Hugging Face's logo

Spaces:
AleksandraSledz
/
fomofixtest
Sleeping

App Files Community
fomofixtest
File size: 4,695 Bytes 
5038c7a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
 

import os
import pandas as pd
import streamlit as st
from q_learning_chatbot import QLearningChatbot
from xgb_mental_health import MentalHealthClassifier
from bm25_retreive_question import QuestionRetriever
from llm_response_generator import LLLResponseGenerator

# Streamlit UI
st.title("FOMO Fix - RL-based Mental Health Assistant")

# Define states and actions
states = ['Negative', 'Moderately Negative', 'Neutral', 'Moderately Positive', 'Positive']
actions = ['encouragement', 'empathy']

# Initialize Q-learning chatbot and mental health classifier
chatbot = QLearningChatbot(states, actions)

# Initialize MentalHealthClassifier
data_path = "data.csv"
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)


# Function to display Q-table
def display_q_table(q_values, states, actions):
    q_table_dict = {'State': states}
    for i, action in enumerate(actions):
        q_table_dict[action] = q_values[:, i]

    q_table_df = pd.DataFrame(q_table_dict)
    return q_table_df


# 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 = []

# Collect user input
user_message = st.text_input("Type your message here:")

# Take user input
if user_message:
    st.session_state.entered_text.append(user_message)

    # Detect mental condition
    mental_classifier.initialize_tokenizer(tokenizer_model_name)
    mental_classifier.preprocess_data()
    predicted_mental_category = mental_classifier.predict_category(user_message)
    print("Predicted mental health condition:", predicted_mental_category)
    # st.subheader("🛑 " + f"{predicted_mental_category.capitalize()}")

    # Retrieve question
    retriever = QuestionRetriever()
    question = retriever.get_response(user_message, predicted_mental_category)
    # st.write(question)

    # Detect sentiment
    user_sentiment = chatbot.detect_sentiment(user_message)
    # Update mood history / moode_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 = +0.8
        else: # decresed
            reward = -0.3

    else:
        if mood_trend == "increased":
            reward = +1
        else:
            reward = -1

    print(f"mood_trend - sentiment - reward: {mood_trend} - {user_sentiment} - 🛑{reward}🛑 -- (a)")

    # Update Q-values
    chatbot.update_q_values(user_sentiment, chatbot.actions[0], reward, user_sentiment)

    # 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')

    llm_model = LLLResponseGenerator()
    temperature = 0.1
    max_length = 128
    template = """INSTRUCTIONS: {context}

        Respond to the user with a tone of {ai_tone}.

        Question asked to the user: {question}

        Response by the user: {user_text}
        Response;
        """
    context = "You are a mental health supporting non-medical assistant. Provide some advice and ask a relevant question back to the user."


    llm_response = llm_model.llm_inference(
        model_type="huggingface",
        question=question,
        prompt_template=template,
        ai_tone=ai_tone,
        questionnaire=predicted_mental_category,
        user_text=user_message,
        temperature=temperature,
        max_length=max_length,
        )


    st.write(f"{llm_response}")
    st.write(f"{question}")

    st.subheader("Behind the Scence - What AI is doing:")
    st.write(f"- User Tone: {user_sentiment}, Possibly {predicted_mental_category.capitalize()}")
    st.write(f"- AI Tone: {ai_tone.capitalize()}")
    # st.write(f"Question: {question}")


    # 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}")