import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import numpy as np
import random
import re
import logging
from datetime import datetime

# Set up logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('boundary_assistant.log'),
        logging.StreamHandler()
    ]
)
logger = logging.getLogger(__name__)

class SimplifiedBoundaryAssistant:
    def __init__(self):
        # Initialize only the essential models
        self.models = {}
        self.tokenizers = {}
        
        # Model paths - keeping the 4 essential ones
        self.model_paths = {
            'boundary': 'SamanthaStorm/healthy-boundary-predictor',  # Your trained model
            'toxicity': 'unitary/toxic-bert',                       # Toxicity detection
            'emotion': 'j-hartmann/emotion-english-distilroberta-base',  # Emotion detection
            'fallacy': 'SamanthaStorm/fallacyfinder'                # Fallacy detection
        }
        
        # Load all models
        self.load_models()
        
        # Emotion labels for the emotion model
        self.emotion_labels = ['anger', 'disgust', 'fear', 'joy', 'neutral', 'sadness', 'surprise']
        
        # Emotion to need mapping
        self.emotion_to_needs = {
            'anger': ['respect', 'fairness', 'to be heard'],
            'sadness': ['comfort', 'understanding', 'connection'],
            'fear': ['safety', 'security', 'reassurance'],
            'disgust': ['boundaries', 'respect', 'different approach'],
            'neutral': ['clarity', 'understanding', 'communication'],
            'joy': ['connection', 'sharing', 'celebration'],
            'surprise': ['information', 'clarity', 'time to process']
        }
        
        # Fallacy labels (from your fallacy finder)
        self.fallacy_labels = {
            'ad_hominem': 'Ad Hominem (Personal Attack)',
            'strawman': 'Strawman (Misrepresenting Argument)', 
            'whataboutism': 'Whataboutism (Deflecting)',
            'gaslighting': 'Gaslighting (Questioning Reality)',
            'false_dichotomy': 'False Dichotomy (Only Two Options)',
            'appeal_to_emotion': 'Appeal to Emotion',
            'darvo': 'DARVO (Deny, Attack, Reverse)',
            'moving_goalposts': 'Moving Goalposts',
            'cherry_picking': 'Cherry Picking',
            'appeal_to_authority': 'Appeal to Authority',
            'slippery_slope': 'Slippery Slope',
            'motte_and_bailey': 'Motte and Bailey',
            'gish_gallop': 'Gish Gallop',
            'kafkatrapping': 'Kafkatrapping',
            'sealioning': 'Sealioning',
            'no_fallacy': 'No Fallacy'
        }

    def load_models(self):
        """Load the essential models"""
        logger.info("Starting model loading process...")
        for name, path in self.model_paths.items():
            try:
                logger.info(f"Loading {name} model: {path}")
                if name == 'fallacy':
                    # Special handling for fallacy model with 16 labels
                    self.tokenizers[name] = AutoTokenizer.from_pretrained(path)
                    self.models[name] = AutoModelForSequenceClassification.from_pretrained(path, num_labels=16)
                else:
                    self.tokenizers[name] = AutoTokenizer.from_pretrained(path)
                    self.models[name] = AutoModelForSequenceClassification.from_pretrained(path)
                logger.info(f"✅ {name} model loaded successfully!")
            except Exception as e:
                logger.error(f"❌ Error loading {name} model: {e}")
                self.models[name] = None
                self.tokenizers[name] = None

    def predict_with_model(self, text, model_name):
        """Make prediction with specified model"""
        if self.models[model_name] is None or self.tokenizers[model_name] is None:
            logger.warning(f"{model_name} model not available")
            return None, 0.0
        
        try:
            inputs = self.tokenizers[model_name](
                text, 
                return_tensors="pt", 
                truncation=True, 
                padding=True, 
                max_length=512
            )
            
            with torch.no_grad():
                outputs = self.models[model_name](**inputs)
                predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
                predicted_class_id = predictions.argmax().item()
                confidence = predictions.max().item()
                
                logger.debug(f"{model_name} prediction: class {predicted_class_id}, confidence {confidence:.3f}")
                return predicted_class_id, confidence
                    
        except Exception as e:
            logger.error(f"Error with {model_name} prediction: {e}")
            return None, 0.0

    def analyze_simplified(self, text):
        """Analysis using 4 essential models"""
        if not text.strip():
            return None
            
        logger.info(f"ANALYZING MESSAGE: '{text[:100]}{'...' if len(text) > 100 else ''}'")
        
        analysis = {
            'text': text,
            'issues': [],
            'underlying_needs': {
                'emotions': [],
                'core_needs': [],
                'likely_feelings': []
            },
            'severity_score': 0,
            'is_toxic': False,
            'is_healthy_boundary': True,
            'fallacies_detected': []
        }
        
        # 1. Emotion Detection (for understanding underlying needs)
        logger.info("Running emotion detection...")
        emotion_pred, emotion_conf = self.predict_with_model(text, 'emotion')
        if emotion_pred is not None and emotion_conf > 0.3:
            detected_emotion = self.emotion_labels[emotion_pred] if emotion_pred < len(self.emotion_labels) else 'neutral'
            logger.info(f"EMOTION DETECTED: {detected_emotion} (confidence: {emotion_conf:.3f})")
            analysis['underlying_needs']['emotions'].append({
                'emotion': detected_emotion,
                'confidence': emotion_conf
            })
            analysis['underlying_needs']['core_needs'].extend(self.emotion_to_needs.get(detected_emotion, ['understanding']))
            analysis['underlying_needs']['likely_feelings'].append(detected_emotion)
        else:
            logger.info(f"No strong emotion detected (pred: {emotion_pred}, conf: {emotion_conf})")
        
        # 2. Toxicity Detection
        logger.info("Running toxicity detection...")
        toxicity_pred, toxicity_conf = self.predict_with_model(text, 'toxicity')
        if toxicity_pred is not None and toxicity_pred == 1 and toxicity_conf > 0.7:  # 1 = TOXIC
            logger.info(f"TOXICITY DETECTED: confidence {toxicity_conf:.3f}")
            analysis['is_toxic'] = True
            analysis['issues'].append({
                'type': 'toxic_language',
                'name': 'Toxic Language Detected',
                'confidence': toxicity_conf,
                'severity': 3 if toxicity_conf > 0.9 else 2
            })
            analysis['severity_score'] += 3 if toxicity_conf > 0.9 else 2
        else:
            logger.info(f"No toxicity detected (pred: {toxicity_pred}, conf: {toxicity_conf})")
        
        # 3. Boundary Health Check (1=healthy, 0=unhealthy)
        logger.info("Running boundary health check...")
        boundary_pred, boundary_conf = self.predict_with_model(text, 'boundary')
        if boundary_pred is not None:
            if boundary_pred == 0 and boundary_conf > 0.7:  # 0 = unhealthy
                logger.info(f"UNHEALTHY BOUNDARY DETECTED: confidence {boundary_conf:.3f}")
                analysis['is_healthy_boundary'] = False
                analysis['issues'].append({
                    'type': 'unhealthy_boundary',
                    'name': 'Unhealthy Boundary Pattern',
                    'confidence': boundary_conf,
                    'severity': 2 if boundary_conf > 0.8 else 1
                })
                analysis['severity_score'] += 2 if boundary_conf > 0.8 else 1
            elif boundary_pred == 1:  # 1 = healthy
                logger.info(f"HEALTHY BOUNDARY DETECTED: confidence {boundary_conf:.3f}")
                analysis['is_healthy_boundary'] = True
        else:
            logger.info(f"Boundary health check failed")
        
        # 4. Fallacy Detection
        logger.info("Running fallacy detection...")
        fallacy_pred, fallacy_conf = self.predict_with_model(text, 'fallacy')
        if fallacy_pred is not None and fallacy_conf > 0.6:  # Higher threshold for fallacies
            fallacy_keys = list(self.fallacy_labels.keys())
            if fallacy_pred < len(fallacy_keys):
                fallacy_type = fallacy_keys[fallacy_pred]
                logger.info(f"FALLACY DETECTED: {fallacy_type} (confidence: {fallacy_conf:.3f})")
                if fallacy_type != 'no_fallacy':
                    analysis['fallacies_detected'].append(fallacy_type)
                    analysis['issues'].append({
                        'type': 'fallacy',
                        'subtype': fallacy_type,
                        'name': self.fallacy_labels[fallacy_type],
                        'confidence': fallacy_conf,
                        'severity': 3 if fallacy_type == 'gaslighting' else 2
                    })
                    analysis['severity_score'] += 3 if fallacy_type == 'gaslighting' else 2
                else:
                    logger.info(f"Fallacy model detected 'no_fallacy' - healthy communication")
        else:
            logger.info(f"No fallacy detected (pred: {fallacy_pred}, conf: {fallacy_conf})")
        
        logger.info(f"ANALYSIS COMPLETE - Severity score: {analysis['severity_score']}, Issues found: {len(analysis['issues'])}")
        logger.info("=" * 80)
        
        return analysis

    def generate_smart_boundary(self, analysis):
        """Generate boundary based on simplified analysis with emotion awareness"""
        if not analysis:
            return "I need to express my feelings about this situation. I'd like us to find a way to communicate that works for both of us."
        
        original_text = analysis['text'].lower()
        
        # Extract context and emotions
        relationship_context = self.infer_relationship_context(original_text)
        situation_context = self.extract_situation_context(original_text)
        
        # Use detected emotions for more empathetic boundaries
        detected_emotions = analysis['underlying_needs']['emotions']
        if detected_emotions:
            primary_emotion = detected_emotions[0]['emotion']  # Highest confidence emotion
            emotion_context = primary_emotion
        else:
            emotion_context = self.extract_emotional_context(original_text)
        
        # Handle different scenarios based on analysis
        if analysis['is_toxic']:
            return self.generate_non_toxic_boundary(emotion_context, situation_context, analysis['underlying_needs']['core_needs'])
        elif analysis['fallacies_detected']:
            # Handle fallacy-based responses
            primary_fallacy = analysis['fallacies_detected'][0]
            return self.generate_fallacy_response(primary_fallacy, situation_context, emotion_context)
        elif not analysis['is_healthy_boundary']:
            return self.generate_healthier_boundary(original_text, situation_context, emotion_context)
        elif analysis['issues']:
            # Has some issues but not major ones
            return self.generate_improved_boundary(situation_context, emotion_context)
        else:
            # No major issues detected, just polish it up
            return self.generate_polished_boundary(original_text, situation_context, emotion_context)

    def generate_non_toxic_boundary(self, emotion, situation, core_needs):
        """Generate boundary that removes toxic language"""
        needs_text = core_needs[0] if core_needs else 'understanding'
        
        if emotion == 'anger':
            return f"I'm feeling really frustrated about {situation}. I need {needs_text} and a chance to discuss this calmly when we can both listen to each other."
        elif emotion == 'sadness':
            return f"I'm feeling hurt about {situation}. I need {needs_text} and for us to find a way to talk about this that doesn't leave either of us feeling attacked."
        elif emotion == 'fear':
            return f"I'm feeling defensive about {situation}. I need {needs_text} and reassurance that we can work through this together respectfully."
        else:
            return f"I'm feeling overwhelmed about {situation}. I need {needs_text} and for us to approach this conversation with more care for each other's feelings."

    def generate_healthier_boundary(self, original_text, situation, emotion):
        """Generate healthier version of boundary"""
        # Look for specific unhealthy patterns in original text
        if any(word in original_text for word in ['always', 'never']):
            return f"I feel {emotion} when {situation} happens. I need us to find a specific solution for this particular issue."
        elif 'you need to' in original_text or 'you should' in original_text:
            return f"I feel {emotion} about {situation}. I'd appreciate it if we could work together on this."
        else:
            return f"I want to express how I feel about {situation}. I feel {emotion} and I need us to find a way to handle this that works for both of us."

    def generate_fallacy_response(self, fallacy_type, situation, emotion):
        """Generate response based on specific fallacy detected"""
        if 'ad_hominem' in fallacy_type:
            return f"I feel {emotion} when {situation} becomes personal. I need us to focus on the actual issues rather than attacking each other's character."
        elif 'gaslighting' in fallacy_type:
            return f"I feel {emotion} when my experiences about {situation} are questioned. I need my perspective to be acknowledged, even if we disagree."
        elif 'strawman' in fallacy_type:
            return f"I feel {emotion} when {situation} gets misrepresented. I need us to address what I'm actually saying."
        elif 'whataboutism' in fallacy_type:
            return f"I feel {emotion} when {situation} gets deflected to other issues. I need us to address this specific concern first."
        else:
            return f"I notice I feel {emotion} about {situation}. I need us to discuss this more constructively."

    def generate_improved_boundary(self, situation, emotion):
        """Generate improved boundary for minor issues"""
        return f"I'm feeling {emotion} about {situation}. I'd like us to step back and approach this differently so we can understand each other better."

    def generate_polished_boundary(self, original_text, situation, emotion):
        """Polish up already decent boundaries"""
        return f"I want to communicate about {situation}. I'm feeling {emotion} and would appreciate if we could work together to find a solution that feels good for both of us."

    def infer_relationship_context(self, text):
        """Infer relationship type from original text"""
        if any(word in text for word in ['love', 'babe', 'honey', 'relationship', 'partner']):
            return 'romantic'
        elif any(word in text for word in ['work', 'meeting', 'boss', 'office', 'colleague']):
            return 'professional'
        elif any(word in text for word in ['mom', 'dad', 'family', 'parent', 'sibling']):
            return 'family'
        else:
            return 'general'

    def extract_situation_context(self, text):
        """Extract specific situation being discussed"""
        if 'interrupt' in text:
            return 'interrupting during conversations'
        elif any(word in text for word in ['late', 'time', 'punctual']):
            return 'time and punctuality issues'
        elif 'listen' in text or 'hear' in text:
            return 'feeling unheard in our communication'
        elif any(word in text for word in ['talk', 'conversation', 'discuss']):
            return 'our communication patterns'
        elif 'respect' in text:
            return 'mutual respect in our interactions'
        else:
            return 'this situation'

    def extract_emotional_context(self, text):
        """Extract emotional undertones from original text"""
        if any(word in text for word in ['hurt', 'pain', 'upset']):
            return 'hurt'
        elif any(word in text for word in ['angry', 'mad', 'furious', 'annoying']):
            return 'frustrated'
        elif any(word in text for word in ['ignore', 'dismissed', 'unheard']):
            return 'unheard'
        elif any(word in text for word in ['disrespect', 'rude']):
            return 'disrespected'
        elif any(word in text for word in ['anxious', 'worried', 'stressed']):
            return 'anxious'
        else:
            return 'uncomfortable'
        """Infer relationship type from original text"""
        if any(word in text for word in ['love', 'babe', 'honey', 'relationship', 'partner']):
            return 'romantic'
        elif any(word in text for word in ['work', 'meeting', 'boss', 'office', 'colleague']):
            return 'professional'
        elif any(word in text for word in ['mom', 'dad', 'family', 'parent', 'sibling']):
            return 'family'
        else:
            return 'general'

    def extract_situation_context(self, text):
        """Extract specific situation being discussed"""
        if 'interrupt' in text:
            return 'interrupting during conversations'
        elif any(word in text for word in ['late', 'time', 'punctual']):
            return 'time and punctuality issues'
        elif 'listen' in text or 'hear' in text:
            return 'feeling unheard in our communication'
        elif any(word in text for word in ['talk', 'conversation', 'discuss']):
            return 'our communication patterns'
        elif 'respect' in text:
            return 'mutual respect in our interactions'
        else:
            return 'this situation'

    def extract_emotional_context(self, text):
        """Extract emotional undertones from original text"""
        if any(word in text for word in ['hurt', 'pain', 'upset']):
            return 'hurt'
        elif any(word in text for word in ['angry', 'mad', 'furious', 'annoying']):
            return 'frustrated'
        elif any(word in text for word in ['ignore', 'dismissed', 'unheard']):
            return 'unheard'
        elif any(word in text for word in ['disrespect', 'rude']):
            return 'disrespected'
        elif any(word in text for word in ['anxious', 'worried', 'stressed']):
            return 'anxious'
        else:
            return 'uncomfortable'

    def calculate_overall_score(self, analysis):
        """Calculate overall boundary health score"""
        if not analysis:
            return 85  # Good baseline if no analysis
        
        base_score = 100
        
        # Major penalties for severe issues
        if analysis['is_toxic']:
            base_score -= 40  # Major penalty for toxic language
        
        if not analysis['is_healthy_boundary']:
            base_score -= 25  # Significant penalty for unhealthy boundaries
        
        # Additional penalties based on severity
        severity_penalty = analysis['severity_score'] * 8
        
        final_score = max(0, base_score - severity_penalty)
        return final_score

    def format_analysis_feedback(self, analysis):
        """Format the analysis into user-friendly feedback"""
        if not analysis:
            return "🟡 **Unable to analyze:** Please try entering your message again.\n"
        
        if not analysis['issues']:
            return "✅ **Great communication!** No major issues detected. Your boundary setting approach looks healthy.\n"
        
        feedback = ""
        
        # Address major issues
        if analysis['is_toxic']:
            feedback += "⚠️ **Toxic Language Detected**\n\n"
            feedback += "• This communication contains language that may be harmful or aggressive\n"
            feedback += "• Consider focusing on specific behaviors rather than personal attacks\n"
            feedback += "• Using 'I' statements can help express feelings without blame\n\n"
        
        if not analysis['is_healthy_boundary']:
            feedback += "🔄 **Boundary Improvement Needed**\n\n"
            feedback += "• This boundary-setting approach may not be as effective\n"
            feedback += "• Consider focusing on your needs rather than what others should do\n"
            feedback += "• Express feelings without making demands or ultimatums\n\n"
        
        # Show fallacy detection
        if analysis['fallacies_detected']:
            feedback += "🧠 **Logical Issues Detected**\n\n"
            for fallacy in analysis['fallacies_detected']:
                fallacy_name = self.fallacy_labels.get(fallacy, fallacy)
                feedback += f"• **{fallacy_name}** detected\n"
            feedback += "• These patterns can make communication less effective\n"
            feedback += "• Focus on specific behaviors rather than general arguments\n\n"
        
        # Show emotional understanding
        if analysis['underlying_needs']['emotions']:
            feedback += "💙 **What You Might Be Feeling**\n\n"
            emotions = analysis['underlying_needs']['emotions']
            for emotion_data in emotions:
                emotion = emotion_data['emotion']
                confidence = emotion_data['confidence']
                feedback += f"• **{emotion.title()}** ({confidence*100:.0f}% confidence)\n"
            
            core_needs = list(set(analysis['underlying_needs']['core_needs']))  # Remove duplicates
            if core_needs:
                feedback += f"\n**You may need:** {', '.join(core_needs[:3])}\n\n"
        
        return feedback

    def process_boundary_request(self, raw_input):
        """Main function using simplified AI analysis"""
        if not raw_input.strip():
            return "Please enter your raw thoughts to get started.", "", ""
        
        # Simplified analysis using 3 models
        analysis = self.analyze_simplified(raw_input)
        
        # Format analysis feedback
        analysis_text = self.format_analysis_feedback(analysis)
        
        # Generate improved boundary
        improved = self.generate_smart_boundary(analysis)
        
        # Calculate score for explanation
        score = self.calculate_overall_score(analysis)
        
        # Generate explanation
        explanation = f"""
**Why this works better:**
✅ Uses "I" statements to express feelings
✅ Removes harmful communication patterns
✅ Focuses on needs rather than blame
✅ Creates opportunity for collaborative solution

**Original Message Health Score: {score}/100**

**Not quite right?** Use the "Custom Boundary Builder" below to create a more personalized boundary.
        """
        
        return analysis_text, improved, explanation

    def validate_boundary_smart(self, boundary_text):
        """Smart validation using simplified models"""
        if not boundary_text.strip():
            return "Please enter a boundary to check.", 0, []
            
        # Run simplified analysis on the boundary
        analysis = self.analyze_simplified(boundary_text)
        score = self.calculate_overall_score(analysis)
        
        # Determine rating
        if analysis and analysis['is_toxic']:
            rating = "🔴 HARMFUL - Contains Toxic Language"
        elif score >= 80:
            rating = "🟢 Excellent Boundary!"
        elif score >= 60:
            rating = "🔵 Good Boundary"
        elif score >= 40:
            rating = "🟡 Fair Boundary"
        else:
            rating = "🔴 Needs Improvement"

        # Format feedback
        feedback = f"**{rating}** (Score: {score}/100)\n\n"
        
        if analysis and analysis['issues']:
            feedback += self.format_analysis_feedback(analysis)
        else:
            feedback += "✅ **Great boundary!** No significant issues detected.\n"
            feedback += "• Uses respectful communication patterns\n"
            feedback += "• Shows healthy boundary-setting approach\n"
            feedback += "• Free of toxic language patterns\n"
        
        return feedback, score, analysis.get('issues', []) if analysis else []

    # Custom boundary generation methods (keeping the same)
    def generate_custom_boundary(self, behavior, relationship, feeling, need, setting, tone):
        """Generate a custom boundary based on guided questions"""
        
        # Relationship-specific language
        relationship_language = {
            'romantic_partner': {
                'address': 'babe/honey',
                'approach': 'collaborative',
                'example': 'I love you and want us to work through this together'
            },
            'friend': {
                'address': 'friend',
                'approach': 'direct but caring',
                'example': 'I value our friendship and want to talk about something'
            },
            'family_member': {
                'address': 'family',
                'approach': 'respectful but firm',
                'example': 'I respect you and need to share something important'
            },
            'coworker': {
                'address': 'colleague',
                'approach': 'professional',
                'example': 'I wanted to discuss our working relationship'
            },
            'boss': {
                'address': 'professional',
                'approach': 'formal but assertive',
                'example': 'I\'d like to discuss some workplace concerns'
            },
            'child': {
                'address': 'child',
                'approach': 'teaching moment',
                'example': 'Let\'s talk about how we treat each other'
            }
        }
        
        # Setting-specific approaches
        setting_approaches = {
            'private_conversation': 'when we\'re alone',
            'public_space': 'respectfully in public',
            'work_environment': 'in a professional setting',
            'family_gathering': 'during family time',
            'text_message': 'via text',
            'email': 'in writing'
        }
        
        # Tone variations
        tone_styles = {
            'gentle': 'I gently need to share',
            'firm': 'I need to be clear about',
            'direct': 'I want to directly address',
            'diplomatic': 'I\'d like to diplomatically discuss'
        }
        
        rel_info = relationship_language.get(relationship, relationship_language['friend'])
        setting_phrase = setting_approaches.get(setting, 'in our interactions')
        tone_phrase = tone_styles.get(tone, 'I need to share')
        
        # Generate boundary with customization
        templates = [
            f"{tone_phrase} that I feel {feeling} when {behavior}. I need {need} for our relationship to work well.",
            f"I want to talk about how I feel {feeling} when {behavior}. Moving forward, I need {need}.",
            f"I've noticed I feel {feeling} when {behavior} happens. Could we work together so I can have {need}?",
            f"I need to share that {behavior} makes me feel {feeling}. I'm hoping we can find a way for me to have {need}."
        ]
        
        boundary = random.choice(templates)
        
        # Add relationship-specific context
        context = f"\n\n**Tailored for {relationship.replace('_', ' ')}:**\n"
        context += f"• Consider saying this {setting_phrase}\n"
        context += f"• Remember: {rel_info['example']}\n"
        context += f"• Approach: {rel_info['approach']}"
        
        return boundary + context

# Initialize the simplified assistant
assistant = SimplifiedBoundaryAssistant()

# Gradio interface functions
def analyze_and_improve(raw_input):
    return assistant.process_boundary_request(raw_input)

def check_boundary_smart(boundary_text):
    feedback, score, issues = assistant.validate_boundary_smart(boundary_text)
    return feedback

def build_custom_boundary(behavior, relationship, feeling, need, setting, tone):
    if not all([behavior, relationship, feeling, need]):
        return "Please fill in all required fields to generate your custom boundary."
    
    custom_boundary = assistant.generate_custom_boundary(behavior, relationship, feeling, need, setting, tone)
    return custom_boundary

def show_custom_builder():
    return gr.update(visible=True)

def hide_custom_builder():
    return gr.update(visible=False)

# Create the Gradio interface
with gr.Blocks(
    theme=gr.themes.Soft(primary_hue="blue", secondary_hue="purple"),
    title="Smart Boundary Writing Assistant",
    css="""
    .gradio-container {
        max-width: 1200px !important;
    }
    """
) as demo:
    
    gr.Markdown(
        """
        # 🛡️ Smart Boundary Writing Assistant
        
        **Powered by 3 essential AI models:** Toxicity Detection, Your Custom Boundary Predictor, and Emotion Recognition.
        
        Transform your raw thoughts into healthy, effective boundaries with AI-powered insights.
        """
    )
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("## 📝 Step 1: Enter Your Raw Thoughts")
            
            raw_input = gr.Textbox(
                label="What boundary do you need to set?",
                placeholder="e.g., 'You always interrupt me and never listen to what I'm saying'",
                lines=4,
                info="Don't worry about being perfect - our AI will analyze and improve it!"
            )
            
            analyze_btn = gr.Button("🔄 Analyze with AI Models", variant="primary", size="lg")
        
        with gr.Column(scale=1):
            gr.Markdown("## ✅ Step 2: AI-Generated Boundary")
            
            analysis_output = gr.Textbox(
                label="AI Analysis", 
                lines=6,
                interactive=False
            )
            
            improved_output = gr.Textbox(
                label="Your AI-Improved Boundary",
                lines=3,
                interactive=False
            )
            
            explanation_output = gr.Textbox(
                label="Why This Works Better",
                lines=4,
                interactive=False
            )
            
            # "Not quite right?" button
            with gr.Row():
                customize_btn = gr.Button("🎯 Not quite right? Create a custom boundary", variant="secondary", size="sm")
    
    gr.Markdown("---")
    
    # Hidden Custom Boundary Builder Section (initially hidden)
    with gr.Column(visible=False) as custom_builder:
        gr.Markdown("## 🎯 Custom Boundary Builder")
        gr.Markdown("*Let's create a personalized boundary that feels right for your specific situation.*")
        
        with gr.Row():
            with gr.Column():
                behavior_input = gr.Textbox(
                    label="What specific behavior do you want them to stop/change?",
                    placeholder="e.g., 'interrupting me during meetings', 'showing up late', 'criticizing my appearance'",
                    lines=2
                )
                
                relationship_input = gr.Dropdown(
                    label="What's your relationship to this person?",
                    choices=[
                        ("Romantic partner", "romantic_partner"),
                        ("Friend", "friend"), 
                        ("Family member", "family_member"),
                        ("Coworker", "coworker"),
                        ("Boss/Manager", "boss"),
                        ("Child", "child")
                    ],
                    value="friend"
                )
                
                feeling_input = gr.Dropdown(
                    label="How does this behavior make you feel?",
                    choices=[
                        "frustrated", "hurt", "disrespected", "unheard", 
                        "uncomfortable", "anxious", "angry", "disappointed",
                        "undervalued", "stressed"
                    ],
                    value="frustrated"
                )
            
            with gr.Column():
                need_input = gr.Textbox(
                    label="What do you need instead?",
                    placeholder="e.g., 'to be heard completely', 'punctuality and respect for my time', 'supportive communication'",
                    lines=2
                )
                
                setting_input = gr.Dropdown(
                    label="Where/how will you communicate this?",
                    choices=[
                        ("Private conversation", "private_conversation"),
                        ("Text message", "text_message"),
                        ("Email", "email"),
                        ("Work environment", "work_environment"),
                        ("Family gathering", "family_gathering"),
                        ("Public space", "public_space")
                    ],
                    value="private_conversation"
                )
                
                tone_input = gr.Dropdown(
                    label="What tone feels right for you?",
                    choices=[
                        ("Gentle", "gentle"),
                        ("Firm", "firm"), 
                        ("Direct", "direct"),
                        ("Diplomatic", "diplomatic")
                    ],
                    value="firm"
                )
        
        build_btn = gr.Button("🔧 Build My Custom Boundary", variant="primary", size="lg")
        
        custom_output = gr.Textbox(
            label="Your Custom Boundary",
            lines=8,
            interactive=False
        )
        
        with gr.Row():
            back_btn = gr.Button("← Back to main tool", variant="secondary", size="sm")
    
    gr.Markdown("---")
    
    # Model status
    gr.Markdown(
        """
        ## 🤖 AI Models Used
        
        This assistant uses **4 essential AI models** for comprehensive analysis:
        
        - **🔍 Fallacy Detector** - Identifies logical fallacies and reasoning errors
        - **🛡️ Your Boundary Predictor** - Assesses boundary communication health  
        - **⚠️ Toxicity Detector** - Identifies harmful/toxic language patterns
        - **🎭 Emotion Recognition** - Recognizes underlying feelings and needs
        
        **Result:** Focused analysis that identifies problems AND understands your emotional needs!
        """
    )
    
    # Connect the functions
    analyze_btn.click(
        fn=analyze_and_improve,
        inputs=[raw_input],
        outputs=[analysis_output, improved_output, explanation_output]
    )
    
    # Custom builder show/hide functionality
    customize_btn.click(
        fn=show_custom_builder,
        outputs=[custom_builder]
    )
    
    back_btn.click(
        fn=hide_custom_builder,
        outputs=[custom_builder]
    )
    
    build_btn.click(
        fn=build_custom_boundary,
        inputs=[behavior_input, relationship_input, feeling_input, need_input, setting_input, tone_input],
        outputs=[custom_output]
    )

# Launch the app
if __name__ == "__main__":
    demo.launch(
        share=True,
        server_name="0.0.0.0",
        server_port=7860
    )