updated prompt not to ask any question

app.py

@@ -7,6 +7,7 @@
 #   - Updated to UI to show predicted mental health condition in behind the scence regardless of the ositive/negative sentiment
 ###
 
+from dotenv import load_dotenv, find_dotenv
 import pandas as pd
 import streamlit as st
 from q_learning_chatbot import QLearningChatbot
@@ -48,16 +49,16 @@ tokenizer_model_name = "nlptown/bert-base-multilingual-uncased-sentiment"
 mental_classifier_model_path = "mental_health_model.pkl"
 mental_classifier = MentalHealthClassifier(data_path, mental_classifier_model_path)
 
-# if not os.path.exists(mental_classifier_model_path):
-#     mental_classifier.initialize_tokenizer(tokenizer_model_name)
-#     X, y = mental_classifier.preprocess_data()
-#     y_test, y_pred = mental_classifier.train_model(X, y)
-#     mental_classifier.save_model()
-# else:
-#     mental_classifier.load_model()
-#     mental_classifier.initialize_tokenizer(tokenizer_model_name)  # Ensure tokenizer is initialized if loading model from pickle
-#     X, y = mental_classifier.preprocess_data()  # Preprocess data again if needed
-#     mental_classifier.model.fit(X, y)  # Fit the loaded model to the data
+if not os.path.exists(mental_classifier_model_path):
+    mental_classifier.initialize_tokenizer(tokenizer_model_name)
+    X, y = mental_classifier.preprocess_data()
+    y_test, y_pred = mental_classifier.train_model(X, y)
+    mental_classifier.save_model()
+else:
+    mental_classifier.load_model()
+    mental_classifier.initialize_tokenizer(tokenizer_model_name)  # Ensure tokenizer is initialized if loading model from pickle
+    # X, y = mental_classifier.preprocess_data()  # Preprocess data again if needed
+    # mental_classifier.model.fit(X, y)  # Fit the loaded model to the data
 
 # Function to display Q-table
 def display_q_table(q_values, states, actions):
@@ -94,6 +95,10 @@ def speech_recognition_callback():
 def remove_html_tags(text):
     # clean_text = re.sub("<.*?>", "", text)
     clean_text = re.sub(r'<.*?>|- |"|\\n', '', text)
+    # Remove indentation
+    clean_text = clean_text.strip()
+    # Remove new lines
+    clean_text = clean_text.replace('\n', ' ')
     return clean_text
 
 # Initialize memory
@@ -162,7 +167,6 @@ llama_guard_enabled = st.sidebar.checkbox(
     key="llama_guard_toggle",
 )
 
-
 # Update the session state based on the checkbox interaction
 st.session_state["llama_guard_enabled"] = llama_guard_enabled
 
@@ -212,6 +216,7 @@ if user_message:
                 question = retriever.get_response(
                     user_message, predicted_mental_category
                 )
+                st.session_state.asked_questions.append(question)
                 show_question = True
             else:
                 show_question = False
@@ -260,7 +265,7 @@ if user_message:
             print(st.session_state.messages)
 
             # LLM Response Generator
-            HUGGINGFACEHUB_API_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')
+            load_dotenv(find_dotenv())
 
             llm_model = LLLResponseGenerator()
             temperature = 0.5
@@ -274,15 +279,15 @@ if user_message:
             # Question asked to the user: {question}
 
             template = """INSTRUCTIONS: {context}
-
-                Respond to the user with a tone of {ai_tone}.
-
-                Response by the user: {user_text}
+            
+                Respond to the user with a tone of {ai_tone}. 
+                
+                Response by the user: {user_text}  
                 Response;
                 """
-            context = f"You are a mental health supporting non-medical assistant. Provide some advice and ask a relevant question back to the user. {all_messages}"
+            context = f"You are a mental health supporting non-medical assistant. Provide brief advice. DO NOT ASK ANY QUESTION. DO NOT REPEAT YOURSELF. {all_messages}" # and ask a relevant question back to the user
             # context = f"You are a Mindful Media Mentor, dedicated to providing compassionate support and guidance to users facing mental health challenges. Your goal is to foster a safe and understanding environment where users feel heard and supported. Draw from your expertise to offer practical advice and resources, and encourage users to explore their feelings and experiences openly. Your responses should aim to empower users to take positive steps towards their well-being. {all_messages}"
-
+ 
             llm_response = llm_model.llm_inference(
                 model_type="huggingface",
                 question=question,
@@ -337,16 +342,14 @@ if user_message:
         st.write(
             f"- Detected User Tone: {st.session_state.user_sentiment} ({st.session_state.mood_trend.capitalize()}{st.session_state.mood_trend_symbol})"
         )
-        # if st.session_state.show_question:
-        st.write(
-            f"- Possible Mental Condition: {st.session_state.predicted_mental_category.capitalize()}"
-        )
+        if st.session_state.show_question:
+            st.write(
+                f"- Possible Mental Condition: {st.session_state.predicted_mental_category.capitalize()}"
+            )
         st.write(f"- AI Tone: {st.session_state.ai_tone.capitalize()}")
-        
-
+    
         # Display Q-table
         st.dataframe(display_q_table(chatbot.q_values, states, actions))
-
         st.write("-----------------------")
         st.write(
             f"- Above 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."
@@ -354,5 +357,4 @@ if user_message:
         st.write(f"- Question retrieved from: {selected_retriever_option}")
         st.write(
             f"- If the user feels negative, moderately negative, or neutral, 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, ADHD, Social Media Addiction, Social Isolation, and Cyberbullying which are most associated with FOMO related to excessive social media usage."
-        )
-        
+        )