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 evalution 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

assessor = ProductionPronunciationAssessor()


@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
            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)}")


# =============================================================================
# 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'",
    }
    return tips.get(phoneme, f"Luyện âm {phoneme}")