update styles in app.py

app.py

@@ -1,848 +1,856 @@
-import gradio as gr
-import torch
-import os
-import json
-import re
-import random
-from transformers import (
-    AutoTokenizer,
-    AutoModelForSequenceClassification,
-    AutoModelForCausalLM,
-    pipeline,
-)
-import datetime
-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
-        self.waiting_for_name = 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
-        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()
-
-class GradioEmotionChatbot:
-    def __init__(self, emotion_model_id, response_model_id=None, confidence_threshold=0.3):
-        self.emotion_model_id = emotion_model_id
-        self.response_model_id = response_model_id or "mistralai/Mistral-7B-Instruct-v0.2"
-        self.confidence_threshold = confidence_threshold
-        self.context = ChatbotContext()
-        self.initialize_models()
-        
-    def initialize_models(self):
-        # Initialize emotion classification model
-        print(f"Loading emotion classification model: {self.emotion_model_id}")
-        try:
-            self.emotion_model = AutoModelForSequenceClassification.from_pretrained(self.emotion_model_id)
-            self.emotion_tokenizer = AutoTokenizer.from_pretrained(self.emotion_model_id)
-            
-            self.emotion_classifier = pipeline(
-                "text-classification",
-                model=self.emotion_model,
-                tokenizer=self.emotion_tokenizer,
-                top_k=None  # Returns scores for all labels
-            )
-            print("Emotion classification model loaded successfully!")
-        except Exception as e:
-            print(f"Error loading emotion classification model: {e}")
-            # Fallback to a dummy classifier for demo purposes
-            self.emotion_classifier = lambda text: [[{"label": "neutral", "score": 1.0}]]
-        
-        # Initialize response generation model (or use fallback)
-        print(f"Loading response generation model: {self.response_model_id}")
-        try:
-            self.response_model = AutoModelForCausalLM.from_pretrained(
-                self.response_model_id,
-                torch_dtype=torch.float16,
-                device_map="auto"
-            )
-            self.response_tokenizer = AutoTokenizer.from_pretrained(self.response_model_id)
-            
-            self.response_generator = pipeline(
-                "text-generation",
-                model=self.response_model,
-                tokenizer=self.response_tokenizer,
-                do_sample=True,
-                top_p=0.92,
-                top_k=50,
-                temperature=0.7,
-                max_new_tokens=100
-            )
-            print("Response generation model loaded successfully!")
-        except Exception as e:
-            print(f"Using fallback response generation. Reason: {e}")
-            self.response_generator = self.fallback_response_generator
-    
-    def fallback_response_generator(self, prompt, **kwargs):
-        """Fallback response generator using templates"""
-        # Try to extract emotion from the prompt
-        emotion_match = re.search(r"emotion: (\w+)", prompt.lower())
-        if emotion_match:
-            emotion = emotion_match.group(1)
-        else:
-            emotion = "neutral"
-            
-        # Default user name
-        user_name = "friend"
-        name_match = re.search(r"Your friend \((.*?)\)", prompt.lower())
-        if name_match:
-            user_name = name_match.group(1)
-        
-        # Extract user message
-        message_match = re.search(r"message: \"(.*?)\"", prompt)
-        user_message = message_match.group(1) if message_match else ""
-        
-        # Generate response using fallback method
-        response = self.natural_fallback_response(user_message, emotion, user_name)
-        
-        # Format as if coming from the pipeline
-        return [{"generated_text": response}]
-    
-    def natural_fallback_response(self, user_message, primary_emotion, user_name):
-        """Conversational fallback responses that sound like a supportive friend"""
-        # Define emotion categories
-        sad_emotions = ["sadness", "disappointment", "grief", "remorse"]
-        fear_emotions = ["fear", "nervousness", "anxiety"]
-        anger_emotions = ["anger", "annoyance", "disapproval", "disgust"] 
-        joy_emotions = ["joy", "admiration", "amusement", "excitement", "optimism", 
-                       "gratitude", "pride", "love", "relief"]
-        
-        # Multi-stage response templates - more natural and varied
-        if primary_emotion in joy_emotions:
-            responses = [
-                f"That's awesome, {user_name}! What made you feel that way?",
-                f"I'm so glad to hear that! Tell me more about it?",
-                f"That's great news! What else is going on with you lately?"
-            ]
-        elif primary_emotion in sad_emotions:
-            responses = [
-                f"I'm sorry to hear that, {user_name}. Want to talk about what happened?",
-                f"That sounds rough. What's been going on?",
-                f"Ugh, that's tough. How are you handling it?"
-            ]
-        elif primary_emotion in anger_emotions:
-            responses = [
-                f"That sounds really frustrating. What happened?",
-                f"Oh no, that would upset me too. Want to vent about it?",
-                f"I can see why you'd be upset about that. What are you thinking of doing?"
-            ]
-        elif primary_emotion in fear_emotions:
-            responses = [
-                f"That sounds scary, {user_name}. What's got you worried?",
-                f"I can imagine that would be stressful. What's on your mind about it?",
-                f"I get feeling anxious about that. What's the biggest concern for you?"
-            ]
-        else:  # neutral emotions
-            responses = [
-                f"What's been on your mind lately, {user_name}?",
-                f"How's everything else going with you?",
-                f"Tell me more about what's going on in your life these days."
-            ]
-        
-        return random.choice(responses)
-    
-    def classify_text(self, text):
-        """Classify text and return emotion data"""
-        try:
-            results = self.emotion_classifier(text)
-            
-            # Sort emotions by score in descending order
-            sorted_emotions = sorted(results[0], key=lambda x: x['score'], reverse=True)
-            
-            # Process emotions above threshold
-            detected_emotions = []
-            for emotion in sorted_emotions:
-                # Map numerical label to emotion name
-                try:
-                    label_id = int(emotion['label'].split('_')[-1]) if '_' in emotion['label'] else int(emotion['label'])
-                    if 0 <= label_id < len(EMOTION_LABELS):
-                        emotion_name = EMOTION_LABELS[label_id]
-                    else:
-                        emotion_name = emotion['label']
-                except (ValueError, IndexError):
-                    emotion_name = emotion['label']
-                    
-                score = emotion['score']
-                
-                if score >= self.confidence_threshold:
-                    detected_emotions.append({"emotion": emotion_name, "score": score})
-            
-            # If no emotions detected above threshold, add neutral
-            if not detected_emotions:
-                detected_emotions.append({"emotion": "neutral", "score": 1.0})
-                
-            return detected_emotions
-        except Exception as e:
-            print(f"Error during classification: {e}")
-            # Return neutral as fallback
-            return [{"emotion": "neutral", "score": 1.0}]
-    
-    def format_emotion_text(self, emotion_data):
-        """Create a simple emotion text display"""
-        if not emotion_data:
-            return ""
-            
-        # Define emotion emojis
-        emotion_emojis = {
-            "joy": "😊", "admiration": "🤩", "amusement": "😄", "approval": "👍",
-            "excitement": "🎉", "gratitude": "🙏", "love": "❤️", "optimism": "🌟",
-            "pride": "🦚", "relief": "😌", "sadness": "😢", "disappointment": "😞",
-            "grief": "💔", "remorse": "😔", "embarrassment": "😳", "anger": "😠",
-            "annoyance": "😤", "disapproval": "👎", "disgust": "🤢", "fear": "😨",
-            "nervousness": "😰", "surprise": "😲", "confusion": "😕", "curiosity": "🤔",
-            "neutral": "😐", "realization": "💡", "desire": "✨"
-        }
-        
-        # Format the primary emotion
-        primary = emotion_data[0]["emotion"]
-        emoji = emotion_emojis.get(primary, "😐")
-        score = emotion_data[0]["score"]
-        
-        return f"Detected: {emoji} {primary.capitalize()} ({score:.2f})"
-        
-    def generate_response(self, user_message, emotion_data):
-        """Generate a response based on the user's message and detected emotions"""
-        # Get the primary emotion with context awareness
-        primary_emotion = emotion_data[0]["emotion"] if emotion_data else "neutral"
-        
-        # Get recent conversation history for context
-        recent_exchanges = self.context.get_recent_messages(6)
-        conversation_history = ""
-        for msg in recent_exchanges:
-            role = "Friend" if msg["role"] == "user" else self.context.bot_name
-            conversation_history += f"{role}: {msg['text']}\n"
-        
-        # Check if this is a greeting
-        is_greeting = any(greeting in user_message.lower() for greeting in ["hi", "hello", "hey", "greetings"])
-        is_question_about_bot = "how are you" in user_message.lower() or any(q in user_message.lower() for q in ["what can you do", "who are you", "what are you", "your purpose"])
-        
-        # Handle special cases
-        if is_greeting:
-            if len(self.context.conversation_history) <= 4:  # First greeting exchange
-                return f"Hi! I'm {self.context.bot_name}. It's nice to meet you. How are you feeling today?"
-            else:
-                return f"Hey! Good to chat with you again. What's been going on with you?"
-        
-        elif is_question_about_bot:
-            return f"I'm doing well, thanks for asking! I'm {self.context.bot_name}, here as a friend to chat whenever you need someone to talk to. What's on your mind today?"
-        
-        # Create a more conversational prompt based on emotion
-        system_instruction = f"""You are {self.context.bot_name}, having a natural conversation with your friend. You should respond in a casual, warm way like a supportive friend would - not like a therapist or clinical chatbot.
-
-Your friend seems to be feeling {primary_emotion}. In your response:
-1. Be genuinely empathetic but natural - like how a real friend would respond
-2. Keep your response short (1-3 sentences) and conversational
-3. Don't use phrases like "I understand" or "I'm here for you" too much - vary your language
-4. Use casual language, contractions (don't instead of do not), and occasional sentence fragments
-5. Don't sound formulaic or overly positive - be authentic
-6. Keep the same emotional tone throughout your response
-7. Don't explain what you're doing or add meta-commentary
-8. DON'T address them by name multiple times or at the end of sentences - it sounds unnatural
-9. Don't end with "Let me know what you'd prefer" or similar phrases
-
-Recent conversation:
-{conversation_history}
-
-Your friend's message: "{user_message}"
-Current emotion: {primary_emotion}
-
-Respond naturally as a supportive friend (without using their name more than once if at all):"""
-        
-        try:
-            # Generate the response
-            generated = self.response_generator(
-                system_instruction,
-                max_new_tokens=100,
-                do_sample=True,
-                temperature=0.8,
-                top_p=0.92,
-                top_k=50,
-            )
-            
-            # Extract the generated text
-            if isinstance(generated, list):
-                response_text = generated[0].get('generated_text', '')
-            else:
-                response_text = generated.get('generated_text', '')
-            
-            # Clean up the response - extract only the actual response without system prompt
-            if "[/INST]" in response_text:
-                parts = response_text.split("[/INST]")
-                if len(parts) > 1:
-                    response_text = parts[1].strip()
-            
-            # If we're still getting the system instruction, try an alternative approach
-            if "Your friend seems to be feeling" in response_text:
-                # Try to extract just the bot's response using pattern matching
-                match = re.search(r'Respond naturally as a supportive friend.*?:\s*(.*?)$', response_text, re.DOTALL)
-                if match:
-                    response_text = match.group(1).strip()
-                else:
-                    # If that fails, try another approach - take text after the last numbered instruction
-                    match = re.search(r'9\.\s+[^\n]+\s*(.*?)$', response_text, re.DOTALL)
-                    if match:
-                        response_text = match.group(1).strip()
-                    else:
-                        # Last resort: pick a fallback response based on emotion
-                        response_text = self.natural_fallback_response(user_message, primary_emotion, self.context.user_name or "friend")
-            
-            # Remove any model-specific markers
-            response_text = response_text.replace("<s>", "").replace("</s>", "")
-            
-            # Remove any internal notes or debugging info that might appear
-            if "Note:" in response_text:
-                response_text = response_text.split("Note:")[0].strip()
-            
-            # Remove any metadata or system-like text
-            response_text = response_text.replace("Assistant:", "").replace(f"{self.context.bot_name}:", "").strip()
-            
-            # Remove any quotation marks surrounding the response
-            response_text = response_text.strip('"').strip()
-            
-            # Handle potential model halt mid-sentence
-            if response_text.endswith((".", "!", "?")):
-                pass  # Response ends with proper punctuation
-            else:
-                # Try to find the last complete sentence
-                last_period = max(response_text.rfind("."), response_text.rfind("!"), response_text.rfind("?"))
-                if last_period > len(response_text) * 0.5:  # If we've got at least half the response
-                    response_text = response_text[:last_period+1]
-            
-            # FINAL CHECK: If we still have parts of the system prompt, use fallback response
-            if any(phrase in response_text for phrase in ["Your friend seems to be feeling", "Keep your response short", "Be genuinely empathetic"]):
-                response_text = self.natural_fallback_response(user_message, primary_emotion, self.context.user_name or "friend")
-            
-            return clean_response_text(response_text.strip(), self.context.user_name)
-            
-        except Exception as e:
-            print(f"Error generating response: {e}")
-            return self.natural_fallback_response(user_message, primary_emotion, self.context.user_name or "friend")
-    
-    def process_message(self, user_message, chatbot_history):
-        """Process a user message and return the chatbot response"""
-        # Initialize context if first message
-        if not self.context.conversation_history:
-            initial_greeting = f"Hi! I'm {self.context.bot_name}, your friendly emotional support chatbot. Who am I talking to today?"
-            self.context.add_message("bot", initial_greeting)
-            self.context.waiting_for_name = True
-            return [[None, initial_greeting]]
-        
-        # Handle name collection if this is the first user message
-        if self.context.waiting_for_name and not self.context.introduced:
-            common_greetings = ["hi", "hey", "hello", "greetings", "howdy", "hiya"]
-            words = user_message.strip().split()
-            potential_name = None
-            
-            if "i'm" in user_message.lower() or "im" in user_message.lower():
-                parts = user_message.lower().replace("i'm", "im").split("im")
-                if len(parts) > 1 and parts[1].strip():
-                    potential_name = parts[1].strip().split()[0].capitalize()
-            
-            elif "my name is" in user_message.lower():
-                parts = user_message.lower().split("my name is")
-                if len(parts) > 1 and parts[1].strip():
-                    potential_name = parts[1].strip().split()[0].capitalize()
-            
-            elif len(words) <= 3 and words[0].lower() not in common_greetings:
-                potential_name = words[0].capitalize()
-            
-            if potential_name:
-                potential_name = ''.join(c for c in potential_name if c.isalnum())
-            
-            if potential_name and len(potential_name) >= 2 and potential_name.lower() not in common_greetings:
-                self.context.user_name = potential_name
-                greeting_response = f"Nice to meet you, {self.context.user_name}! How are you feeling today?"
-            else:
-                self.context.user_name = "friend"
-                greeting_response = "Nice to meet you! How are you feeling today?"
-            
-            self.context.introduced = True
-            self.context.waiting_for_name = False
-            self.context.add_message("user", user_message)
-            self.context.add_message("bot", greeting_response)
-            
-            return chatbot_history + [[user_message, greeting_response]]
-        
-        # Regular message processing
-        emotion_data = self.classify_text(user_message)
-        self.context.add_message("user", user_message, emotion_data)
-        
-        # Generate the response
-        bot_response = self.generate_response(user_message, emotion_data)
-        self.context.add_message("bot", bot_response)
-        
-        # Create a simple emotion display text
-        emotion_text = self.format_emotion_text(emotion_data)
-        
-        # Combine emotion text with bot response
-        full_response = f"{emotion_text}\n\n{bot_response}" if emotion_text else bot_response
-        
-        # Return updated chat history in the expected tuple format
-        return chatbot_history + [[user_message, full_response]]
-    
-    def reset_conversation(self):
-        """Reset the conversation context"""
-        self.context = ChatbotContext()
-        return []
-
-# Create the Gradio interface
-def create_gradio_interface():
-    # Initialize the chatbot with default models
-    emotion_model_id = os.environ.get("EMOTION_MODEL_ID", "suku9/emotion-classifier")
-    response_model_id = os.environ.get("RESPONSE_MODEL_ID", "mistralai/Mistral-7B-Instruct-v0.2")
-    
-    chatbot = GradioEmotionChatbot(emotion_model_id, response_model_id)
-    
-    # Create the Gradio interface with dark mode styling
-    custom_css = """
-    /* Dark mode styling */
-    body {
-        background-color: #1a1a1a !important;
-        color: #e0e0e0 !important;
-    }
-    
-    .gradio-container {
-        max-width: 750px !important;
-        margin: auto !important;
-        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif !important;
-        border-radius: 12px !important;
-        background: #2d2d2d !important;
-        padding: 15px !important;
-    }
-
-    /* Chatbot header styling */
-    .gradio-container h1, #header {
-        color: #a29bfe !important;
-        text-align: center !important;
-        font-size: 1.8rem !important;
-        margin-bottom: 5px !important;
-        font-weight: 600 !important;
-    }
-
-    .gradio-container p, #subheader {
-        text-align: center !important;
-        color: #b0b0b0 !important;
-        margin-bottom: 15px !important;
-        font-size: 0.9rem !important;
-    }
-
-    /* Chatbot window styling */
-    #chatbot {
-        height: 380px !important;
-        overflow: auto !important;
-        border-radius: 10px !important;
-        background-color: #1a1a1a !important;
-        border: 1px solid #3d3d3d !important;
-        padding: 10px !important;
-        margin-bottom: 15px !important;
-    }
-    
-    /* Force horizontal text orientation for ALL elements */
-    * {
-        writing-mode: horizontal-tb !important;
-        text-orientation: mixed !important;
-        direction: ltr !important;
-    }
-    
-    /* Message styling */
-    .message {
-        border-radius: 12px !important;
-        padding: 8px 12px !important;
-        margin: 5px 0 !important;
-        max-width: 85% !important;
-        width: 250px !important;
-        word-break: break-word !important;
-        writing-mode: horizontal-tb !important;
-        text-orientation: mixed !important;
-        direction: ltr !important;
-    }
-    
-    .user-message {
-        background-color: #4a5568 !important;
-        color: #e2e8f0 !important;
-        writing-mode: horizontal-tb !important;
-        text-orientation: mixed !important;
-    }
-    
-    .bot-message {
-        background-color: #553c9a !important;
-        color: #f8f9fa !important;
-        writing-mode: horizontal-tb !important;
-        text-orientation: mixed !important;
-    }
-
-    /* User input styling - FIX FOR VERTICAL TEXT */
-    #user-input, .gradio-container textarea, .gradio-container input[type="text"] {
-        background-color: #2d2d2d !important;
-        color: #e0e0e0 !important;
-        border-radius: 20px !important;
-        padding: 10px 15px !important;
-        border: 1px solid #3d3d3d !important;
-        margin-bottom: 10px !important;
-        writing-mode: horizontal-tb !important;
-        text-orientation: mixed !important;
-        direction: ltr !important;
-        width: 100% !important;
-        min-height: 45px !important;
-        height: auto !important;
-        resize: none !important;
-    }
-    
-    /* Force text orientation for any text inputs */
-    .cm-editor, .cm-scroller, .cm-content, .cm-line {
-        writing-mode: horizontal-tb !important;
-        text-orientation: mixed !important;
-    }
-    
-    /* Ensure row is horizontal */
-    .gradio-row {
-        flex-direction: row !important;
-    }
-
-    /* Fix for chat bubbles */
-    .chat, .chat > div, .chat > div > div, .chat-msg, .chat-msg > div, .chat-msg-content {
-        writing-mode: horizontal-tb !important;
-        text-orientation: mixed !important;
-    }
-
-    /* Apply horizontal text to all text elements in chatbot */
-    .prose, .prose p, .prose span, .text-input-with-enter {
-        writing-mode: horizontal-tb !important;
-        text-orientation: mixed !important;
-        direction: ltr !important;
-    }
-    
-    /* Target the specific user bubble on the right side */
-    .gradio-chatbot > div > div {
-        writing-mode: horizontal-tb !important;
-        text-orientation: mixed !important;
-        direction: ltr !important;
-    }
-
-    /* Target any text inside chatbot bubbles */
-    .gradio-chatbot * {
-        writing-mode: horizontal-tb !important;
-        text-orientation: mixed !important;
-        direction: ltr !important;
-    }
-
-    /* AVATAR AND USERNAME FIXES */
-    .avatar, .avatar-container, .avatar-image, .user-avatar, .bot-avatar {
-        writing-mode: horizontal-tb !important;
-        text-orientation: mixed !important;
-        direction: ltr !important;
-    }
-
-    /* Fix for specific containers that might be causing issues */
-    [class*="message"], [class*="bubble"], [class*="avatar"], [class*="chat"] {
-        writing-mode: horizontal-tb !important;
-        text-orientation: mixed !important;
-        direction: ltr !important;
-    }
-
-    /* Button styling */
-    .send-btn, .clear-btn {
-        background-color: #6c5ce7 !important;
-        color: white !important;
-        border: none !important;
-        border-radius: 20px !important;
-        padding: 8px 16px !important;
-        font-weight: 600 !important;
-        cursor: pointer !important;
-        transition: all 0.3s ease !important;
-    }
-
-    .send-btn:hover, .clear-btn:hover {
-        background-color: #5649c1 !important;
-        transform: translateY(-1px) !important;
-    }
-
-    .clear-btn {
-        background-color: #e74c3c !important;
-    }
-
-    .clear-btn:hover {
-        background-color: #c0392b !important;
-    }
-
-    /* Hide footer */
-    footer {
-        display: none !important;
-    }
-    
-    /* Fix scrollbar */
-    ::-webkit-scrollbar {
-        width: 6px;
-        background-color: #1a1a1a;
-    }
-    
-    ::-webkit-scrollbar-thumb {
-        background-color: #4a4a4a;
-        border-radius: 3px;
-    }
-    """
-    
-    with gr.Blocks(css=custom_css) as demo:
-        gr.Markdown("# EmotionChat", elem_id="header")
-        gr.Markdown("A supportive chatbot that understands how you feel", elem_id="subheader")
-        
-        # Chat interface with improved styling
-        chatbot_interface = gr.Chatbot(
-            elem_id="chatbot",
-            show_label=False,
-            height=380,
-            avatar_images=["https://em-content.zobj.net/source/microsoft-teams/363/bust-in-silhouette_1f464.png", 
-                           "https://em-content.zobj.net/source/microsoft-teams/363/robot_1f916.png"],
-        )
-        
-        # Input and button row with better styling
-        with gr.Row():
-            user_input = gr.Textbox(
-                placeholder="Type your message here...",
-                show_label=False,
-                container=False,
-                scale=9,
-                elem_id="user-input",
-                lines=1,
-                max_lines=1,
-                rtl=False
-            )
-            submit_btn = gr.Button("Send", scale=1, elem_classes="send-btn")
-        
-        # New conversation button
-        clear_btn = gr.Button("New Conversation", elem_classes="clear-btn")
-        
-        # Set up the event handlers
-        submit_btn.click(
-            chatbot.process_message,
-            inputs=[user_input, chatbot_interface],
-            outputs=[chatbot_interface],
-        ).then(
-            lambda: "",  # Clear the input box after sending
-            None,
-            [user_input],
-        )
-        
-        user_input.submit(
-            chatbot.process_message,
-            inputs=[user_input, chatbot_interface],
-            outputs=[chatbot_interface],
-        ).then(
-            lambda: "",  # Clear the input box after sending
-            None,
-            [user_input],
-        )
-        
-        clear_btn.click(
-            chatbot.reset_conversation,
-            inputs=None,
-            outputs=[chatbot_interface],
-        )
-    
-    return demo
-
-if __name__ == "__main__":
-    demo = create_gradio_interface()
+import gradio as gr
+import torch
+import os
+import json
+import re
+import random
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSequenceClassification,
+    AutoModelForCausalLM,
+    pipeline,
+)
+import datetime
+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
+        self.waiting_for_name = 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
+        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()
+
+class GradioEmotionChatbot:
+    def __init__(self, emotion_model_id, response_model_id=None, confidence_threshold=0.3):
+        self.emotion_model_id = emotion_model_id
+        self.response_model_id = response_model_id or "mistralai/Mistral-7B-Instruct-v0.2"
+        self.confidence_threshold = confidence_threshold
+        self.context = ChatbotContext()
+        self.initialize_models()
+        
+    def initialize_models(self):
+        # Initialize emotion classification model
+        print(f"Loading emotion classification model: {self.emotion_model_id}")
+        try:
+            self.emotion_model = AutoModelForSequenceClassification.from_pretrained(self.emotion_model_id)
+            self.emotion_tokenizer = AutoTokenizer.from_pretrained(self.emotion_model_id)
+            
+            self.emotion_classifier = pipeline(
+                "text-classification",
+                model=self.emotion_model,
+                tokenizer=self.emotion_tokenizer,
+                top_k=None  # Returns scores for all labels
+            )
+            print("Emotion classification model loaded successfully!")
+        except Exception as e:
+            print(f"Error loading emotion classification model: {e}")
+            # Fallback to a dummy classifier for demo purposes
+            self.emotion_classifier = lambda text: [[{"label": "neutral", "score": 1.0}]]
+        
+        # Initialize response generation model (or use fallback)
+        print(f"Loading response generation model: {self.response_model_id}")
+        try:
+            self.response_model = AutoModelForCausalLM.from_pretrained(
+                self.response_model_id,
+                torch_dtype=torch.float16,
+                device_map="auto"
+            )
+            self.response_tokenizer = AutoTokenizer.from_pretrained(self.response_model_id)
+            
+            self.response_generator = pipeline(
+                "text-generation",
+                model=self.response_model,
+                tokenizer=self.response_tokenizer,
+                do_sample=True,
+                top_p=0.92,
+                top_k=50,
+                temperature=0.7,
+                max_new_tokens=100
+            )
+            print("Response generation model loaded successfully!")
+        except Exception as e:
+            print(f"Using fallback response generation. Reason: {e}")
+            self.response_generator = self.fallback_response_generator
+    
+    def fallback_response_generator(self, prompt, **kwargs):
+        """Fallback response generator using templates"""
+        # Try to extract emotion from the prompt
+        emotion_match = re.search(r"emotion: (\w+)", prompt.lower())
+        if emotion_match:
+            emotion = emotion_match.group(1)
+        else:
+            emotion = "neutral"
+            
+        # Default user name
+        user_name = "friend"
+        name_match = re.search(r"Your friend \((.*?)\)", prompt.lower())
+        if name_match:
+            user_name = name_match.group(1)
+        
+        # Extract user message
+        message_match = re.search(r"message: \"(.*?)\"", prompt)
+        user_message = message_match.group(1) if message_match else ""
+        
+        # Generate response using fallback method
+        response = self.natural_fallback_response(user_message, emotion, user_name)
+        
+        # Format as if coming from the pipeline
+        return [{"generated_text": response}]
+    
+    def natural_fallback_response(self, user_message, primary_emotion, user_name):
+        """Conversational fallback responses that sound like a supportive friend"""
+        # Define emotion categories
+        sad_emotions = ["sadness", "disappointment", "grief", "remorse"]
+        fear_emotions = ["fear", "nervousness", "anxiety"]
+        anger_emotions = ["anger", "annoyance", "disapproval", "disgust"] 
+        joy_emotions = ["joy", "admiration", "amusement", "excitement", "optimism", 
+                       "gratitude", "pride", "love", "relief"]
+        
+        # Multi-stage response templates - more natural and varied
+        if primary_emotion in joy_emotions:
+            responses = [
+                f"That's awesome, {user_name}! What made you feel that way?",
+                f"I'm so glad to hear that! Tell me more about it?",
+                f"That's great news! What else is going on with you lately?"
+            ]
+        elif primary_emotion in sad_emotions:
+            responses = [
+                f"I'm sorry to hear that, {user_name}. Want to talk about what happened?",
+                f"That sounds rough. What's been going on?",
+                f"Ugh, that's tough. How are you handling it?"
+            ]
+        elif primary_emotion in anger_emotions:
+            responses = [
+                f"That sounds really frustrating. What happened?",
+                f"Oh no, that would upset me too. Want to vent about it?",
+                f"I can see why you'd be upset about that. What are you thinking of doing?"
+            ]
+        elif primary_emotion in fear_emotions:
+            responses = [
+                f"That sounds scary, {user_name}. What's got you worried?",
+                f"I can imagine that would be stressful. What's on your mind about it?",
+                f"I get feeling anxious about that. What's the biggest concern for you?"
+            ]
+        else:  # neutral emotions
+            responses = [
+                f"What's been on your mind lately, {user_name}?",
+                f"How's everything else going with you?",
+                f"Tell me more about what's going on in your life these days."
+            ]
+        
+        return random.choice(responses)
+    
+    def classify_text(self, text):
+        """Classify text and return emotion data"""
+        try:
+            results = self.emotion_classifier(text)
+            
+            # Sort emotions by score in descending order
+            sorted_emotions = sorted(results[0], key=lambda x: x['score'], reverse=True)
+            
+            # Process emotions above threshold
+            detected_emotions = []
+            for emotion in sorted_emotions:
+                # Map numerical label to emotion name
+                try:
+                    label_id = int(emotion['label'].split('_')[-1]) if '_' in emotion['label'] else int(emotion['label'])
+                    if 0 <= label_id < len(EMOTION_LABELS):
+                        emotion_name = EMOTION_LABELS[label_id]
+                    else:
+                        emotion_name = emotion['label']
+                except (ValueError, IndexError):
+                    emotion_name = emotion['label']
+                    
+                score = emotion['score']
+                
+                if score >= self.confidence_threshold:
+                    detected_emotions.append({"emotion": emotion_name, "score": score})
+            
+            # If no emotions detected above threshold, add neutral
+            if not detected_emotions:
+                detected_emotions.append({"emotion": "neutral", "score": 1.0})
+                
+            return detected_emotions
+        except Exception as e:
+            print(f"Error during classification: {e}")
+            # Return neutral as fallback
+            return [{"emotion": "neutral", "score": 1.0}]
+    
+    def format_emotion_text(self, emotion_data):
+        """Create a simple emotion text display"""
+        if not emotion_data:
+            return ""
+            
+        # Define emotion emojis
+        emotion_emojis = {
+            "joy": "😊", "admiration": "🤩", "amusement": "😄", "approval": "👍",
+            "excitement": "🎉", "gratitude": "🙏", "love": "❤️", "optimism": "🌟",
+            "pride": "🦚", "relief": "😌", "sadness": "😢", "disappointment": "😞",
+            "grief": "💔", "remorse": "😔", "embarrassment": "😳", "anger": "😠",
+            "annoyance": "😤", "disapproval": "👎", "disgust": "🤢", "fear": "😨",
+            "nervousness": "😰", "surprise": "😲", "confusion": "😕", "curiosity": "🤔",
+            "neutral": "😐", "realization": "💡", "desire": "✨"
+        }
+        
+        # Format the primary emotion
+        primary = emotion_data[0]["emotion"]
+        emoji = emotion_emojis.get(primary, "😐")
+        score = emotion_data[0]["score"]
+        
+        return f"Detected: {emoji} {primary.capitalize()} ({score:.2f})"
+        
+    def generate_response(self, user_message, emotion_data):
+        """Generate a response based on the user's message and detected emotions"""
+        # Get the primary emotion with context awareness
+        primary_emotion = emotion_data[0]["emotion"] if emotion_data else "neutral"
+        
+        # Get recent conversation history for context
+        recent_exchanges = self.context.get_recent_messages(6)
+        conversation_history = ""
+        for msg in recent_exchanges:
+            role = "Friend" if msg["role"] == "user" else self.context.bot_name
+            conversation_history += f"{role}: {msg['text']}\n"
+        
+        # Check if this is a greeting
+        is_greeting = any(greeting in user_message.lower() for greeting in ["hi", "hello", "hey", "greetings"])
+        is_question_about_bot = "how are you" in user_message.lower() or any(q in user_message.lower() for q in ["what can you do", "who are you", "what are you", "your purpose"])
+        
+        # Handle special cases
+        if is_greeting:
+            if len(self.context.conversation_history) <= 4:  # First greeting exchange
+                return f"Hi! I'm {self.context.bot_name}. It's nice to meet you. How are you feeling today?"
+            else:
+                return f"Hey! Good to chat with you again. What's been going on with you?"
+        
+        elif is_question_about_bot:
+            return f"I'm doing well, thanks for asking! I'm {self.context.bot_name}, here as a friend to chat whenever you need someone to talk to. What's on your mind today?"
+        
+        # Create a more conversational prompt based on emotion
+        system_instruction = f"""You are {self.context.bot_name}, having a natural conversation with your friend. You should respond in a casual, warm way like a supportive friend would - not like a therapist or clinical chatbot.
+
+Your friend seems to be feeling {primary_emotion}. In your response:
+1. Be genuinely empathetic but natural - like how a real friend would respond
+2. Keep your response short (1-3 sentences) and conversational
+3. Don't use phrases like "I understand" or "I'm here for you" too much - vary your language
+4. Use casual language, contractions (don't instead of do not), and occasional sentence fragments
+5. Don't sound formulaic or overly positive - be authentic
+6. Keep the same emotional tone throughout your response
+7. Don't explain what you're doing or add meta-commentary
+8. DON'T address them by name multiple times or at the end of sentences - it sounds unnatural
+9. Don't end with "Let me know what you'd prefer" or similar phrases
+
+Recent conversation:
+{conversation_history}
+
+Your friend's message: "{user_message}"
+Current emotion: {primary_emotion}
+
+Respond naturally as a supportive friend (without using their name more than once if at all):"""
+        
+        try:
+            # Generate the response
+            generated = self.response_generator(
+                system_instruction,
+                max_new_tokens=100,
+                do_sample=True,
+                temperature=0.8,
+                top_p=0.92,
+                top_k=50,
+            )
+            
+            # Extract the generated text
+            if isinstance(generated, list):
+                response_text = generated[0].get('generated_text', '')
+            else:
+                response_text = generated.get('generated_text', '')
+            
+            # Clean up the response - extract only the actual response without system prompt
+            if "[/INST]" in response_text:
+                parts = response_text.split("[/INST]")
+                if len(parts) > 1:
+                    response_text = parts[1].strip()
+            
+            # If we're still getting the system instruction, try an alternative approach
+            if "Your friend seems to be feeling" in response_text:
+                # Try to extract just the bot's response using pattern matching
+                match = re.search(r'Respond naturally as a supportive friend.*?:\s*(.*?)$', response_text, re.DOTALL)
+                if match:
+                    response_text = match.group(1).strip()
+                else:
+                    # If that fails, try another approach - take text after the last numbered instruction
+                    match = re.search(r'9\.\s+[^\n]+\s*(.*?)$', response_text, re.DOTALL)
+                    if match:
+                        response_text = match.group(1).strip()
+                    else:
+                        # Last resort: pick a fallback response based on emotion
+                        response_text = self.natural_fallback_response(user_message, primary_emotion, self.context.user_name or "friend")
+            
+            # Remove any model-specific markers
+            response_text = response_text.replace("<s>", "").replace("</s>", "")
+            
+            # Remove any internal notes or debugging info that might appear
+            if "Note:" in response_text:
+                response_text = response_text.split("Note:")[0].strip()
+            
+            # Remove any metadata or system-like text
+            response_text = response_text.replace("Assistant:", "").replace(f"{self.context.bot_name}:", "").strip()
+            
+            # Remove any quotation marks surrounding the response
+            response_text = response_text.strip('"').strip()
+            
+            # Handle potential model halt mid-sentence
+            if response_text.endswith((".", "!", "?")):
+                pass  # Response ends with proper punctuation
+            else:
+                # Try to find the last complete sentence
+                last_period = max(response_text.rfind("."), response_text.rfind("!"), response_text.rfind("?"))
+                if last_period > len(response_text) * 0.5:  # If we've got at least half the response
+                    response_text = response_text[:last_period+1]
+            
+            # FINAL CHECK: If we still have parts of the system prompt, use fallback response
+            if any(phrase in response_text for phrase in ["Your friend seems to be feeling", "Keep your response short", "Be genuinely empathetic"]):
+                response_text = self.natural_fallback_response(user_message, primary_emotion, self.context.user_name or "friend")
+            
+            return clean_response_text(response_text.strip(), self.context.user_name)
+            
+        except Exception as e:
+            print(f"Error generating response: {e}")
+            return self.natural_fallback_response(user_message, primary_emotion, self.context.user_name or "friend")
+    
+    def process_message(self, user_message, chatbot_history):
+        """Process a user message and return the chatbot response"""
+        # Initialize context if first message
+        if not self.context.conversation_history:
+            initial_greeting = f"Hi! I'm {self.context.bot_name}, your friendly emotional support chatbot. Who am I talking to today?"
+            self.context.add_message("bot", initial_greeting)
+            self.context.waiting_for_name = True
+            return [[None, initial_greeting]]
+        
+        # Handle name collection if this is the first user message
+        if self.context.waiting_for_name and not self.context.introduced:
+            common_greetings = ["hi", "hey", "hello", "greetings", "howdy", "hiya"]
+            words = user_message.strip().split()
+            potential_name = None
+            
+            if "i'm" in user_message.lower() or "im" in user_message.lower():
+                parts = user_message.lower().replace("i'm", "im").split("im")
+                if len(parts) > 1 and parts[1].strip():
+                    potential_name = parts[1].strip().split()[0].capitalize()
+            
+            elif "my name is" in user_message.lower():
+                parts = user_message.lower().split("my name is")
+                if len(parts) > 1 and parts[1].strip():
+                    potential_name = parts[1].strip().split()[0].capitalize()
+            
+            elif len(words) <= 3 and words[0].lower() not in common_greetings:
+                potential_name = words[0].capitalize()
+            
+            if potential_name:
+                potential_name = ''.join(c for c in potential_name if c.isalnum())
+            
+            if potential_name and len(potential_name) >= 2 and potential_name.lower() not in common_greetings:
+                self.context.user_name = potential_name
+                greeting_response = f"Nice to meet you, {self.context.user_name}! How are you feeling today?"
+            else:
+                self.context.user_name = "friend"
+                greeting_response = "Nice to meet you! How are you feeling today?"
+            
+            self.context.introduced = True
+            self.context.waiting_for_name = False
+            self.context.add_message("user", user_message)
+            self.context.add_message("bot", greeting_response)
+            
+            return chatbot_history + [[user_message, greeting_response]]
+        
+        # Regular message processing
+        emotion_data = self.classify_text(user_message)
+        self.context.add_message("user", user_message, emotion_data)
+        
+        # Generate the response
+        bot_response = self.generate_response(user_message, emotion_data)
+        self.context.add_message("bot", bot_response)
+        
+        # Create a simple emotion display text
+        emotion_text = self.format_emotion_text(emotion_data)
+        
+        # Combine emotion text with bot response
+        full_response = f"{emotion_text}\n\n{bot_response}" if emotion_text else bot_response
+        
+        # Return updated chat history in the expected tuple format
+        return chatbot_history + [[user_message, full_response]]
+    
+    def reset_conversation(self):
+        """Reset the conversation context"""
+        self.context = ChatbotContext()
+        return []
+
+# Create the Gradio interface
+import gradio as gr
+import os
+
+def create_gradio_interface():
+    # Initialize the chatbot with default models
+    emotion_model_id = os.environ.get("EMOTION_MODEL_ID", "suku9/emotion-classifier")
+    response_model_id = os.environ.get("RESPONSE_MODEL_ID", "mistralai/Mistral-7B-Instruct-v0.2")
+    
+    chatbot = GradioEmotionChatbot(emotion_model_id, response_model_id)
+    
+    # Create the Gradio interface with dark mode styling
+    custom_css = """
+    /* Dark mode styling */
+    body {
+        background-color: #1a1a1a !important;
+        color: #e0e0e0 !important;
+    }
+    
+    .gradio-container {
+        max-width: 1200px !important; /* Increased width for horizontal expansion */
+        margin: auto !important;
+        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif !important;
+        border-radius: 12px !important;
+        background: #2d2d2d !important;
+        padding: 20px !important;
+    }
+
+    /* Chatbot header styling */
+    .gradio-container h1, #header {
+        color: #a29bfe !important;
+        text-align: center !important;
+        font-size: 2.2rem !important; /* Larger font for better visibility */
+        margin-bottom: 8px !important;
+        font-weight: 700 !important;
+        text-shadow: 0 0 2px rgba(0,0,0,0.5) !important; /* Subtle shadow for clarity */
+    }
+
+    .gradio-container p, #subheader {
+        text-align: center !important;
+        color: #d0d0d0 !important; /* Lighter color for better contrast */
+        margin-bottom: 20px !important;
+        font-size: 1.1rem !important; /* Slightly larger font */
+        font-weight: 400 !important;
+    }
+
+    /* Chatbot window styling */
+    #chatbot {
+        height: 450px !important; /* Slightly taller for better content display */
+        overflow: auto !important;
+        border-radius: 10px !important;
+        background-color: #1a1a1a !important;
+        border: 1px solid #3d3d3d !important;
+        padding: 15px !important;
+        margin-bottom: 20px !important;
+    }
+    
+    /* Force horizontal text orientation for ALL elements */
+    * {
+        writing-mode: horizontal-tb !important;
+        text-orientation: mixed !important;
+        direction: ltr !important;
+    }
+    
+    /* Message styling */
+    .message {
+        border-radius: 12px !important;
+        padding: 10px 15px !important;
+        margin: 8px 0 !important;
+        max-width: 70% !important; /* Adjusted for horizontal expansion */
+        width: auto !important; /* Allow messages to expand */
+        word-break: break-word !important;
+        font-size: 1rem !important; /* Clearer font size */
+        line-height: 1.4 !important; /* Improved readability */
+        text-shadow: 0 0 1px rgba(0,0,0,0.3) !important; /* Subtle shadow for text clarity */
+    }
+    
+    .user-message {
+        background-color: #4a5568 !important;
+        color: #f0f4f8 !important; /* Lighter text for contrast */
+        margin-left: auto !important; /* Align user messages to the right */
+    }
+    
+    .bot-message {
+        background-color: #553c9a !important;
+        color: #ffffff !important; /* Pure white for maximum clarity */
+        margin-right: auto !important; /* Align bot messages to the left */
+    }
+
+    /* User input styling */
+    #user-input, .gradio-container textarea, .gradio-container input[type="text"] {
+        background-color: #2d2d2d !important;
+        color: #e0e0e0 !important;
+        border-radius: 20px !important;
+        padding: 12px 18px !important;
+        border: 1px solid #4a4a4a !important;
+        margin-bottom: 15px !important;
+        writing-mode: horizontal-tb !important;
+        text-orientation: mixed !important;
+        direction: ltr !important;
+        width: 100% !important;
+        min-height: 50px !important;
+        height: auto !important;
+        resize: none !important;
+        font-size: 1rem !important; /* Clearer font size */
+    }
+    
+    /* Force text orientation for any text inputs */
+    .cm-editor, .cm-scroller, .cm-content, .cm-line {
+        writing-mode: horizontal-tb !important;
+        text-orientation: mixed !important;
+    }
+    
+    /* Ensure row is horizontal */
+    .gradio-row {
+        flex-direction: row !important;
+        gap: 10px !important; /* Add spacing between elements */
+    }
+
+    /* Fix for chat bubbles */
+    .chat, .chat > div, .chat > div > div, .chat-msg, .chat-msg > div, .chat-msg-content {
+        writing-mode: horizontal-tb !important;
+        text-orientation: mixed !important;
+    }
+
+    /* Apply horizontal text to all text elements in chatbot */
+    .prose, .prose p, .prose span, .text-input-with-enter {
+        writing-mode: horizontal-tb !important;
+        text-orientation: mixed !important;
+        direction: ltr !important;
+    }
+    
+    /* Target the specific user bubble on the right side */
+    .gradio-chatbot > div > div {
+        writing-mode: horizontal-tb !important;
+        text-orientation: mixed !important;
+        direction: ltr !important;
+    }
+
+    /* Target any text inside chatbot bubbles */
+    .gradio-chatbot * {
+        writing-mode: horizontal-tb !important;
+        text-orientation: mixed !important;
+        direction: ltr !important;
+    }
+
+    /* AVATAR AND USERNAME FIXES */
+    .avatar, .avatar-container, .avatar-image, .user-avatar, .bot-avatar {
+        writing-mode: horizontal-tb !important;
+        text-orientation: mixed !important;
+        direction: ltr !important;
+    }
+
+    /* Fix for specific containers */
+    [class*="message"], [class*="bubble"], [class*="avatar"], [class*="chat"] {
+        writing-mode: horizontal-tb !important;
+        text-orientation: mixed !important;
+        direction: ltr !important;
+    }
+
+    /* Button styling */
+    .send-btn, .clear-btn {
+        background-color: #6c5ce7 !important;
+        color: #ffffff !important;
+        border: none !important;
+        border-radius: 20px !important;
+        padding: 10px 20px !important;
+        font-weight: 600 !important;
+        cursor: pointer !important;
+        transition: all 0.3s ease !important;
+        font-size: 1rem !important;
+    }
+
+    .send-btn:hover, .clear-btn:hover {
+        background-color: #5649c1 !important;
+        transform: translateY(-1px) !important;
+    }
+
+    .clear-btn {
+        background-color: #e74c3c !important;
+    }
+
+    .clear-btn:hover {
+        background-color: #c0392b !important;
+    }
+
+    /* Hide footer */
+    footer {
+        display: none !important;
+    }
+    
+    /* Fix scrollbar */
+    ::-webkit-scrollbar {
+        width: 8px;
+        background-color: #1a1a1a;
+    }
+    
+    ::-webkit-scrollbar-thumb {
+        background-color: #4a4a4a;
+        border-radius: 4px;
+    }
+    """
+    
+    with gr.Blocks(css=custom_css) as demo:
+        gr.Markdown("# EmotionChat", elem_id="header")
+        gr.Markdown("A supportive chatbot that understands how you feel", elem_id="subheader")
+        
+        # Chat interface with improved styling
+        chatbot_interface = gr.Chatbot(
+            elem_id="chatbot",
+            show_label=False,
+            height=450,
+            avatar_images=["https://em-content.zobj.net/source/microsoft-teams/363/bust-in-silhouette_1f464.png", 
+                           "https://em-content.zobj.net/source/microsoft-teams/363/robot_1f916.png"],
+        )
+        
+        # Input and button row with better styling
+        with gr.Row():
+            user_input = gr.Textbox(
+                placeholder="Type your message here...",
+                show_label=False,
+                container=False,
+                scale=8,
+                elem_id="user-input",
+                lines=1,
+                max_lines=1,
+                rtl=False
+            )
+            submit_btn = gr.Button("Send", scale=2, elem_classes="send-btn")
+        
+        # New conversation button
+        clear_btn = gr.Button("New Conversation", elem_classes="clear-btn")
+        
+        # Set up the event handlers
+        submit_btn.click(
+            chatbot.process_message,
+            inputs=[user_input, chatbot_interface],
+            outputs=[chatbot_interface],
+        ).then(
+            lambda: "",  # Clear the input box after sending
+            None,
+            [user_input],
+        )
+        
+        user_input.submit(
+            chatbot.process_message,
+            inputs=[user_input, chatbot_interface],
+            outputs=[chatbot_interface],
+        ).then(
+            lambda: "",  # Clear
+
+ the input box after sending
+            None,
+            [user_input],
+        )
+        
+        clear_btn.click(
+            chatbot.reset_conversation,
+            inputs=None,
+            outputs=[chatbot_interface],
+        )
+    
+    return demo
+
+if __name__ == "__main__":
+    demo = create_gradio_interface()
     demo.launch(debug=True, share=True)