chatbot-py.py

import os
import json
import random
import datetime
import re
import sys

# Define emotion label mapping
EMOTION_LABELS = [
    "admiration", "amusement", "anger", "annoyance", "approval", "caring", "confusion",
    "curiosity", "desire", "disappointment", "disapproval", "disgust", "embarrassment",
    "excitement", "fear", "gratitude", "grief", "joy", "love", "nervousness", "optimism",
    "pride", "realization", "relief", "remorse", "sadness", "surprise", "neutral"
]

# Map similar emotions to our response categories
EMOTION_MAPPING = {
    "admiration": "joy",
    "amusement": "joy",
    "anger": "anger",
    "annoyance": "anger",
    "approval": "joy",
    "caring": "joy",
    "confusion": "neutral",
    "curiosity": "neutral",
    "desire": "neutral",
    "disappointment": "sadness",
    "disapproval": "anger",
    "disgust": "disgust",
    "embarrassment": "sadness",
    "excitement": "joy",
    "fear": "fear",
    "gratitude": "joy",
    "grief": "sadness",
    "joy": "joy",
    "love": "joy",
    "nervousness": "fear",
    "optimism": "joy",
    "pride": "joy",
    "realization": "neutral",
    "relief": "joy",
    "remorse": "sadness",
    "sadness": "sadness",
    "surprise": "surprise",
    "neutral": "neutral"
}

class ChatbotContext:
    """Class to maintain conversation context and history"""
    def __init__(self):
        self.conversation_history = []
        self.detected_emotions = []
        self.user_feedback = []
        self.current_session_id = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
        # Track emotional progression for therapeutic conversation flow
        self.conversation_stage = "initial"  # initial, middle, advanced
        self.emotion_trajectory = []  # track emotion changes over time
        self.consecutive_positive_count = 0
        self.consecutive_negative_count = 0
        # Add user name tracking
        self.user_name = None
        self.bot_name = "Mira"  # Friendly, easy to remember name
        self.introduced = False
    
    def add_message(self, role, text, emotions=None):
        """Add a message to the conversation history"""
        timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        message = {
            "role": role,
            "text": text,
            "timestamp": timestamp
        }
        if emotions and role == "user":
            message["emotions"] = emotions
            self.detected_emotions.append(emotions)
            self._update_emotional_trajectory(emotions)
        
        self.conversation_history.append(message)
        return message
    
    def _update_emotional_trajectory(self, emotions):
        """Update the emotional trajectory based on newly detected emotions"""
        # Get the primary emotion
        primary_emotion = emotions[0]["emotion"] if emotions else "neutral"
        
        # Add to trajectory
        self.emotion_trajectory.append(primary_emotion)
        
        # Classify as positive, negative, or neutral
        positive_emotions = ["joy", "admiration", "amusement", "excitement", 
                             "optimism", "gratitude", "pride", "love", "relief"]
        negative_emotions = ["sadness", "anger", "fear", "disgust", "disappointment", 
                             "annoyance", "disapproval", "embarrassment", "grief", 
                             "remorse", "nervousness"]
        
        if primary_emotion in positive_emotions:
            self.consecutive_positive_count += 1
            self.consecutive_negative_count = 0
        elif primary_emotion in negative_emotions:
            self.consecutive_negative_count += 1
            self.consecutive_positive_count = 0
        else:  # neutral or other
            # Don't reset counters for neutral emotions to maintain progress
            pass
        
        # Update conversation stage based on trajectory and message count
        # This now uses conversation history length rather than just emotion trajectory
        msg_count = len(self.conversation_history) // 2  # Count actual exchanges (user/bot pairs)
        if msg_count <= 1:  # First real exchange
            self.conversation_stage = "initial"
        elif msg_count <= 3:  # First few exchanges
            self.conversation_stage = "middle"
        else:  # More established conversation
            self.conversation_stage = "advanced"
    
    def get_emotional_state(self):
        """Get the current emotional state of the conversation"""
        if len(self.emotion_trajectory) < 2:
            return "unknown"
        
        # Get the last few emotions (with 'neutral' having less weight)
        recent_emotions = self.emotion_trajectory[-3:]
        positive_emotions = ["joy", "admiration", "amusement", "excitement", 
                             "optimism", "gratitude", "pride", "love", "relief"]
        negative_emotions = ["sadness", "anger", "fear", "disgust", "disappointment", 
                             "annoyance", "disapproval", "embarrassment", "grief", 
                             "remorse", "nervousness"]
        
        # Count positive and negative emotions
        pos_count = sum(1 for e in recent_emotions if e in positive_emotions)
        neg_count = sum(1 for e in recent_emotions if e in negative_emotions)
        
        if self.consecutive_positive_count >= 2:
            return "positive"
        elif self.consecutive_negative_count >= 2:
            return "negative"
        elif pos_count > neg_count:
            return "improving"
        elif neg_count > pos_count:
            return "declining"
        else:
            return "neutral"
    
    def add_feedback(self, rating, comments=None):
        """Add user feedback about the chatbot's response"""
        feedback = {
            "rating": rating,
            "comments": comments,
            "timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        }
        self.user_feedback.append(feedback)
        return feedback
    
    def get_recent_messages(self, count=5):
        """Get the most recent messages from the conversation history"""
        return self.conversation_history[-count:] if len(self.conversation_history) >= count else self.conversation_history
    
    def save_conversation(self, filepath=None):
        """Save the conversation history to a JSON file"""
        if not filepath:
            os.makedirs("./conversations", exist_ok=True)
            filepath = f"./conversations/conversation_{self.current_session_id}.json"
        
        data = {
            "conversation_history": self.conversation_history,
            "user_feedback": self.user_feedback,
            "emotion_trajectory": self.emotion_trajectory,
            "session_id": self.current_session_id,
            "start_time": self.conversation_history[0]["timestamp"] if self.conversation_history else None,
            "end_time": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
        }
        
        with open(filepath, 'w') as f:
            json.dump(data, f, indent=2)
        print(f"Conversation saved to {filepath}")
        return filepath

def clean_response_text(response, user_name):
    """Clean up the response text to make it more natural"""
    # Remove repeated name mentions
    if user_name:
        # Replace patterns like "Hey user_name," or "Hi user_name,"
        response = re.sub(r'^(Hey|Hi|Hello)\s+' + re.escape(user_name) + r',?\s+', '', response, flags=re.IGNORECASE)
        
        # Replace duplicate name mentions
        pattern = re.escape(user_name) + r',?\s+.*' + re.escape(user_name)
        if re.search(pattern, response, re.IGNORECASE):
            response = re.sub(r',?\s+' + re.escape(user_name) + r'([,.!?])', r'\1', response, flags=re.IGNORECASE)
        
        # Remove name at the end of sentences if it appears earlier
        if response.count(user_name) > 1:
            response = re.sub(r',\s+' + re.escape(user_name) + r'([.!?])(\s|$)', r'\1\2', response, flags=re.IGNORECASE)
    
    # Remove phrases that feel repetitive or formulaic
    phrases_to_remove = [
        r"let me know what you'd prefer,?\s+",
        r"i'm here to listen,?\s+",
        r"let me know if there's anything else,?\s+",
        r"i'm all ears,?\s+",
        r"i'm here for you,?\s+"
    ]
    
    for phrase in phrases_to_remove:
        response = re.sub(phrase, "", response, flags=re.IGNORECASE)
    
    # Fix multiple punctuation
    response = re.sub(r'([.!?])\s+\1', r'\1', response)
    
    # Fix missing space after punctuation
    response = re.sub(r'([.!?])([A-Za-z])', r'\1 \2', response)
    
    # Make sure first letter is capitalized
    if response and len(response) > 0:
        response = response[0].upper() + response[1:]
    
    return response.strip()