# Voice Bot Service – TTS handled by browser speechSynthesis

import requests
import re
from datetime import datetime, timedelta
from django.apps import apps
from django.http import JsonResponse, HttpResponse
from django.views.decorators.csrf import csrf_exempt
from django.views.decorators.http import require_http_methods
from django.utils import timezone
import json
from voice_config import *
from gemini_helper import gemini_ai


def _get_voice_models():
    """Lazy import of VoiceSession / VoiceMessage to avoid AppRegistryNotReady."""
    VoiceSession = apps.get_model('store', 'VoiceSession')
    VoiceMessage = apps.get_model('store', 'VoiceMessage')
    return VoiceSession, VoiceMessage


class GroqTTS:
    """Text-to-Speech using Groq Orpheus TTS API"""

    MAX_INPUT_LEN = 200  # Groq Orpheus hard limit per request

    def __init__(self):
        self.api_key = GROQ_API_KEY
        self.model = GROQ_TTS_MODEL
        self.voice = GROQ_TTS_VOICE
        self.base_url = "https://api.groq.com/openai/v1"

    # ── internal: single chunk (≤200 chars) ────────────────────────
    def _tts_chunk(self, text):
        """Send a single ≤200-char chunk to Groq and return WAV bytes."""
        url = f"{self.base_url}/audio/speech"

        headers = {
            "Authorization": f"Bearer {self.api_key.strip()}",
            "Content-Type": "application/json",
        }

        data = {
            "model": self.model,
            "input": text,
            "voice": self.voice,
            "response_format": "wav",
        }

        response = requests.post(url, json=data, headers=headers)
        response.raise_for_status()
        return response.content

    # ── split long text into ≤200-char pieces at sentence boundaries ──
    @staticmethod
    def _split_text(text, limit=200):
        """Break *text* into chunks of at most *limit* characters,
        splitting on sentence-ending punctuation when possible."""
        chunks = []
        while text:
            if len(text) <= limit:
                chunks.append(text)
                break
            # Try to split at the last sentence-end within the limit
            segment = text[:limit]
            split_pos = -1
            for sep in ['. ', '! ', '? ', '.\'', '!\"', '?\"']:
                idx = segment.rfind(sep)
                if idx > split_pos:
                    split_pos = idx + len(sep)
            if split_pos <= 0:
                # Fall back to last space
                split_pos = segment.rfind(' ')
            if split_pos <= 0:
                split_pos = limit  # hard cut
            chunks.append(text[:split_pos].strip())
            text = text[split_pos:].strip()
        return [c for c in chunks if c]

    # ── public API (handles any length) ────────────────────────────
    def text_to_speech(self, text):
        """
        Convert text to speech using Groq Orpheus TTS.
        Automatically chunks text >200 chars and concatenates WAV output.
        Returns audio data in WAV format.
        """
        import struct, io
        chunks = self._split_text(text, self.MAX_INPUT_LEN)
        if not chunks:
            return None

        try:
            if len(chunks) == 1:
                return self._tts_chunk(chunks[0])

            # Multiple chunks: concatenate raw PCM from each WAV
            pcm_parts = []
            wav_params = None  # (channels, sample_width, sample_rate)
            for chunk in chunks:
                wav_bytes = self._tts_chunk(chunk)
                buf = io.BytesIO(wav_bytes)
                # Parse minimal WAV header (44 bytes canonical)
                buf.read(4)   # RIFF
                buf.read(4)   # file size
                buf.read(4)   # WAVE
                buf.read(4)   # fmt 
                fmt_size = struct.unpack('<I', buf.read(4))[0]
                fmt_data = buf.read(fmt_size)
                channels = struct.unpack('<H', fmt_data[2:4])[0]
                sample_rate = struct.unpack('<I', fmt_data[4:8])[0]
                bits = struct.unpack('<H', fmt_data[14:16])[0]
                if wav_params is None:
                    wav_params = (channels, bits // 8, sample_rate)
                # Skip to data chunk
                while True:
                    chunk_id = buf.read(4)
                    if not chunk_id:
                        break
                    chunk_size = struct.unpack('<I', buf.read(4))[0]
                    if chunk_id == b'data':
                        pcm_parts.append(buf.read(chunk_size))
                        break
                    else:
                        buf.read(chunk_size)

            # Rebuild single WAV
            pcm = b''.join(pcm_parts)
            ch, sw, sr = wav_params
            data_size = len(pcm)
            header = struct.pack(
                '<4sI4s4sIHHIIHH4sI',
                b'RIFF', 36 + data_size, b'WAVE',
                b'fmt ', 16, 1, ch, sr, sr * ch * sw, ch * sw, sw * 8,
                b'data', data_size,
            )
            return header + pcm

        except requests.exceptions.RequestException as e:
            print(f"Groq TTS API Error: {e}")
            return None
        except Exception as e:
            print(f"Groq TTS processing error: {e}")
            return None


# Backward-compatible alias so existing references keep working
ElevenLabsTTS = GroqTTS


class ConversationFlow:
    """Manages the conversation flow for returns and exchanges"""
    
    def __init__(self):
        self.tts = ElevenLabsTTS()
        self.state = {}
    
    def process_user_input(self, session_id, user_text, current_step):
        """Process user input and generate appropriate response"""
        
        # Initialize session state
        if session_id not in self.state:
            self.state[session_id] = {
                'step': 'greeting',
                'data': {}
            }
        
        session = self.state[session_id]
        
        # Process based on current step
        if current_step == 'greeting':
            # Only short-circuit when the user clearly says exchange/return.
            # Without a keyword match we ask explicitly — this prevents
            # inputs like "I would be 23" from being misclassified.
            user_lower = user_text.strip().lower()
            intent_keywords = [
                'return', 'refund', 'money back', 'send back',
                'exchange', 'swap', 'replace', 'replacement', 'different product',
            ]
            has_intent = any(kw in user_lower for kw in intent_keywords)

            if has_intent:
                question_asked = CONVERSATION_TEMPLATES['ask_exchange_or_return']
                request_type = self._extract_request_type(user_text, question_asked)
                print(f"[flow] greeting -> keyword found -> classified: {request_type}")
                session['data']['request_type'] = request_type
                response_text = CONVERSATION_TEMPLATES['ask_order_id']
                next_step = 'ask_order_id'
            else:
                print(f"[flow] greeting -> no intent keyword in '{user_text}', asking explicitly")
                response_text = CONVERSATION_TEMPLATES['ask_exchange_or_return']
                next_step = 'ask_exchange_or_return'
        
        elif current_step == 'ask_exchange_or_return':
            question_asked = CONVERSATION_TEMPLATES['ask_exchange_or_return']
            print(f"\n{'='*70}")
            print(f"🎤 PROCESSING STEP: ask_exchange_or_return")
            print(f"📝 User input: '{user_text}'")
            print(f"❓ Question asked: '{question_asked}'")
            print(f"{'='*70}")
            
            request_type = self._extract_request_type(user_text, question_asked)
            
            print(f"\n{'='*70}")
            print(f"🔍 EXTRACTION RESULT: '{request_type}'")
            print(f"🔍 Result type: {type(request_type)}")
            print(f"🔍 Result is truthy: {bool(request_type)}")
            print(f"{'='*70}\n")
            
            # request_type should NEVER be None now, but just in case
            if request_type and request_type in ['exchange', 'return']:
                session['data']['request_type'] = request_type
                print(f"✅ SUCCESS: Request type set to: {request_type}")
                response_text = CONVERSATION_TEMPLATES['ask_order_id']
                next_step = 'ask_order_id'
            else:
                print(f"❌ UNEXPECTED: Got invalid request_type: {request_type}")
                print(f"⚠️ This should not happen - defaulting to exchange")
                session['data']['request_type'] = 'exchange'
                response_text = CONVERSATION_TEMPLATES['ask_order_id']
                next_step = 'ask_order_id'
        
        elif current_step == 'ask_order_id':
            question_asked = CONVERSATION_TEMPLATES['ask_order_id']
            print(f"\n🎤 PROCESSING STEP: ask_order_id")
            print(f"📝 User input: '{user_text}'")
            
            order_id = self._extract_order_id(user_text, question_asked)
            
            print(f"🔍 Extraction result: {order_id}")
            
            if order_id:
                session['data']['order_id'] = order_id
                print(f"✅ Order ID set to: {order_id}")
                request_type = session['data']['request_type']
                response_text = CONVERSATION_TEMPLATES['ask_reason'].format(request_type=request_type)
                next_step = 'ask_reason'
            else:
                print(f"⚠️ Could not extract order ID from: '{user_text}'")
                response_text = "I couldn't find an order number. Please say something like 'order 123' or 'my order number is 456'."
                next_step = 'ask_order_id'
        
        elif current_step == 'ask_reason':
            # Clean up and standardize the reason using Gemini
            request_type = session['data']['request_type']
            question_asked = CONVERSATION_TEMPLATES['ask_reason'].format(request_type=request_type)
            cleaned_reason = gemini_ai.extract_reason(user_text, request_type, question_asked)
            session['data']['reason'] = cleaned_reason
            print(f"🤖 Gemini cleaned reason: '{user_text}' -> '{cleaned_reason}'")
            
            if request_type == 'exchange':
                response_text = CONVERSATION_TEMPLATES['ask_exchange_preference']
                next_step = 'ask_exchange_preference'
            else:
                response_text = self._generate_confirmation(session['data'])
                next_step = 'confirm_details'
        
        elif current_step == 'ask_exchange_preference':
            question_asked = CONVERSATION_TEMPLATES['ask_exchange_preference']
            session['data']['exchange_preference'] = gemini_ai.extract_exchange_preference(user_text, question_asked)
            print(f"🤖 Gemini cleaned preference: '{user_text}' -> '{session['data']['exchange_preference']}'")
            response_text = self._generate_confirmation(session['data'])
            next_step = 'confirm_details'
        
        elif current_step == 'confirm_details':
            confirmation_question = self._generate_confirmation(session['data'])
            if self._is_confirmation(user_text, confirmation_question):
                result = self._process_request(session['data'])
                response_text = result['message']
                next_step = 'completed'
                
                # Save to database
                if DATABASE_INTEGRATION:
                    self._save_to_database(session['data'], result)
            else:
                # BUG FIX 2: User said no - ask what was wrong
                response_text = CONVERSATION_TEMPLATES['ask_what_wrong']
                next_step = 'identify_correction'
        
        elif current_step == 'identify_correction':
            # User specified what they want to correct
            field_to_correct = gemini_ai.identify_correction_field(user_text, session['data'])
            print(f"🔧 User wants to correct: {field_to_correct}")
            
            # Redirect to the appropriate question to get NEW answer
            if field_to_correct == 'request_type':
                response_text = CONVERSATION_TEMPLATES['ask_exchange_or_return']
                next_step = 'ask_exchange_or_return'
            elif field_to_correct == 'order_id':
                response_text = CONVERSATION_TEMPLATES['ask_order_id']
                next_step = 'ask_order_id'
            elif field_to_correct == 'reason':
                request_type = session['data']['request_type']
                response_text = CONVERSATION_TEMPLATES['ask_reason'].format(request_type=request_type)
                next_step = 'ask_reason'
            elif field_to_correct == 'exchange_preference':
                response_text = CONVERSATION_TEMPLATES['ask_exchange_preference']
                next_step = 'ask_exchange_preference'
            else:  # everything or unrecognized
                # Start over
                session['data'] = {}
                response_text = CONVERSATION_TEMPLATES['ask_exchange_or_return']
                next_step = 'ask_exchange_or_return'
        
        else:
            response_text = CONVERSATION_TEMPLATES['greeting']
            next_step = 'greeting'
        
        # Update session
        session['step'] = next_step
        
        return {
            'text': response_text,
            'next_step': next_step,
            'session_data': session['data'],
            'valid_answers': self._get_valid_answers(next_step),
        }
    
    def _extract_request_type(self, text, question_asked):
        """Extract whether user wants exchange or return using Gemini AI with question context"""
        result = gemini_ai.extract_request_type(text, question_asked)
        if result:
            print(f"🤖 Gemini understood '{text}' as: {result}")
        return result
    
    def _extract_order_id(self, text, question_asked):
        """Extract order ID from user input using Gemini AI with question context"""
        result = gemini_ai.extract_order_id(text, question_asked)
        if result:
            print(f"🤖 Gemini extracted order ID from '{text}': {result}")
        return result
    
    def _is_confirmation(self, text, question_asked):
        """Check if user is confirming using Gemini AI with question context"""
        result = gemini_ai.is_confirmation(text, question_asked)
        print(f"🤖 Gemini understood '{text}' as confirmation: {result}")
        return result

    def _get_valid_answers(self, step):
        """Return the set of valid / example answers for a given step.
        For ask_order_id we pull real order IDs from the database."""
        if step == 'greeting' or step == 'ask_exchange_or_return':
            return ['exchange', 'return', 'I want to exchange', 'I want to return']
        elif step == 'ask_order_id':
            try:
                Order = apps.get_model('store', 'Order')
                order_ids = list(
                    Order.objects.order_by('-id').values_list('id', flat=True)[:20]
                )
                return [str(oid) for oid in order_ids] if order_ids else ['No orders found']
            except Exception as e:
                print(f'[valid_answers] DB error: {e}')
                return ['order 123', '12345']
        elif step == 'ask_reason':
            return ['wrong size', 'defective product', "I don't like it", 'color mismatch', 'poor quality']
        elif step == 'ask_exchange_preference':
            return ['size large', 'different color - blue', 'size medium', 'black color']
        elif step == 'confirm_details':
            return ['yes', "yes that's correct", 'no', 'change the order number', 'wrong reason']
        return []
    def _generate_confirmation(self, data):
        """Generate confirmation message"""
        request_type = data['request_type']
        order_id = data['order_id']
        reason = data['reason']
        
        extra_info = ""
        if request_type == 'exchange':
            extra_info = f"You want {data.get('exchange_preference', 'a different product')} instead. "
        
        return CONVERSATION_TEMPLATES['confirm_details'].format(
            request_type=request_type,
            order_id=order_id,
            reason=reason,
            extra_info=extra_info
        )
    
    def _process_request(self, data):
        """Process the return/exchange request"""
        request_type = data['request_type']
        
        if request_type == 'exchange':
            return self._process_exchange(data)
        else:
            return self._process_return(data)
    
    def _process_exchange(self, data):
        """Process exchange request"""
        tracking_number = f"EXG-{data['order_id']}-{datetime.now().strftime('%Y%m%d%H%M%S')}"
        pickup_date = (datetime.now() + timedelta(days=2)).strftime('%B %d, %Y')
        delivery_date = (datetime.now() + timedelta(days=EXCHANGE_DELIVERY_DAYS)).strftime('%B %d, %Y')
        
        message = CONVERSATION_TEMPLATES['processing'].format(request_type='exchange')
        message += " " + CONVERSATION_TEMPLATES['exchange_pickup_scheduled'].format(pickup_date=pickup_date)
        message += " " + CONVERSATION_TEMPLATES['exchange_delivery_scheduled'].format(delivery_date=delivery_date)
        message += " " + CONVERSATION_TEMPLATES['success'].format(
            request_type='exchange',
            tracking_number=tracking_number
        )
        
        return {
            'message': message,
            'tracking_number': tracking_number,
            'pickup_date': pickup_date,
            'delivery_date': delivery_date
        }
    
    def _process_return(self, data):
        """Process return request"""
        tracking_number = f"RET-{data['order_id']}-{datetime.now().strftime('%Y%m%d%H%M%S')}"
        pickup_date = (datetime.now() + timedelta(days=2)).strftime('%B %d, %Y')
        
        message = CONVERSATION_TEMPLATES['processing'].format(request_type='return')
        message += " " + CONVERSATION_TEMPLATES['return_scheduled'].format(pickup_date=pickup_date)
        message += " " + CONVERSATION_TEMPLATES['success'].format(
            request_type='return',
            tracking_number=tracking_number
        )
        
        return {
            'message': message,
            'tracking_number': tracking_number,
            'pickup_date': pickup_date
        }
    
    def _save_to_database(self, data, result):
        """Save exchange/return data to database"""
        try:
            # Import your models dynamically
            Order = apps.get_model('shop', 'Order')
            OrderDetail = apps.get_model('shop', 'OrderDetail')
            
            order_id = data.get('order_id')
            
            # Find the order
            try:
                order = Order.objects.get(id=order_id)
                order_details = OrderDetail.objects.filter(order=order)
                
                # Update all order details
                for detail in order_details:
                    detail.return_reason = data.get('reason', '')
                    detail.return_status = 'Returned'
                    detail.return_date = datetime.now()
                    detail.days_to_return = DEFAULT_RETURN_DAYS
                    
                    if data['request_type'] == 'exchange':
                        detail.is_exchanged = True
                        detail.exchange_order = data.get('exchange_preference', '')
                    
                    detail.save()
                
                print(f"✅ Database updated for Order {order_id}")
                print(f"   Reason: {data.get('reason')}")
                print(f"   Type: {data['request_type']}")
                print(f"   Tracking: {result.get('tracking_number')}")
                
            except Order.DoesNotExist:
                print(f"❌ Order {order_id} not found in database")
                
        except Exception as e:
            print(f"❌ Database error: {e}")


# Global conversation flow instance
conversation_flow = ConversationFlow()


# Django Views
@csrf_exempt
@require_http_methods(["POST"])
def start_conversation(request):
    """Start a new conversation session"""
    import uuid
    session_id = str(uuid.uuid4())
    
    # Generate greeting audio
    tts = ElevenLabsTTS()
    greeting_text = CONVERSATION_TEMPLATES['greeting']

    # Create DB session record
    try:
        VoiceSession, _ = _get_voice_models()
        VoiceSession.objects.create(session_id=session_id)
    except Exception as e:
        print(f'[voice] DB session create error: {e}')

    return JsonResponse({
        'session_id': session_id,
        'message': greeting_text,
        'next_step': 'greeting',
        'gemini_enabled': gemini_ai.enabled,
        'valid_answers': ['exchange', 'return', 'I want to exchange', 'I want to return'],
    })


@csrf_exempt
@require_http_methods(["POST"])
def process_voice(request):
    """Process user voice input and return audio response"""
    try:
        data = json.loads(request.body)
        session_id = data.get('session_id')
        user_text = data.get('text')
        current_step = data.get('current_step')
        
        # Process input
        # Log what goes into the LLM (and the current state)
        print(f"[voice] LLM_INPUT step={current_step} user='{user_text}'")

        result = conversation_flow.process_user_input(session_id, user_text, current_step)
        print(
            f"[voice] session={session_id} step={current_step} -> next={result['next_step']} "
            f"user='{user_text}' response='{result['text'][:200]}'"
        )
        # Log LLM outputs / parsed session data for debugging intent extraction
        try:
            print(f"[voice] LLM_OUTPUT data={json.dumps(result.get('session_data', {}))}")
        except Exception:
            print(f"[voice] LLM_OUTPUT data={result.get('session_data')}")

        # ── Persist turn to database ──────────────────────────────────
        try:
            VoiceSession, VoiceMessage = _get_voice_models()
            vs = VoiceSession.objects.filter(session_id=session_id).first()
            if vs:
                turn_num = vs.messages.count() + 1
                VoiceMessage.objects.create(
                    session=vs,
                    turn_number=turn_num,
                    step=current_step,
                    next_step=result['next_step'],
                    user_text=user_text,
                    bot_text=result['text'],
                    extracted_value=str(result.get('session_data', {}).get(
                        {'greeting': 'request_type',
                         'ask_exchange_or_return': 'request_type',
                         'ask_order_id': 'order_id',
                         'ask_reason': 'reason',
                         'ask_exchange_preference': 'exchange_preference',
                         'confirm_details': 'confirmed',
                        }.get(current_step, ''), '')),
                    session_data_snapshot=result.get('session_data', {}),
                )
                # Update session-level fields from collected data
                sd = result.get('session_data', {})
                vs.request_type = sd.get('request_type', vs.request_type)
                vs.order_id = sd.get('order_id', vs.order_id)
                vs.reason = sd.get('reason', vs.reason)
                vs.exchange_preference = sd.get('exchange_preference', vs.exchange_preference)
                vs.session_data_json = sd
                if result['next_step'] == 'completed':
                    vs.status = 'completed'
                    vs.ended_at = timezone.now()
                    vs.tracking_number = sd.get('tracking_number', '')
                vs.save()
        except Exception as e:
            print(f'[voice] DB log error: {e}')
        # ──────────────────────────────────────────────────────────────
        # Return JSON – TTS is handled in the browser via speechSynthesis
        return JsonResponse({
            'text': result['text'],
            'next_step': result['next_step'],
            'session_data': result['session_data'],
            'valid_answers': result.get('valid_answers', []),
        })
            
    except Exception as e:
        return JsonResponse({'error': str(e)}, status=500)


@csrf_exempt
@require_http_methods(["POST"])
def get_speech(request):
    """Return the text as JSON – browser handles TTS via speechSynthesis"""
    try:
        data = json.loads(request.body)
        text = data.get('text', '')
        return JsonResponse({'text': text})
    except Exception as e:
        return JsonResponse({'error': str(e)}, status=500)