src/apis/routes/speaking_route.py

from fastapi import UploadFile, File, Form, HTTPException, APIRouter
from pydantic import BaseModel
from typing import List, Dict, Optional, Optional
import tempfile
import numpy as np
import re
import warnings
from loguru import logger
from src.apis.controllers.speaking_controller import (
    SimpleG2P,
    PhonemeComparator,
    SimplePronunciationAssessor,
)
from src.utils.speaking_utils import convert_numpy_types

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]  # 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


assessor = SimplePronunciationAssessor()


@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(
        "normal",
        description="Assessment mode: 'normal' (Whisper) or 'advanced' (Wav2Vec2)",
    ),
):
    """
    Pronunciation Assessment API with mode selection

    Key Features:
    - Normal mode: Uses Whisper for more accurate transcription with language model
    - Advanced mode: Uses facebook/wav2vec2-large-960h-lv60-self for character transcription
    - NO language model correction in advanced mode (shows actual pronunciation errors)
    - Character-level accuracy converted to phoneme representation
    - Vietnamese-optimized feedback and tips

    Input: Audio file + Reference text + Mode
    Output: Word highlights + Phoneme differences + Wrong words
    """

    import time

    start_time = time.time()

    # Validate mode
    if mode not in ["normal", "advanced"]:
        raise HTTPException(
            status_code=400, detail="Mode must be 'normal' or 'advanced'"
        )

    # 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 selected mode
            result = assessor.assess_pronunciation(tmp_file.name, reference_text, mode)

        # Get reference phonemes and IPA
        g2p = SimpleG2P()
        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:
        g2p = SimpleG2P()
        phoneme_data = g2p.text_to_phonemes(word)[0]

        # Add difficulty analysis for Vietnamese speakers
        difficulty_scores = []
        comparator = PhonemeComparator()

        for phoneme in phoneme_data["phonemes"]:
            difficulty = comparator.difficulty_map.get(phoneme, 0.3)
            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": comparator.difficulty_map.get(p, 0.3),
                    "vietnamese_tip": get_vietnamese_tip(p),
                }
                for p in phoneme_data["phonemes"]
                if comparator.difficulty_map.get(p, 0.3) > 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}")