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| import os | |
| import numpy as np | |
| import pandas as pd | |
| from xgb_mental_health import MentalHealthClassifier | |
| import pickle | |
| import streamlit as st | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| from torch.nn.functional import softmax | |
| import torch | |
| class QLearningChatbot: | |
| def __init__(self, states, actions, learning_rate=0.1, discount_factor=0.9): | |
| self.states = states | |
| self.actions = actions | |
| self.learning_rate = learning_rate | |
| self.discount_factor = discount_factor | |
| self.q_values = np.random.rand(len(states), len(actions)) | |
| self.mood = "Neutral" | |
| self.mood_history = [] | |
| self.mood_history_int = [] | |
| self.tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment") | |
| self.bert_sentiment_model_path = "bert_sentiment.pkl" | |
| self.bert_sentiment_model = self.load_model() if os.path.exists(self.bert_sentiment_model_path) else AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment") | |
| def detect_sentiment(self, input_text): | |
| # Encode the text | |
| encoded_input = self.tokenizer(input_text, return_tensors='pt', truncation=True, max_length=512) | |
| # Perform inference | |
| with torch.no_grad(): | |
| output = self.bert_sentiment_model(**encoded_input) | |
| # Process the output (softmax to get probabilities) | |
| scores = softmax(output.logits, dim=1) | |
| # Map scores to sentiment labels | |
| labels = ['Negative', 'Moderately Negative', 'Neutral', 'Moderately Positive', 'Positive'] | |
| scores = scores.numpy().flatten() | |
| scores_dict = {label: score for label, score in zip(labels, scores)} | |
| highest_sentiment = max(scores_dict, key=scores_dict.get) | |
| self.mood = highest_sentiment | |
| return highest_sentiment | |
| def get_action(self, current_state): | |
| current_state_index = self.states.index(current_state) | |
| # print(np.argmax(self.q_values[current_state_index, :])) | |
| return self.actions[np.argmax(self.q_values[current_state_index, :])] | |
| def update_q_values(self, current_state, action, reward, next_state): | |
| # print(f"state-reward: {current_state} - {reward} -- (b)") | |
| current_state_index = self.states.index(current_state) | |
| action_index = self.actions.index(action) | |
| next_state_index = self.states.index(next_state) | |
| current_q_value = self.q_values[current_state_index, action_index] | |
| max_next_q_value = np.max(self.q_values[next_state_index, :]) | |
| new_q_value = current_q_value + self.learning_rate * (reward + self.discount_factor * max_next_q_value - current_q_value) | |
| self.q_values[current_state_index, action_index] = new_q_value | |
| def update_mood_history(self): | |
| st.session_state.entered_mood.append(self.mood) | |
| self.mood_history = st.session_state.entered_mood | |
| return self.mood_history | |
| def check_mood_trend(self): | |
| mood_dict = {'Negative': 1, 'Moderately Negative': 2, 'Neutral': 3, 'Moderately Positive': 4, 'Positive': 5} | |
| if len(self.mood_history) >= 2: | |
| self.mood_history_int = [mood_dict.get(x) for x in self.mood_history] | |
| recent_moods = self.mood_history_int[-2:] | |
| if recent_moods[-1] > recent_moods[-2]: | |
| return 'increased' | |
| elif recent_moods[-1] < recent_moods[-2]: | |
| return 'decreased' | |
| else: | |
| return 'unchanged' | |
| else: | |
| return 'unchanged' | |