import gradio as gr
import openai
import json
import re
import os
from datetime import datetime, timedelta
import uuid
import random
from typing import Dict

from config import (
    OPENAI_API_KEY, DB_PATH, EMBED_MODEL,
    GEN_MODEL, FAST_MODEL,
    EMOTIONAL_KEYWORDS, ACTION_KEYWORDS, POLICY_KEYWORDS,
    EMAIL_ONLY_KEYWORDS, DETAIL_SYNONYMS, PERSONA_INSTRUCTION
)
from utils import (
    get_embedding, cosine_similarity, find_top_k_matches,
    classify_intent, should_include_email, classify_user_type
)
from database import (
    fetch_all_embeddings, 
    fetch_row_by_id, 
    fetch_all_faq_embeddings, 
    get_session_state, 
    update_session_state, 
    log_question,
    get_recent_history
)
from scraper import scrape_workshops_from_squarespace

# ============================================================================
# CONFIGURATION
# ============================================================================

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY not found in .env file")

openai.api_key = OPENAI_API_KEY


# Removed global session_id for multi-user compatibility

# Cache for workshop data and embeddings
workshop_cache = {
    'data': [],
    'embeddings': [],
    'last_updated': None,
    'cache_duration': timedelta(hours=24)
}

# Load Truth Sheet (Structured Knowledge)
STRUCTURED_KNOWLEDGE = {}
try:
    knowledge_path = os.path.join(os.path.dirname(__file__), "structured_knowledge.json")
    with open(knowledge_path, "r") as f:
        STRUCTURED_KNOWLEDGE = json.load(f)
except Exception as e:
    print(f"Error loading structured_knowledge.json: {e}")

def get_structured_knowledge_snippet(preference=None):
    """Formats structured knowledge into a text snippet for the prompt, filtering by preference if provided."""
    if not STRUCTURED_KNOWLEDGE:
        return ""
    
    snippet = "--- STRUCTURED TRUTH SHEET (VERIFIED KNOWLEDGE) ---\n"
    
    # Links
    free_online = STRUCTURED_KNOWLEDGE.get('free_online_class', {}).get('link', '')
    if not preference or preference.lower() == 'online':
        snippet += f"Free Online Class: {free_online}\n"
    
    kids = STRUCTURED_KNOWLEDGE.get('kids_classes', {})
    if not preference or preference.lower() == 'online':
        snippet += f"Kids Classes (Online): {kids.get('online_link', '')}\n"
    if not preference or preference.lower() == 'instudio':
        snippet += f"Kids Classes (Atlanta): {kids.get('atlanta_link', '')}\n"
    
    summit = STRUCTURED_KNOWLEDGE.get('summit', {})
    snippet += f"Summit: {summit.get('link', '')} - {summit.get('description', '')}\n"
    
    # Instructors
    instructors = STRUCTURED_KNOWLEDGE.get('instructors', [])
    if instructors:
        snippet += "Instructors & Roles (STRICT):\n"
        for inst in instructors:
            not_roles = inst.get('not_roles', [])
            not_str = f" [NOT: {', '.join(not_roles)}]" if not_roles else ""
            snippet += f"- {inst['name']}: {inst['role']}{not_str}\n"
            
    # Paths
    paths = STRUCTURED_KNOWLEDGE.get('paths', {})
    if not preference or preference.lower() == 'online':
        snippet += f"Online Path: {paths.get('online', '')}\n"
    if not preference or preference.lower() == 'instudio':
        snippet += f"Atlanta Path: {paths.get('atlanta', '')}\n"
    
    snippet += "--------------------------------------------------\n"
    return snippet

# ============================================================================
# HELPER FUNCTIONS
# ============================================================================

def calculate_workshop_confidence(w: Dict) -> float:
    """Calculate confidence score of retrieved workshop data"""
    score = 0.0
    if w.get('title'): score += 0.3
    if w.get('instructor_name'): score += 0.3
    if w.get('date'): score += 0.2
    if w.get('time'): score += 0.1
    if w.get('source_url'): score += 0.1
    return round(score, 2)

# ============================================================================
# WORKSHOP FUNCTIONS
# ============================================================================

def get_current_workshops():
    """Get current workshops with caching"""
    global workshop_cache
    
    now = datetime.now()
    
    # Check if cache is still valid
    if (workshop_cache['last_updated'] and 
        now - workshop_cache['last_updated'] < workshop_cache['cache_duration'] and
        workshop_cache['data']):
        print("Using cached workshop data")
        return workshop_cache['data'], workshop_cache['embeddings']
    
    print("Fetching fresh workshop data...")
    
    # Use robust Squarespace scraping system
    online_workshops = scrape_workshops_from_squarespace("https://www.getscenestudios.com/online")
    instudio_workshops = scrape_workshops_from_squarespace("https://www.getscenestudios.com/instudio")
    
    all_workshops = online_workshops + instudio_workshops
    
    # Data Integrity: Validate and score workshops
    valid_workshops = []
    total_score = 0
    for w in all_workshops:
        conf = calculate_workshop_confidence(w)
        if conf >= 0.8:
            valid_workshops.append(w)
            total_score += conf
        else:
            print(f"⚠️ Rejecting weak record (Confidence: {conf}): {w.get('title', 'Unknown')}", flush=True)
    
    avg_conf = total_score / len(valid_workshops) if valid_workshops else 0
    print(f"📊 DATA INTEGRITY: Found {len(all_workshops)} total, {len(valid_workshops)} valid (Confidence >= 0.8)", flush=True)
    print(f"📈 Retrieval Confidence: {avg_conf:.2f} (Average)", flush=True)
    
    all_workshops = valid_workshops
    
    if not all_workshops:
        if workshop_cache['data']:
            print("Scraping failed, using cached data")
            return workshop_cache['data'], workshop_cache['embeddings']
        else:
            print("No workshop data available")
            return [], []
    
    # Generate embeddings for workshops
    workshop_embeddings = []
    for workshop in all_workshops:
        try:
            embedding = get_embedding(workshop['full_text'])
            workshop_embeddings.append(embedding)
        except Exception as e:
            print(f"Error generating embedding for workshop: {e}")
            workshop_embeddings.append([0] * 1536)
    
    # Update cache
    workshop_cache['data'] = all_workshops
    workshop_cache['embeddings'] = workshop_embeddings
    workshop_cache['last_updated'] = now
    
    print(f"Cached {len(all_workshops)} workshops")
    return all_workshops, workshop_embeddings

def find_top_workshops(user_embedding, k=3):
    """Find top matching workshops using real-time data"""
    workshops, workshop_embeddings = get_current_workshops()
    
    if not workshops:
        return []
    
    scored = []
    for i, (workshop, emb) in enumerate(zip(workshops, workshop_embeddings)):
        try:
            score = cosine_similarity(user_embedding, emb)
            scored.append((score, i, workshop['full_text'], workshop))
        except Exception as e:
            print(f"Error calculating similarity: {e}")
            continue
    
    scored.sort(reverse=True)
    return scored[:k]

# ============================================================================
# PROMPT BUILDING FUNCTIONS
# ============================================================================

def generate_enriched_links(row):
    base_url = row.get("youtube_url")
    guest_name = row.get("guest_name", "")
    highlights = json.loads(row.get("highlight_json", "[]"))
    summary = highlights[0]["summary"] if highlights else ""
    
    # Truncate summary to first sentence only
    if summary:
        first_sentence = summary.split('.')[0] + '.'
        
        if len(first_sentence) > 120:
            short_summary = first_sentence[:117] + "..."
        else:
            short_summary = first_sentence
    else:
        short_summary = "Industry insights for actors"
    
    markdown = f"🎧 [Watch {guest_name}'s episode here]({base_url}) - {short_summary}"
    return [markdown]

def build_enhanced_prompt(user_question, context_results, top_workshops, user_preference=None, user_type='unknown', enriched_podcast_links=None, wants_details=False, current_topic=None, mode="Mode B", is_low_confidence=False, is_faq_match=False, is_policy_query=False):
    """Builds the system prompt with strict formatting rules."""
    
    # Dynamic Links from Structured Knowledge
    free_class_url = STRUCTURED_KNOWLEDGE.get('free_online_class', {}).get('link', "https://www.getscenestudios.com/online")
    if user_preference and user_preference.lower() == 'instudio':
        free_class_url = STRUCTURED_KNOWLEDGE.get('paths', {}).get('atlanta', "https://www.getscenestudios.com/instudio")
    
    atlanta_link = STRUCTURED_KNOWLEDGE.get('paths', {}).get('atlanta', "https://www.getscenestudios.com/instudio")
    online_link = STRUCTURED_KNOWLEDGE.get('paths', {}).get('online', "https://www.getscenestudios.com/online")
    
    truth_sheet_snippet = get_structured_knowledge_snippet(preference=user_preference)
    # Placeholder removed to ensure strict usage of retrieved data
    single_podcast = "" 
    
    # helper for clean links
    def format_workshop(w):
        # Strict validation: Title, Instructor, Date must be present and non-empty
        if not w.get('title') or not w.get('instructor_name') or not w.get('date'):
            return None
            
        # Strict formatting: [{Title}]({Link}) with {Instructor} ({Format}) on {Date}
        link = "https://www.getscenestudios.com/instudio" if "/instudio" in w.get('source_url', '') else "https://www.getscenestudios.com/online"
        
        # User Preference Filtering
        w_type = "Online" if "online" in w.get('source_url', '') else "In-Studio"
        if user_preference:
            if user_preference.lower() != w_type.lower():
                return None

        # Calculate confidence
        confidence = calculate_workshop_confidence(w)
        if confidence < 0.70:
            return None

        return f"- [{w['title']}]({link}) with {w['instructor_name']} ({w_type}) on {w['date']} at {w.get('time', '')}"

    # Prepare workshop list (Top 3 max)
    workshop_lines = []
    if top_workshops:
        for _, _, _, w_data in top_workshops[:5]:  # Check top 5, take top 3 valid
            formatted = format_workshop(w_data)
            if formatted:
                workshop_lines.append(formatted)
                

    workshop_text = ""
    if workshop_lines:
        workshop_text = "\n".join(workshop_lines[:3])
    else:
        # Improved fallback to avoid generic/placeholder-like feeling
        label = f"{user_preference.capitalize()} " if user_preference else ""
        link = online_link if user_preference == 'online' else atlanta_link if user_preference == 'instudio' else online_link
        # Mandatory Hyperlink Enforcement
        workshop_text = f"We are constantly updating our schedule! You can view and [register for upcoming {label}workshops here]({link})."
    
    # Pass multiple podcast options to the LLM for variety
    podcast_options = ""
    if not enriched_podcast_links:
        podcast_options = "Our latest industry insights are available on YouTube: https://www.youtube.com/@GetSceneStudios"
    else:
        # Provide up to 3 options
        podcast_options = "\n".join(enriched_podcast_links[:3])
    
    # --- EMOTIONAL / SUPPORT MODE CHECK ---
    is_emotional = detect_response_type(user_question) == "support"
    
    if is_emotional:
        prompt = f"""{PERSONA_INSTRUCTION}

You are acting in SUPPORT MODE. 
        
CRITICAL INSTRUCTIONS:
1. ACKNOWLEDGE their feelings first (e.g., "I hear how frustrating it is to feel stuck...").
2. Provide SUPPORTIVE language (2-3 sentences max).
3. Offer EXACTLY ONE gentle follow-up resource: either the podcast OR the free class. 
4. DO NOT suggest paid workshops or upsell in this response.
5. KEEP IT BRIEF (≤150 words).

USER'S QUESTION: {user_question}

REQUIRED RESPONSE FORMAT:
[Your empathetic, supportive acknowledgment]

Here's a free resource that might help you move forward:
[Pick ONE: {single_podcast} OR Free Class at {free_class_url}]

Questions? Contact info@getscenestudios.com"""
        return prompt
    
    # --- POLICY QUERY MODE ---
    if is_policy_query:
        prompt = f"""{PERSONA_INSTRUCTION}
        
You are acting as a helpful mentor assisting with a policy-related inquiry (refund, cancellation, or billing).

CRITICAL INSTRUCTIONS:
1. Be extremely polite, warm, and professional.
2. If the user is asking for a refund or to cancel, you MUST ask them for a reason if they haven't provided one yet.
3. If they haven't mentioned which class or podcast they are referring to, ask them politely to specify.
4. Let them know it's no problem at all and that we want to make sure they are taken care of.
5. Always provide the contact email: info@getscenestudios.com for them to finalize their request.
6. Do NOT suggest new workshops or podcasts in this response.
7. ACKNOWLEDGE their situation first.

USER'S QUESTION: {user_question}

REQUIRED RESPONSE FORMAT:
[Your polite, mentor-like response asking for missing details or acknowledging the reason]

Questions? Contact info@getscenestudios.com"""
        return prompt

    # --- STANDARD LOGIC FOR CONTEXT SNIPPET ---
    question_lower = user_question.lower()
    context_snippet = ""
    
    # Priority 1: Direct Keywords in current question
    detected_topic = None
    if any(word in question_lower for word in ['agent', 'representation', 'rep', 'manager']):
        detected_topic = 'agent'
    elif any(word in question_lower for word in ['beginner', 'new', 'start', 'beginning']):
        detected_topic = 'beginner'
    elif any(word in question_lower for word in ['callback', 'audition', 'tape', 'self-tape', 'booking']):
        detected_topic = 'audition'
    elif any(word in question_lower for word in ['mentorship', 'coaching']):
        detected_topic = 'mentorship'
    elif any(word in question_lower for word in ['price', 'cost', 'how much']):
        detected_topic = 'pricing'
    elif any(word in question_lower for word in ['class', 'workshop', 'training', 'learn']):
        detected_topic = 'classes'
    elif any(word in question_lower for word in ['membership', 'gsp', 'plus']):
        detected_topic = 'membership'
        
    # Priority 2: Fallback to session context if current question is ambiguous
    if not detected_topic and current_topic:
        topic_map = {
            'agent_seeking': 'agent',
            'beginner': 'beginner',
            'audition_help': 'audition',
            'mentorship': 'mentorship',
            'pricing': 'pricing',
            'classes': 'classes',
            'membership': 'membership'
        }
        detected_topic = topic_map.get(current_topic)

    # Assign snippet based on topic
    if detected_topic == 'agent':
        context_snippet = "Get Scene Studios has helped 1000+ actors land representation. Total Agent Prep offers live practice with working agents (age 16+, limited to 12 actors)."
    elif detected_topic == 'beginner':
        context_snippet = "Get Scene Studios specializes in getting actors audition-ready fast with camera technique and professional self-tape skills."
    elif detected_topic == 'audition':
        context_snippet = "Get Scene offers Crush the Callback (Zoom simulation) and Perfect Submission (self-tape mastery) for actors refining their technique."
    elif detected_topic == 'mentorship':
        context_snippet = "Working Actor Mentorship is a 6-month program ($3,000) with structured feedback and industry access."
    elif detected_topic == 'pricing':
        context_snippet = "Get Scene Studios pricing varies by program. Most workshops cap at 12-14 actors for personalized feedback."
    elif detected_topic == 'classes':
         link = online_link if user_preference == 'online' else atlanta_link
         context_snippet = f"Get Scene Studios offers world-class {user_preference or ''} acting workshops. Our sessions focus on camera technique and industry readiness. Full details at {link}."
    elif detected_topic == 'membership':
         context_snippet = "Get Scene Plus (GSP) is our membership program that provides ongoing access to industry pros and audition insights."
    elif 'summit' in question_lower:
         context_snippet = "The Get Scene Summit is a premier special event featuring massive line-ups of agents, managers, and casting directors. It is NOT a recursive workshop."
    else:
        context_snippet = "Get Scene Studios (founded by Jesse Malinowski) offers training for TV/film actors at all levels."

    preference_instruction = ""
    if not user_preference:
        preference_instruction = """
IMPORTANT: We need to know if the user prefers "Online" or "In-Studio" workshops.
If their question is broad (e.g., "starting acting", "kids classes", "workshops", "training", "classes") and they haven't specified a format, you MUST START your response with this exact question: "Are you looking for online training or in-studio in Atlanta?"
NO PREFIXES, NO "WARM" TRANSITIONS, NO PARAPHRASING. 

FEW-SHOT EXAMPLES:
User: "I want to start acting"
Response: "Are you looking for online training or in-studio in Atlanta? That's a fantastic decision! With Get Scene Studios..."

User: "Do you have kids classes?"
Response: "Are you looking for online training or in-studio in Atlanta? Absolutely, we offer kids classes in both formats..."
"""
    else:
         preference_instruction = f"""
USER PREFERENCE KNOWN: {user_preference.upper()}
1. DO NOT ask "Online or In-Studio" again.
2. Ensure your recommendations align with {user_preference.upper()} where possible.
"""

    BUSINESS_RULES_INSTRUCTION = f"""
TOP-PRIORITY BUSINESS RULES (NO EXCEPTIONS):
1. **NO AUDITING**: Workshops can NEVER be audited. Do not reason about this. Tell the user "We do not allow auditing for our workshops" and immediately redirect them to the Free Online Class.
2. **FREE CLASS FIRST**: The Free Online Class is the MANDATORY first step for ALL new users. If a user is "starting out", "new to acting", or asking "how to begin", you MUST route them to the Free Online Class link below as their primary next step. 
3. **NO IMMEDIATE PAID RECOMMENDATIONS**: For new or unclear users, do NOT recommend specific paid workshops yet. Focus entirely on the Free Online Class as the entry point.
4. **KIDS CLASSES**: We offer kids classes both Online and in Atlanta (In-Studio).
5. **SUMMIT**: The Summit is a special event offering, NOT a regular workshop.
{"6. **STRICT LINK FILTERING**: User prefers " + user_preference.upper() + ". You MUST ONLY provide links for " + user_preference.upper() + " training. OMIT any " + ("In-Studio" if user_preference.lower() == 'online' else "Online") + " links entirely." if user_preference else ""}
7. **ROLE INTEGRITY (STRICT)**: 
   - **THE TRUTH SHEET IS THE ABSOLUTE AND FINAL AUTHORITY.** It overrides ANY information found in podcast descriptions, workshop titles, or suggested by the user.
   - ONLY use the roles explicitly defined in the Truth Sheet.
   - **NEVER infer a role** from the context of a workshop or podcast. 
   - If someone is teaching a class, do NOT assume they are an "Instructor" unless the Truth Sheet says so. 
   - If someone is labeled as an "Agent", do NOT call them an "Instructor" or "Mentor" unless explicitly listed as such in the TRUTH SHEET.
   - Pay attention to the "[NOT: ...]" list for each person in the Truth Sheet. For example, if someone is listed as "[NOT: Instructor]", NEVER call them an instructor, even if they are described as one in a podcast or workshop description.
   - **NEVER** guess or invent a role for anyone.
"""
    # Brevity & Cognitive Load: Direct instructions based on user intent
    detail_instruction = "Answer the user's question briefly (2-3 sentences max, ≤150 words total)."
    if wants_details:
        target = f" regarding {detected_topic or 'the current recommendations'}"
        detail_instruction = f"Provide a detailed and thorough explanation for the user's request{target}. Focus on being helpful and providing deep value as a mentor."

    # Email contact line is conditional
    email_contact = ""
    if should_include_email(user_question):
        email_contact = "\n \nQuestions? Contact info@getscenestudios.com"

    # Context inclusion
    retrieved_info = ""
    if context_results:
        retrieved_info = f"\nRELEVANT INFORMATION FROM KNOWLEDGE BASE:\n{context_results}\n"

    is_beginner = (detected_topic == 'beginner')
    beginner_enforcement = ""
    if is_beginner:
        beginner_enforcement = """
CRITICAL: The user is a BEGINNER. You MUST prioritize the Free Online Class above all else. 
1. Do NOT recommend specific paid workshops in your numbered list. 
2. Instead, provide the Free Online Class as your primary recommendation.
3. Your numbered list should be: 
   1. Free Online Class (The mandatory first step)
   2. The Get Scene Podcast (For industry mindset)
   3. [Choose a very general resource or a 1:1 consultation if available, but NOT a specific workshop]
"""

    user_type_instruction = ""
    if user_type == 'new_actor':
        user_type_instruction = "USER TYPE: NEW ACTOR. Focus heavily on foundation, the Free Online Class, and beginner-friendly mindset. Avoid advanced industry jargon."
    elif user_type == 'experienced_actor':
        user_type_instruction = "USER TYPE: EXPERIENCED ACTOR. Focus on advanced technique, Agent Prep, mentorship, and industry networking. Use professional terminology."
    elif user_type == 'parent':
        user_type_instruction = "USER TYPE: PARENT. Focus on kids/teen programs, safety, youth training paths, and parent-specific concerns."
    elif user_type == 'current_student':
        user_type_instruction = "USER TYPE: EXISTING STUDENT. Focus on GSP membership benefits, advanced mentorships (WAM), and specialized recurring workshops."

    # --- FAQ MATCH MODE (Highest Priority) ---
    if is_faq_match:
        prompt = f"""{PERSONA_INSTRUCTION}

{truth_sheet_snippet}

{BUSINESS_RULES_INSTRUCTION}

{user_type_instruction}

{context_snippet}{retrieved_info}

CRITICAL INSTRUCTIONS (FAQ MODE):
- You are answering a question that has a direct match in our FAQ.
- Answer the user's question directly and punchily using ONLY the provided information.
- **DO NOT** use the structured 1. 2. 3. format.
- **DO NOT** ask a routing question.
- **MANDATORY: Use direct hyperlinks.** For ANY mention of signing up, classes, kids programs, or the free class, you MUST include the direct [Title](Link) format.
- Focus on being a helpful guide. {preference_instruction}

CRITICAL ROLE GUARD (FINAL AUTHORITY):
- Corey Lawson: Instructor/Actor [NOT an Agent]
- Jacob Lawson: Agent/Owner [NOT an Instructor]
- Jesse Malinowski: Founder/Mentor [NOT an Agent]
- Alex White: Agent [NOT an Instructor/Mentor]
- THE TRUTH SHEET IS THE ABSOLUTE AUTHORITY.
 
USER'S QUESTION: {user_question}
 
REQUIRED RESPONSE FORMAT:
[Punchy, helpful answer based on FAQ with relevant links]{email_contact}"""
        return prompt

    if mode == "Mode A":
        # Recommendation Mode: Existing checklist applies
        prompt = f"""{PERSONA_INSTRUCTION}

{truth_sheet_snippet}

{BUSINESS_RULES_INSTRUCTION}

{user_type_instruction}

{beginner_enforcement}

{context_snippet}{retrieved_info}
 
CRITICAL INSTRUCTIONS (RECOMMENDATION MODE):
- {detail_instruction}
- Use natural, human transitions between your answer and the recommendations.
- For each recommendation, add a tiny bit of "mentor advice" on why it helps.
- Use ONLY the provided links - do not invent recommendations.
- **MANDATORY: Use direct hyperlinks.** For ANY mention of signing up, classes, kids programs, the Summit, or the free class, you MUST include the direct [Title](Link) format.
- **CRITICAL: PRESERVE URLS.** You MUST include the full URL in parentheses `(https://...)`. DO NOT output just the bracketed text `[Title]`. If you fail to include the URL, the link will be broken.
- **NEVER say "check our website"** or "visit the link below". Embed the link directly into the relevant part of your mentor advice.
- Focus on clean, readable formatting.{preference_instruction}

CRITICAL ROLE GUARD (FINAL AUTHORITY):
- Corey Lawson: Instructor/Actor [NOT an Agent]
- Jacob Lawson: Agent/Owner [NOT an Instructor]
- Jesse Malinowski: Founder/Mentor [NOT an Agent]
- Alex White: Agent [NOT an Instructor/Mentor]
- THE TRUTH SHEET IS THE ABSOLUTE AUTHORITY. It overrides ALL other info.
- NEVER call Corey Lawson an agent. They are brothers with different roles.
 
USER'S QUESTION: {user_question}
 
REQUIRED RESPONSE FORMAT (STRICT):
[Helpful, mentor-like answer]

Here's your path forward:
1. Free Online Class (Mandatory First Step): {free_class_url}
2. Recommended Podcast Episode (For Industry Mindset):
{podcast_options}
3. Recommended Workshop/Next Step:
{workshop_text}{email_contact}

CRITICAL: YOU MUST USE THE ABOVE "1. 2. 3." STRUCTURE EXACTLY. DO NOT RENAME THE STEPS. DO NOT SKIP THE PODCAST.
"""
    else:
        # Front Desk Mode: More conversational, direct answers, recommendations are optional but encouraged
        prompt = f"""{PERSONA_INSTRUCTION}

{truth_sheet_snippet}

{BUSINESS_RULES_INSTRUCTION}

{context_snippet}{retrieved_info}
 
CRITICAL INSTRUCTIONS (FRONT DESK MODE):
- You are acting as the warm and helpful Front Desk Mentor.
- **MANDATORY: Ask a routing question AT THE BEGINNING** of your response (e.g., "Are you looking to start your journey or refine existing skills?").
- Answer the user's question directly using the provided information but keep it punchy—**no essays**.
- **MANDATORY: Provide direct hyperlinks** for ANY mention of registration, classes, kids programs, the Summit, or more information. Use EXACTLY these links as relevant:
    - Free Online Class: [{free_class_url}]({free_class_url})
    - Recommended for you: {podcast_options}
    - Upcoming Workshops: {workshop_text}
    - Southeast Actor Summit: [Southeast Actor Summit Registration](https://www.getscenestudios.com/southeast-actor-summit)
- **NEVER say "go to the website"** or "check our site". Always provide the specific hyperlink directly in your answer.
- **NEVER guess** or invent information. If it's not in the context, guide the user to clarify.
- **MANDATORY: Guide the user to the next step** at the end of your response (e.g., "A great next step for you would be to sign up for our free class").
- {detail_instruction}
- Focus on being a helpful guide.{preference_instruction}
{"MANDATORY: We don't have a high-confidence match for this specific question. Provide the CLOSEST possible link from our verified knowledge above for their general query." if is_low_confidence else ""}

CRITICAL ROLE GUARD (FINAL AUTHORITY):
- Corey Lawson: Instructor/Actor [NOT an Agent]
- Jacob Lawson: Agent/Owner [NOT an Instructor]
- Jesse Malinowski: Founder/Mentor [NOT an Agent]
- Alex White: Agent [NOT an Instructor/Mentor]
- THE TRUTH SHEET IS THE ABSOLUTE AUTHORITY. It overrides ALL other info.
- NEVER call Corey Lawson an agent. They are brothers with different roles.
 
USER'S QUESTION: {user_question}
 
[Routing Question]
[Helpful, punchy response with links]
**IMPORTANT: You MUST choose the most relevant podcast from the list provided above and include its FULL Markdown link including the URL in your response.**
[Next step guidance]{email_contact}"""
    
    return prompt

# ============================================================================
# DETECTION FUNCTIONS
# ============================================================================

def detect_question_category(question):
    """Categorize user questions for better context injection"""
    question_lower = question.lower()
    
    categories = {
        'agent_seeking': ['agent', 'representation', 'rep', 'manager', 'get an agent'],
        'beginner': ['beginner', 'new', 'start', 'beginning', 'first time', 'never acted'],
        'audition_help': ['audition', 'callback', 'tape', 'self-tape', 'submission'],
        'mentorship': ['mentorship', 'coaching', 'intensive', 'mentor', 'one-on-one'],
        'pricing': ['price', 'cost', 'pricing', '$', 'money', 'payment', 'fee'],
        'classes': ['class', 'workshop', 'training', 'course', 'learn'],
        'membership': ['membership', 'join', 'member', 'gsp', 'plus'],
        'podcast': ['podcast', 'podcasts', 'youtube', 'watch', 'listen', 'episode', 'episodes'],
        'technical': ['self-tape', 'equipment', 'lighting', 'editing', 'camera']
    }
    
    detected = []
    for category, keywords in categories.items():
        if any(keyword in question_lower for keyword in keywords):
            detected.append(category)
    
    return detected

def detect_response_type(question):
    """Detect if question is emotional/support vs action/results oriented"""
    question_lower = question.lower()
    
    emotional_count = sum(1 for word in EMOTIONAL_KEYWORDS if word in question_lower)
    action_count = sum(1 for word in ACTION_KEYWORDS if word in question_lower)
    
    if emotional_count > 0 and emotional_count >= action_count:
        return "support"
    return "standard"

def detect_policy_issue(question):
    """Detect if question violates hard policy rules (refunds, attendance, etc.) using word boundaries"""
    question_lower = question.lower()
    for word in POLICY_KEYWORDS:
        # Use regex word boundaries to prevent substring matches (e.g., 'submission' matching 'miss')
        pattern = rf'\b{re.escape(word)}\b'
        if re.search(pattern, question_lower):
            return True
    return False

def detect_preference(question):
    """Detect if user is stating a preference"""
    q_lower = question.lower()
    if 'online' in q_lower and 'studio' not in q_lower:
        return 'online'
    if ('studio' in q_lower or 'person' in q_lower or 'atlanta' in q_lower) and 'online' not in q_lower:
        return 'instudio'
    return None

def get_contextual_business_info(categories):
    """Return relevant business information based on detected question categories"""
    
    context_map = {
        'agent_seeking': {
            'programs': ['Total Agent Prep', 'Working Actor Mentorship'],
            'key_info': 'Live pitch practice with real agents, Actors Access optimization',
            'journey': 'Total Agent Prep → GSP → Mentorship for sustained progress'
        },
        'beginner': {
            'programs': ['Free Classes', 'Get Scene 360', 'Get Scene Plus'],
            'key_info': 'Start with holistic foundation, build consistency',
            'journey': 'Free class → Get Scene 360 → GSP membership'
        },
        'audition_help': {
            'programs': ['Perfect Submission', 'Crush the Callback', 'Audition Insight'],
            'key_info': 'Self-tape mastery, callback simulation, pro feedback',
            'journey': 'Perfect Submission → GSP for ongoing Audition Insight'
        },
        'mentorship': {
            'programs': ['Working Actor Mentorship'],
            'key_info': '6-month intensive with structured feedback and accountability',
            'journey': 'Ready for commitment → WAM → Advanced workshops'
        }
    }
    
    relevant_info = {}
    for category in categories:
        if category in context_map:
            relevant_info[category] = context_map[category]
    
    return relevant_info

# ============================================================================
# MAIN CHATBOT LOGIC
# ============================================================================

def update_knowledge_from_question(session_id: str, question: str):
    """Extract attributes and update knowledge dictionary"""
    updates = {}
    
    # Extract Format
    pref = detect_preference(question)
    if pref:
        updates['format'] = pref
        
    # Extract Topic
    cats = detect_question_category(question)
    if cats:
        # Prioritize specific topics over generic ones
        priority_topics = ['agent_seeking', 'beginner', 'audition_help', 'mentorship', 'pricing']
        for topic in priority_topics:
            if topic in cats:
                updates['topic'] = topic
                break
        if 'topic' not in updates and cats:
             updates['topic'] = cats[0]

    if updates:
        update_session_state(session_id, knowledge_update=updates, increment_count=False)
        return updates
    return {}

def process_question(question: str, current_session_id: str):
    """Main function to process user questions - replaces Flask /ask endpoint"""
    
    try:
        if not question:
            return "Question is required"

        # 0. INTENT CLASSIFICATION
        activated_mode = "Mode B"  # Default safe value

        # 1. HARD POLICY CHECK (Internal critical issues only)
        is_policy_query = False
        if detect_policy_issue(question) and should_include_email(question):
            q_lower = question.lower()
            
            # Specific handling for late/miss issues
            is_late_miss = any(re.search(rf'\b{re.escape(word)}\b', q_lower) for word in ['late', 'miss', 'missed', 'attendance', 'attend'])
            
            if is_late_miss:
                ans = "Don't worry! You can email info@getscenestudios.com and our team will help you with any attendance or scheduling issues."
                log_question(
                    question=question,
                    session_id=current_session_id,
                    category="policy_late_miss",
                    answer=ans,
                    detected_mode="Mode B",
                    routing_question=None,
                    rule_triggered="late_miss_polite",
                    link_provided=False
                )
                return ans

            # For refund/cancel, we set a flag and continue to LLM for conversational response
            is_policy_query = True

        # 2. Handle Session & Knowledge State
        update_knowledge_from_question(current_session_id, question)
        
        session_state = get_session_state(current_session_id)
        
        try:
            knowledge = json.loads(session_state.get('knowledge_context', '{}'))
        except:
            knowledge = {}

        user_type = knowledge.get('user_type', 'unknown')

        # Update User Type if unknown or enough turn count
        if user_type == 'unknown' or session_state.get('msg_count', 0) % 3 == 0:
            new_user_type = classify_user_type(question)
            if new_user_type != 'unknown':
                user_type = new_user_type
                knowledge['user_type'] = user_type
                update_session_state(current_session_id, knowledge_update=knowledge, increment_count=False)

        user_preference = knowledge.get('format')
        current_topic = knowledge.get('topic')
        
        if not user_preference:
            user_preference = session_state.get('preference')
        
        update_session_state(current_session_id, increment_count=True)

        # 3. ROUTING: Use classification LLM to decide Mode A or Mode B
        activated_mode = classify_intent(question)
        last_mode = knowledge.get('last_mode')
        
        if session_state.get('clarification_count', 0) > 0 and last_mode:
            if len(question.split()) < 5 or any(k in question.lower() for k in ['yes', 'no', 'sure', 'not sure', 'dont know']):
                activated_mode = last_mode
        
        # Store mode for next turn's potentially sticky logic
        knowledge['last_mode'] = activated_mode
        print(f"DEBUG: [{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Activated Mode for session {current_session_id}: {activated_mode}")
        update_session_state(current_session_id, knowledge_update=knowledge, increment_count=False)

        # 4. SEMANTIC SEARCH: Create embedding of user question
        user_embedding = get_embedding(question)

        # Check FAQ embeddings first
        faq_data = fetch_all_faq_embeddings()
        top_faqs = []

        for entry_id, question_text, answer_text, emb in faq_data:
            score = cosine_similarity(user_embedding, emb)
            top_faqs.append((score, entry_id, question_text, answer_text))
            top_faqs.sort(reverse=True)

        faq_threshold = 0.50     
        ambiguous_threshold = 0.60

        is_low_confidence = False  # Default safe initialization
        context_results = None
        is_faq_match = False

        if top_faqs and top_faqs[0][0] >= faq_threshold:
            best_score, faq_id, question_text, answer_text = top_faqs[0]
            print(f"DEBUG: Processing FAQ match through LLM and Truth Sheet rules...")
            context_results = answer_text
            is_faq_match = True

        elif activated_mode == "Mode A":
            # Mode A: Any score < 0.85 triggers Clarification -> Email
            # EXCEPTION: If they specifically ask for podcasts or recommendations, let it through to LLM path
            is_recommendation_query = any(k in question.lower() for k in ['podcast', 'reccomend', 'recommend', 'path', 'help', 'advice', 'guide'])
            
            clarification_count = session_state.get('clarification_count', 0)
            if clarification_count == 0 and not is_recommendation_query:
                 update_session_state(current_session_id, increment_clarification=True, increment_count=False)
                 return "I want to make sure I give you the best advice. Are you looking for classes in [Atlanta](https://www.getscenestudios.com/instudio), [Online](https://www.getscenestudios.com/online), or something else like getting an agent? You can also start right now with our [Free Online Class](https://www.getscenestudios.com/online)!"
            elif clarification_count > 0 and not is_recommendation_query:
                 update_session_state(current_session_id, reset_clarification=True)
                 return "I'm still not quite sure, and I want to make sure you get the right answer! Please email our team at info@getscenestudios.com and we'll help you directly. In the meantime, you can explore or [register for our Online Path](https://www.getscenestudios.com/online) or [In-Studio classes in Atlanta](https://www.getscenestudios.com/instudio)."
            # Else: is_recommendation_query is True, fall through to LLM path

        elif top_faqs and top_faqs[0][0] >= ambiguous_threshold:
            # Mode B: Ambiguous Score -> Use best FAQ match as context for LLM
            # Instead of asking "Did you mean?", answer naturally using the FAQ content
            best_score, faq_id, question_text, answer_text = top_faqs[0]
            print(f"DEBUG: Ambiguous FAQ match (score={best_score:.2f}), using as LLM context: {question_text[:60]}...")
            context_results = answer_text
            is_faq_match = True

        else:
            # 5. HALLUCINATION GUARD: Check if query is acting-related before blocking
            categories = detect_question_category(question)
            
            has_session_context = (current_topic is not None) or (user_preference is not None)
            
            FOLLOWUP_KEYWORDS = ['yes', 'no', 'sure', 'okay', 'thanks', 'thank you', 'please', 'go ahead', 'continue', 'more']
            ACTING_KEYWORDS = ['class', 'workshop', 'coaching', 'studio', 'acting', 'online', 'person', 'atlanta', 'training', 'prefer', 'preference', 'format', 'recommendation', 'online class', 'online workshop','instudio class','instudio workshop', 'actor', 'scene', 'audition', 'theatre', 'film', 'tv', 'commercial', 'agent', 'rep', 'manager', 'instructor', 'role', 'auditing', 'audit', 'representation', 'summit', 'sign up', 'sign-up', 'register', 'enroll', 'schedule', 'cancel', 'reschedule', 'how do i', 'podcast', 'podcasts', 'youtube', 'episode', 'episodes', 'watch', 'refund']
            
            is_acting_related = (
                is_policy_query or
                len(categories) > 0 or 
                detect_response_type(question) == "support" or 
                any(re.search(rf'\b{re.escape(k)}\b', question.lower()) for k in ACTION_KEYWORDS) or
                any(re.search(rf'\b{re.escape(k)}\b', question.lower()) for k in DETAIL_SYNONYMS) or
                any(re.search(rf'\b{re.escape(k)}\b', question.lower()) for k in ACTING_KEYWORDS) or
                (has_session_context and any(re.search(rf'\b{re.escape(k)}\b', question.lower().strip('.!')) for k in FOLLOWUP_KEYWORDS)) or
                (session_state.get('clarification_count', 0) > 0 and len(question.split()) < 5)  # Only allow short answers to bypass
            )
            
            if not is_acting_related:
                return "I'm not exactly sure about that. Could you clarify your question?"

        # Flag for Mode B Low Confidence
        is_low_confidence = (activated_mode == "Mode B" and not context_results)
        
        # 6. LLM PATH: No high-confidence FAQ match, or Mode B FAQ formatting
        update_session_state(current_session_id, reset_clarification=True, increment_count=False)
        
        # RAG: Fetch relevant workshops and podcasts
        podcast_data = fetch_all_embeddings("podcast_episodes")
        top_workshops = find_top_workshops(user_embedding, k=3)
        top_podcasts = find_top_k_matches(user_embedding, podcast_data, k=3)

        # Get chat history for rotation logic
        chat_history = get_recent_history(current_session_id, limit=5)
        history_text = " ".join([m['content'] for m in chat_history]).lower()

        enriched_podcast_links = []
        for _, podcast_id, _ in top_podcasts:
            row = fetch_row_by_id("podcast_episodes", podcast_id)
            links = generate_enriched_links(row)
            enriched_podcast_links.extend(links)

        if not enriched_podcast_links:
            fallback = fetch_row_by_id("podcast_episodes", podcast_data[0][0])
            enriched_podcast_links = generate_enriched_links(fallback)
        
        # Diversity Logic: Shuffle and prioritize unseen podcasts
        random.shuffle(enriched_podcast_links)
        seen_links = []
        unseen_links = []
        
        for link in enriched_podcast_links:
            # Extract guest name or unique part to check history
            # e.g. "🎧 [Watch Haillie Ricardo's episode here]..."
            match = re.search(r'Watch (.*)\'s episode', link)
            if match:
                guest_name = match.group(1).lower()
                if guest_name in history_text:
                    seen_links.append(link)
                else:
                    unseen_links.append(link)
            else:
                unseen_links.append(link)
        
        # Combine: Unseen first, then seen
        final_podcast_options = unseen_links + seen_links

        # Brevity & Detail Detection
        wants_details = any(syn in question.lower() for syn in DETAIL_SYNONYMS)
        
        # Use enhanced prompt building
        final_prompt = build_enhanced_prompt(
            question, 
            context_results, 
            top_workshops,
            user_preference=user_preference,
            user_type=user_type,
            enriched_podcast_links=final_podcast_options,
            wants_details=wants_details,
            current_topic=current_topic,
            mode=activated_mode,
            is_low_confidence=is_low_confidence,
            is_faq_match=is_faq_match,
            is_policy_query=is_policy_query
        )

        # LLM messages
        messages = [{"role": "system", "content": final_prompt}]
        messages.extend(chat_history)
        messages.append({"role": "user", "content": question})
        
        response = openai.chat.completions.create(
            model=GEN_MODEL,
            messages=messages
        )
        
        answer_text = response.choices[0].message.content.strip()

        # 7. ROUTING QUESTION ENFORCEMENT (Python-level Fallback)
        routing_q = "Are you looking for online training or in-studio in Atlanta?"
        broad_triggers = ['start acting', 'beginner', 'new actor', 'kids class', 'workshops', 'training', 'classes']
        is_broad = any(t in question.lower() for t in broad_triggers)
        
        if is_broad and not user_preference:
            if not answer_text.lower().startswith(routing_q.lower()):
                if routing_q.lower() in answer_text.lower():
                    answer_text = re.sub(rf'{re.escape(routing_q)}[?!.]*', '', answer_text, flags=re.IGNORECASE).strip()
                
                answer_text = f"{routing_q} {answer_text}"

        # Detect if routing question was asked
        routing_q_asked = routing_q if (is_broad and not user_preference and routing_q in answer_text) else None
        
        # Detect if links were provided
        has_links = bool(re.search(r'\[.*?\]\(http', answer_text))
        
        # Log question with comprehensive metadata
        log_question(
            question=question,
            session_id=current_session_id,
            category="llm_generated",
            answer=answer_text,
            detected_mode=activated_mode,
            routing_question=routing_q_asked,
            rule_triggered=None,
            link_provided=has_links
        )

        return answer_text

    except Exception as e:
        import traceback
        print(f"❌ CRITICAL ERROR in process_question: {e}")
        traceback.print_exc()
        return f"I apologize, but I encountered an error processing your question. Please try again or email info@getscenestudios.com for assistance."

# ============================================================================
# GRADIO INTERFACE
# ============================================================================

def chat_with_bot(message, history, session_id):
    """
    Process message directly without Flask API
    
    Args:
        message: User's current message
        history: Chat history
        session_id: Per-user session ID state
    
    Returns:
        Updated history and session_id
    """
    if not session_id:
        session_id = str(uuid.uuid4())
        
    if not message.strip():
        return history, session_id
    
    try:
        # Process question directly
        bot_reply = process_question(message, session_id)
    except Exception as e:
        bot_reply = f"❌ Error: {str(e)}"
    
    # Append to history
    history.append({"role": "user", "content": message})
    history.append({"role": "assistant", "content": bot_reply})
    return history, session_id

def reset_session():
    """Reset session ID for new conversation"""
    new_id = str(uuid.uuid4())
    return [], new_id

# Create Gradio interface
with gr.Blocks(title="Get Scene Studios Chatbot") as demo:
    
    gr.Markdown(
        """
        # 🎬 Get Scene Studios AI Chatbot
        
        Ask questions about acting classes, workshops and more!
        """
    )
    
    # Chatbot interface
    chatbot = gr.Chatbot(
        label="Conversation",
        height=500
    )
    
    # Input area
    with gr.Row():
        msg = gr.Textbox(
            label="Your Message",
            lines=2,
            scale=4
        )
        submit_btn = gr.Button("Send 📤", scale=1, variant="primary")
    
    # Action buttons
    with gr.Row():
        clear_btn = gr.Button("Clear Chat 🗑️", scale=1)
        reset_btn = gr.Button("New Session 🔄", scale=1)
    
    # Session state
    session_state = gr.State("")
    
    # Event handlers
    submit_btn.click(
        fn=chat_with_bot,
        inputs=[msg, chatbot, session_state],
        outputs=[chatbot, session_state]
    ).then(
        fn=lambda: "",
        inputs=None,
        outputs=[msg]
    )
    
    msg.submit(
        fn=chat_with_bot,
        inputs=[msg, chatbot, session_state],
        outputs=[chatbot, session_state]
    ).then(
        fn=lambda: "",
        inputs=None,
        outputs=[msg]
    )
    
    clear_btn.click(
        fn=lambda: [],
        inputs=None,
        outputs=[chatbot]
    )
    
    reset_btn.click(
        fn=reset_session,
        inputs=None,
        outputs=[chatbot, session_state]
    )

# Launch the app
if __name__ == "__main__":
    print("\n" + "="*60)
    print("🎬 Get Scene Studios Chatbot")
    print("="*60)
    print("\n✅ No Flask API needed - all processing is done directly!")
    print("🌐 Gradio interface will open in your browser")
    print("="*60 + "\n")
    
    demo.launch()