src/apis/routes/speaking_route.py

from fastapi import UploadFile, File, Form, HTTPException, APIRouter
from pydantic import BaseModel
from typing import List, Dict, Optional
import tempfile
import numpy as np
import re
import warnings
from loguru import logger
from src.utils.speaking_utils import convert_numpy_types

# Import the new evaluation system
from src.apis.controllers.speaking_controller import ProductionPronunciationAssessor, EnhancedG2P
warnings.filterwarnings("ignore")

router = APIRouter(prefix="/speaking", tags=["Speaking"])


class PronunciationAssessmentResult(BaseModel):
    transcript: str  # What the user actually said (character transcript)
    transcript_phonemes: str  # User's phonemes
    user_phonemes: str  # Alias for transcript_phonemes for UI clarity
    user_ipa: Optional[str] = None  # User's IPA notation
    reference_ipa: str  # Reference IPA notation
    reference_phonemes: str  # Reference phonemes
    character_transcript: str
    overall_score: float
    word_highlights: List[Dict]
    phoneme_differences: List[Dict]
    wrong_words: List[Dict]
    feedback: List[str]
    processing_info: Dict
    # Enhanced features
    phoneme_pairs: Optional[List[Dict]] = None
    phoneme_comparison: Optional[Dict] = None
    prosody_analysis: Optional[Dict] = None
    assessment_mode: Optional[str] = None
    character_level_analysis: Optional[bool] = None


class IPAAssessmentResult(BaseModel):
    """Optimized response model for IPA-focused pronunciation assessment"""
    # Core assessment data
    transcript: str  # What the user actually said
    user_ipa: Optional[str] = None  # User's IPA transcription
    target_word: str  # Target word being assessed
    target_ipa: str  # Target IPA transcription
    overall_score: float  # Overall pronunciation score (0-1)
    
    # Character-level analysis for IPA mapping
    character_analysis: List[Dict]  # Each character with its IPA and score
    
    # Phoneme-specific analysis
    phoneme_scores: List[Dict]  # Individual phoneme scores with colors
    focus_phonemes_analysis: List[Dict]  # Detailed analysis of target phonemes
    
    # Feedback and recommendations
    vietnamese_tips: List[str]  # Vietnamese-specific pronunciation tips
    practice_recommendations: List[str]  # Practice suggestions
    feedback: List[str]  # General feedback messages
    
    # Assessment metadata
    processing_info: Dict  # Processing details
    assessment_type: str = "ipa_focused"
    error: Optional[str] = None

# Global assessor instance - singleton pattern for performance
global_assessor = None

def get_assessor():
    """Get or create the global assessor instance"""
    global global_assessor
    if global_assessor is None:
        logger.info("Creating global ProductionPronunciationAssessor instance...")
        global_assessor = ProductionPronunciationAssessor()
    return global_assessor


@router.post("/assess", response_model=PronunciationAssessmentResult)
async def assess_pronunciation(
    audio_file: UploadFile = File(..., description="Audio file (.wav, .mp3, .m4a)"),
    reference_text: str = Form(..., description="Reference text to pronounce"),
    mode: str = Form(
        "auto",
        description="Assessment mode: 'word', 'sentence', or 'auto' (determined by text length)",
    ),
):
    """
    Enhanced Pronunciation Assessment API with word/sentence mode support

    Key Features:
    - Word mode: For single words or short phrases (1-3 words)
    - Sentence mode: For longer sentences with prosody analysis
    - Advanced phoneme comparison using Levenshtein distance
    - Prosody analysis (pitch, rhythm, intensity) for sentence mode
    - Detailed phoneme pair visualization
    - Vietnamese-optimized feedback and tips

    Input: Audio file + Reference text + Mode
    Output: Enhanced assessment results with visualization data
    """

    import time

    start_time = time.time()

    # Validate mode and set to auto if invalid
    if mode not in ["word", "sentence", "auto"]:
        mode = "auto"  # Set to auto as default instead of throwing error
        logger.info(f"Invalid mode '{mode}' provided, defaulting to 'auto' mode")

    # Validate inputs
    if not reference_text.strip():
        raise HTTPException(status_code=400, detail="Reference text cannot be empty")

    if len(reference_text) > 500:
        raise HTTPException(
            status_code=400, detail="Reference text too long (max 500 characters)"
        )

    # Check for valid English characters
    if not re.match(r"^[a-zA-Z\s\'\-\.!?,;:]+$", reference_text):
        raise HTTPException(
            status_code=400,
            detail="Text must contain only English letters, spaces, and basic punctuation",
        )

    try:
        # Save uploaded file temporarily
        file_extension = ".wav"
        if audio_file.filename and "." in audio_file.filename:
            file_extension = f".{audio_file.filename.split('.')[-1]}"

        with tempfile.NamedTemporaryFile(
            delete=False, suffix=file_extension
        ) as tmp_file:
            content = await audio_file.read()
            tmp_file.write(content)
            tmp_file.flush()

            logger.info(f"Processing audio file: {tmp_file.name} with mode: {mode}")

            # Run assessment using enhanced assessor (singleton)
            assessor = get_assessor()
            result = assessor.assess_pronunciation(tmp_file.name, reference_text, mode)
            
            # Get reference phonemes and IPA
            g2p = EnhancedG2P()
            reference_words = reference_text.strip().split()
            reference_phonemes_list = []
            reference_ipa_list = []
            
            for word in reference_words:
                word_phonemes = g2p.text_to_phonemes(word.strip('.,!?;:'))[0]
                reference_phonemes_list.append(word_phonemes["phoneme_string"])
                reference_ipa_list.append(word_phonemes["ipa"])
            
            # Join phonemes and IPA for the full text
            result["reference_phonemes"] = " ".join(reference_phonemes_list)
            result["reference_ipa"] = " ".join(reference_ipa_list)
            
            # Create user_ipa from transcript using G2P (same way as reference)
            if "transcript" in result and result["transcript"]:
                try:
                    user_transcript = result["transcript"].strip()
                    user_words = user_transcript.split()
                    user_ipa_list = []
                    
                    for word in user_words:
                        clean_word = word.strip('.,!?;:').lower()
                        if clean_word:  # Skip empty words
                            try:
                                word_phonemes = g2p.text_to_phonemes(clean_word)[0]
                                user_ipa_list.append(word_phonemes["ipa"])
                            except Exception as e:
                                logger.warning(f"Failed to get IPA for word '{clean_word}': {e}")
                                # Fallback: use the word itself
                                user_ipa_list.append(f"/{clean_word}/")
                    
                    result["user_ipa"] = " ".join(user_ipa_list) if user_ipa_list else None
                    logger.info(f"Generated user IPA from transcript '{user_transcript}': '{result['user_ipa']}'")
                except Exception as e:
                    logger.warning(f"Failed to generate user IPA from transcript: {e}")
                    result["user_ipa"] = None
            else:
                result["user_ipa"] = None

        # Add processing time
        processing_time = time.time() - start_time
        result["processing_info"]["processing_time"] = processing_time

        # Convert numpy types for JSON serialization
        final_result = convert_numpy_types(result)

        logger.info(
            f"Assessment completed in {processing_time:.2f} seconds using {mode} mode"
        )

        return PronunciationAssessmentResult(**final_result)

    except Exception as e:
        logger.error(f"Assessment error: {str(e)}")
        import traceback

        traceback.print_exc()
        raise HTTPException(status_code=500, detail=f"Assessment failed: {str(e)}")


@router.post("/assess-ipa", response_model=IPAAssessmentResult)
async def assess_ipa_pronunciation(
    audio_file: UploadFile = File(..., description="Audio file (.wav, .mp3, .m4a)"),
    target_word: str = Form(..., description="Target word to assess (e.g., 'bed')"),
    target_ipa: str = Form(None, description="Target IPA notation (e.g., '/bɛd/')"),
    focus_phonemes: str = Form(None, description="Comma-separated focus phonemes (e.g., 'ɛ,b')"),
):
    """
    Optimized IPA pronunciation assessment for phoneme-focused learning
    
    Evaluates:
    - Overall word pronunciation accuracy
    - Character-to-phoneme mapping accuracy 
    - Specific phoneme pronunciation (e.g., /ɛ/ in 'bed')
    - Vietnamese-optimized feedback and tips
    - Dynamic color scoring for UI visualization
    
    Example: Assessing 'bed' /bɛd/ with focus on /ɛ/ phoneme
    """
    
    import time
    start_time = time.time()
    
    # Validate inputs
    if not target_word.strip():
        raise HTTPException(status_code=400, detail="Target word cannot be empty")
    
    if len(target_word) > 50:
        raise HTTPException(status_code=400, detail="Target word too long (max 50 characters)")
    
    # Clean target word
    target_word = target_word.strip().lower()
    
    try:
        # Save uploaded file temporarily
        file_extension = ".wav"
        if audio_file.filename and "." in audio_file.filename:
            file_extension = f".{audio_file.filename.split('.')[-1]}"

        with tempfile.NamedTemporaryFile(delete=False, suffix=file_extension) as tmp_file:
            content = await audio_file.read()
            tmp_file.write(content)
            tmp_file.flush()

            logger.info(f"IPA assessment for word '{target_word}' with IPA '{target_ipa}'")

            # Get the assessor instance
            assessor = get_assessor()
            
            # Run base pronunciation assessment in word mode
            base_result = assessor.assess_pronunciation(tmp_file.name, target_word, "word")
            
            # Get target IPA and phonemes using G2P
            g2p = EnhancedG2P()
            
            if not target_ipa:
                target_phonemes_data = g2p.text_to_phonemes(target_word)[0]
                target_ipa = target_phonemes_data["ipa"]
                target_phonemes = target_phonemes_data["phonemes"]
            else:
                # Parse provided IPA
                clean_ipa = target_ipa.replace("/", "").strip()
                target_phonemes = list(clean_ipa)  # Simple phoneme parsing
            
            # Parse focus phonemes
            focus_phonemes_list = []
            if focus_phonemes:
                focus_phonemes_list = [p.strip() for p in focus_phonemes.split(",")]
            
            # Character-level analysis for UI mapping
            character_analysis = []
            target_chars = list(target_word)
            target_phoneme_chars = list(target_ipa.replace("/", ""))
            
            for i, char in enumerate(target_chars):
                # Map character to its phoneme
                char_phoneme = target_phoneme_chars[i] if i < len(target_phoneme_chars) else ""
                
                # Calculate character-level score based on overall assessment
                char_score = base_result.get("overall_score", 0.0)
                
                # If we have detailed phoneme analysis, use specific scores
                if base_result.get("phoneme_differences"):
                    for phoneme_diff in base_result["phoneme_differences"]:
                        if phoneme_diff.get("reference_phoneme") == char_phoneme:
                            char_score = phoneme_diff.get("score", char_score)
                            break
                
                # Color coding based on score
                color_class = "text-green-600" if char_score > 0.8 else \
                            "text-yellow-600" if char_score > 0.6 else "text-red-600"
                
                character_analysis.append({
                    "character": char,
                    "phoneme": char_phoneme,
                    "score": float(char_score),
                    "color_class": color_class,
                    "is_focus": char_phoneme in focus_phonemes_list
                })
            
            # Phoneme-specific scoring for visualization
            phoneme_scores = []
            for phoneme in target_phonemes:
                phoneme_score = base_result.get("overall_score", 0.0)
                
                # Find specific phoneme score from assessment
                if base_result.get("phoneme_differences"):
                    for phoneme_diff in base_result["phoneme_differences"]:
                        if phoneme_diff.get("reference_phoneme") == phoneme:
                            phoneme_score = phoneme_diff.get("score", phoneme_score)
                            break
                
                # Color coding for phonemes
                color_class = "bg-green-100 text-green-800" if phoneme_score > 0.8 else \
                            "bg-yellow-100 text-yellow-800" if phoneme_score > 0.6 else \
                            "bg-red-100 text-red-800"
                
                phoneme_scores.append({
                    "phoneme": phoneme,
                    "score": float(phoneme_score),
                    "color_class": color_class,
                    "percentage": int(phoneme_score * 100),
                    "is_focus": phoneme in focus_phonemes_list
                })
            
            # Focus phonemes detailed analysis
            focus_phonemes_analysis = []
            
            for focus_phoneme in focus_phonemes_list:
                phoneme_analysis = {
                    "phoneme": focus_phoneme,
                    "score": base_result.get("overall_score", 0.0),
                    "status": "correct",
                    "vietnamese_tip": get_vietnamese_tip(focus_phoneme),
                    "difficulty": "medium",
                    "color_class": "bg-green-100 text-green-800"
                }
                
                # Get specific analysis from base result
                if base_result.get("phoneme_differences"):
                    for phoneme_diff in base_result["phoneme_differences"]:
                        if phoneme_diff.get("reference_phoneme") == focus_phoneme:
                            score = phoneme_diff.get("score", 0.0)
                            phoneme_analysis.update({
                                "score": float(score),
                                "status": phoneme_diff.get("status", "unknown"),
                                "color_class": "bg-green-100 text-green-800" if score > 0.8 else
                                             "bg-yellow-100 text-yellow-800" if score > 0.6 else 
                                             "bg-red-100 text-red-800"
                            })
                            break
                
                focus_phonemes_analysis.append(phoneme_analysis)
            
            # Vietnamese-specific tips
            vietnamese_tips = []
            difficult_phonemes = ["θ", "ð", "v", "z", "ʒ", "r", "w", "æ", "ɪ", "ʊ", "ɛ"]
            
            for phoneme in set(target_phonemes + focus_phonemes_list):
                if phoneme in difficult_phonemes:
                    tip = get_vietnamese_tip(phoneme)
                    if tip not in vietnamese_tips:
                        vietnamese_tips.append(tip)
            
            # Practice recommendations based on score
            practice_recommendations = []
            overall_score = base_result.get("overall_score", 0.0)
            
            if overall_score < 0.7:
                practice_recommendations.extend([
                    "Nghe từ mẫu nhiều lần trước khi phát âm",
                    "Phát âm chậm và rõ ràng từng âm vị",
                    "Chú ý đến vị trí lưỡi và môi khi phát âm"
                ])
                
                # Add specific recommendations for focus phonemes
                for analysis in focus_phonemes_analysis:
                    if analysis["score"] < 0.6:
                        practice_recommendations.append(
                            f"Luyện đặc biệt âm /{analysis['phoneme']}/: {analysis['vietnamese_tip']}"
                        )
            
            if overall_score >= 0.8:
                practice_recommendations.append("Phát âm rất tốt! Tiếp tục luyện tập để duy trì chất lượng")
            elif overall_score >= 0.6:
                practice_recommendations.append("Phát âm khá tốt, cần cải thiện một số âm vị")
            
            # Handle error cases
            error_message = None
            feedback = base_result.get("feedback", [])
            
            if base_result.get("error"):
                error_message = base_result["error"]
                feedback = [f"Lỗi: {error_message}"]
            
            # Processing information
            processing_time = time.time() - start_time
            processing_info = {
                "processing_time": processing_time,
                "mode": "ipa_focused",
                "model_used": "Wav2Vec2-Enhanced",
                "confidence": base_result.get("processing_info", {}).get("confidence", 0.0),
                "enhanced_features": True
            }
            
            # Create final result
            result = IPAAssessmentResult(
                transcript=base_result.get("transcript", ""),
                user_ipa=base_result.get("user_ipa", ""),
                target_word=target_word,
                target_ipa=target_ipa,
                overall_score=float(overall_score),
                character_analysis=character_analysis,
                phoneme_scores=phoneme_scores,
                focus_phonemes_analysis=focus_phonemes_analysis,
                vietnamese_tips=vietnamese_tips,
                practice_recommendations=practice_recommendations,
                feedback=feedback,
                processing_info=processing_info,
                error=error_message
            )
            
            logger.info(f"IPA assessment completed for '{target_word}' in {processing_time:.2f}s with score {overall_score:.2f}")
            
            return result

    except Exception as e:
        logger.error(f"IPA assessment error: {str(e)}")
        import traceback
        traceback.print_exc()
        raise HTTPException(status_code=500, detail=f"IPA assessment failed: {str(e)}")


# =============================================================================
# UTILITY ENDPOINTS
# =============================================================================


@router.get("/phonemes/{word}")
def get_word_phonemes(word: str):
    """Get phoneme breakdown for a specific word"""
    try:
        # Use the new EnhancedG2P from evaluation module
        from evalution import EnhancedG2P
        g2p = EnhancedG2P()
        phoneme_data = g2p.text_to_phonemes(word)[0]

        # Add difficulty analysis for Vietnamese speakers
        difficulty_scores = []
        
        for phoneme in phoneme_data["phonemes"]:
            difficulty = g2p.get_difficulty_score(phoneme)
            difficulty_scores.append(difficulty)

        avg_difficulty = float(np.mean(difficulty_scores)) if difficulty_scores else 0.3

        return {
            "word": word,
            "phonemes": phoneme_data["phonemes"],
            "phoneme_string": phoneme_data["phoneme_string"],
            "ipa": phoneme_data["ipa"],
            "difficulty_score": avg_difficulty,
            "difficulty_level": (
                "hard"
                if avg_difficulty > 0.6
                else "medium" if avg_difficulty > 0.4 else "easy"
            ),
            "challenging_phonemes": [
                {
                    "phoneme": p,
                    "difficulty": g2p.get_difficulty_score(p),
                    "vietnamese_tip": get_vietnamese_tip(p),
                }
                for p in phoneme_data["phonemes"]
                if g2p.get_difficulty_score(p) > 0.6
            ],
        }

    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Word analysis error: {str(e)}")


def get_vietnamese_tip(phoneme: str) -> str:
    """Get Vietnamese pronunciation tip for a phoneme"""
    tips = {
        "θ": "Đặt lưỡi giữa răng, thổi nhẹ",
        "ð": "Giống θ nhưng rung dây thanh âm",
        "v": "Môi dưới chạm răng trên",
        "r": "Cuộn lưỡi, không chạm vòm miệng",
        "l": "Lưỡi chạm vòm miệng sau răng",
        "z": "Như 's' nhưng rung dây thanh",
        "ʒ": "Như 'ʃ' nhưng rung dây thanh",
        "w": "Tròn môi như 'u'",
        "ɛ": "Mở miệng vừa phải, lưỡi hạ thấp như 'e' tiếng Việt",
        "æ": "Mở miệng rộng, lưỡi thấp như nói 'a' nhưng ngắn hơn",
        "ɪ": "Âm 'i' ngắn, lưỡi không căng như 'i' tiếng Việt",
        "ʊ": "Âm 'u' ngắn, môi tròn nhẹ",
        "ə": "Âm trung tính, miệng thả lỏng",
        "ɔ": "Mở miệng tròn như 'o' nhưng rộng hơn",
        "ʌ": "Miệng mở vừa, lưỡi ở giữa",
        "f": "Răng trên chạm môi dưới, thổi nhẹ",
        "b": "Hai môi chạm nhau, rung dây thanh",
        "p": "Hai môi chạm nhau, không rung dây thanh",
        "d": "Lưỡi chạm nướu răng trên, rung dây thanh",
        "t": "Lưỡi chạm nướu răng trên, không rung dây thanh",
        "k": "Lưỡi chạm vòm miệng, không rung dây thanh",
        "g": "Lưỡi chạm vòm miệng, rung dây thanh"
    }
    return tips.get(phoneme, f"Luyện tập phát âm /{phoneme}/")


def get_phoneme_difficulty(phoneme: str) -> str:
    """Get difficulty level for Vietnamese speakers"""
    hard_phonemes = ["θ", "ð", "r", "w", "æ", "ʌ", "ɪ", "ʊ"]
    medium_phonemes = ["v", "z", "ʒ", "ɛ", "ə", "ɔ", "f"]
    
    if phoneme in hard_phonemes:
        return "hard"
    elif phoneme in medium_phonemes:
        return "medium"
    else:
        return "easy"