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import os
import numpy as np
import pandas as pd
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, learning_rate=0.9, discount_factor=0.1):
self.states = states
self.learning_rate = learning_rate
self.discount_factor = discount_factor
self.q_values = np.random.rand(len(states))
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 update_q_values(self, current_state, reward, next_state):
# print(f"state-reward: {current_state} - {reward} -- (b)")
current_state_index = self.states.index(current_state)
next_state_index = self.states.index(next_state)
current_q_value = self.q_values[current_state_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] = 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' |