
         # Update Q-values
@@ -131,7 +146,7 @@ if user_message:
         # Get recommended action based on the updated Q-values
         ai_tone = chatbot.get_action(user_sentiment)
         print(ai_tone)
-        # --------------
+
         # LLM Response Generator
         HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')
 
@@ -139,11 +154,13 @@ if user_message:
         temperature = 0.1
         max_length = 128
 
+        #Question asked to the user: {question}
+
         template = """INSTRUCTIONS: {context}
 
             Respond to the user with a tone of {ai_tone}.
 
-            Question asked to the user: {question}
+            Question asked to the user: "None"
 
             Response by the user: {user_text}
             Response;
@@ -161,44 +178,53 @@ if user_message:
             temperature=temperature,
             max_length=max_length,
         )
-        st.session_state.messages.append({"role": "ai", "content": llm_response})
+
+        if show_question:
+            llm_reponse_with_quesiton = f"{llm_response}\n\n{question}"
+        else:
+            llm_reponse_with_quesiton = llm_response
+
+        st.session_state.messages.append({"role": "ai", "content": llm_reponse_with_quesiton})
 
     with st.chat_message("ai"):
-        st.markdown(llm_response)
+        st.markdown(llm_reponse_with_quesiton)
     # st.write(f"{llm_response}")
-    if show_question:
-        st.write(f"{question}")
+    # if show_question:
+    #     st.write(f"{question}")
     # else:
     # user doesn't feel negative.
     # get question to ecourage even more positive behaviour
 
-    st.subheader("Behind the Scence - What AI is doing:")
+
+        # 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
+
+# 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: {user_sentiment} ({mood_trend.capitalize()}{mood_trend_symbol})"
+        f"- Detected User Tone: {st.session_state.user_sentiment} ({st.session_state.mood_trend.capitalize()}{st.session_state.mood_trend_symbol})"
     )
-    if show_question:
+    if st.session_state.show_question:
         st.write(
-            f"- Possible Mental Condition: {predicted_mental_category.capitalize()}"
+            f"- Possible Mental Condition: {st.session_state.predicted_mental_category.capitalize()}"
         )
-    st.write(f"- AI Tone: {ai_tone.capitalize()}")
+    st.write(f"- AI Tone: {st.session_state.ai_tone.capitalize()}")
     st.write(f"- Question retrieved from: {selected_retriever_option}")
     st.write(
-        f"- If the user feels neagative or moderately negative, at the end of the AI response, it adds a mental health condition realted question. The question is retrieved from DB. The categories of questions are limited to Depression, Anxiety, and ADHD which are most associated with FOMO related to excessive social media usage."
+        f"- If the user feels negative or moderately negative, 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, and ADHD which are most associated with FOMO related to excessive social media usage."
     )
     st.write(
-        f"- Below q-table is continously updated after each interaction with the user. If the user's mood increases, AI gets reward. Else, AI gets punishment."
+        f"- Below 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."
     )
 
-    # Display results
-    # st.subheader(f"{user_sentiment.capitalize()}")
-    # st.write("->" + f"{ai_tone.capitalize()}")
-    # st.write(f"Mood {chatbot.check_mood_trend()}")
-    # st.write(f"{ai_tone.capitalize()}, {chatbot.check_mood_trend()}")
-
     # Display Q-table
     st.dataframe(display_q_table(chatbot.q_values, states, actions))
-
-    # Display mood history
-    # st.subheader("Mood History (Recent 5):")
-    # for mood_now in reversed(chatbot.mood_history[-5:]): #st.session_state.entered_mood[-5:], chatbot.mood_history[-5:]): #st.session_state.entered_text[-5:]
-    #     st.write(f"{mood_now}")