services/sentence_transformer_service.py

import os
import pickle
import pandas as pd
import warnings

# Suppress pandas warnings globally
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=pd.errors.SettingWithCopyWarning)
pd.set_option("mode.chained_assignment", None)

import sys
from config import (
    MODEL_NAME,
    MODEL_TYPE,
    DEVICE_TYPE,
    SENTENCE_EMBEDDING_FILE,
    STANDARD_NAME_MAP_DATA_FILE,
    SUBJECT_DATA_FILE,
    DATA_DIR,
    HALF,
    ABSTRACT_MAP_DATA_FILE,
    NAME_ABSTRACT_MAP_DATA_FILE,
)

# Add the path to import modules from meisai-check-ai
# sys.path.append(os.path.join(os.path.dirname(__file__), "..", "meisai-check-ai"))

from sentence_transformer_lib.sentence_transformer_helper import SentenceTransformerHelper
from sentence_transformer_lib.cached_embedding_helper import CachedEmbeddingHelper

# Cache file paths for different types of embeddings
CACHED_EMBEDDINGS_SUBJECT_FILE = os.path.join(DATA_DIR, "cached_embeddings_subject.pkl")
CACHED_EMBEDDINGS_NAME_FILE = os.path.join(DATA_DIR, "cached_embeddings_name.pkl")
CACHED_EMBEDDINGS_ABSTRACT_FILE = os.path.join(
    DATA_DIR, "cached_embeddings_abstract.pkl"
)
CACHED_EMBEDDINGS_SUB_SUBJECT_FILE = os.path.join(
    DATA_DIR, "cached_embeddings_sub_subject.pkl"
)
CACHED_EMBEDDINGS_UNIT_FILE = os.path.join(DATA_DIR, "cached_embeddings_unit.pkl")


def load_cached_embeddings_by_type(cache_type):
    """Load cached embeddings from file based on type"""
    cache_files = {
        "subject": CACHED_EMBEDDINGS_SUBJECT_FILE,
        "name": CACHED_EMBEDDINGS_NAME_FILE,
        "abstract": CACHED_EMBEDDINGS_ABSTRACT_FILE,
        "sub_subject": CACHED_EMBEDDINGS_SUB_SUBJECT_FILE,
        "unit": CACHED_EMBEDDINGS_UNIT_FILE,
    }

    cache_file = cache_files.get(cache_type)
    if not cache_file:
        print(f"Unknown cache type: {cache_type}")
        return {}

    if os.path.exists(cache_file):
        try:
            with open(cache_file, "rb") as f:
                cached_embeddings = pickle.load(f)
            print(
                f"Loaded {cache_type} embeddings with {len(cached_embeddings)} entries from {cache_file}"
            )
            return cached_embeddings
        except Exception as e:
            print(f"Error loading {cache_type} embeddings: {e}")
            return {}
    else:
        print(
            f"No {cache_type} embeddings cache file found. Starting with empty cache."
        )
        return {}


def save_cached_embeddings_by_type(cached_embedding_helper, cache_type):
    """Save cached embeddings to file based on type"""
    cache_files = {
        "subject": CACHED_EMBEDDINGS_SUBJECT_FILE,
        "name": CACHED_EMBEDDINGS_NAME_FILE,
        "abstract": CACHED_EMBEDDINGS_ABSTRACT_FILE,
        "sub_subject": CACHED_EMBEDDINGS_SUB_SUBJECT_FILE,
        "unit": CACHED_EMBEDDINGS_UNIT_FILE,
    }

    cache_file = cache_files.get(cache_type)
    if not cache_file:
        print(f"Unknown cache type: {cache_type}")
        return

    try:
        # Ensure directory exists
        os.makedirs(os.path.dirname(cache_file), exist_ok=True)
        cached_embeddings = cached_embedding_helper._cached_sentence_embeddings
        with open(cache_file, "wb") as f:
            pickle.dump(cached_embeddings, f)
        print(
            f"Saved {cache_type} embeddings with {len(cached_embeddings)} entries to {cache_file}"
        )
    except Exception as e:
        print(f"Error saving {cache_type} embeddings: {e}")


def create_cached_embedding_helper_for_type(sentence_transformer, cache_type):
    """Create a CachedEmbeddingHelper for specific embedding type"""
    cached_embeddings = load_cached_embeddings_by_type(cache_type)
    return CachedEmbeddingHelper(
        sentence_transformer, cached_sentence_embeddings=cached_embeddings
    )


class SentenceTransformerService:
    def __init__(self):
        self.sentenceTransformerHelper = None

        # Different cached embedding helpers for different types
        self.unit_cached_embedding_helper = None
        self.subject_cached_embedding_helper = None
        self.sub_subject_cached_embedding_helper = None
        self.name_cached_embedding_helper = None
        self.abstract_cached_embedding_helper = None

        # Map data holders
        self.df_unit_map_data = None
        self.df_subject_map_data = None
        self.df_standard_subject_map_data = None
        self.df_sub_subject_map_data = None
        self.df_name_map_data = None
        self.df_abstract_map_data = None
        self.df_name_and_subject_map_data = None
        self.df_sub_subject_and_name_map_data = None

    def load_model_data(self):
        """Load model and data only once at startup"""
        if self.sentenceTransformerHelper is not None:
            print("Model already loaded. Skipping reload.")
            return  # Không load lại nếu đã có model

        print("Loading models and data...")
        # Load sentence transformer model
        print(f"Loading model {MODEL_NAME} with type {MODEL_TYPE} and half={HALF}")
        self.sentenceTransformerHelper = SentenceTransformerHelper(
            model_name=MODEL_NAME, model_type=MODEL_TYPE, half=HALF
        )

        # Create different cached embedding helpers for different types
        self.unit_cached_embedding_helper = create_cached_embedding_helper_for_type(
            self.sentenceTransformerHelper, "unit"
        )
        self.subject_cached_embedding_helper = create_cached_embedding_helper_for_type(
            self.sentenceTransformerHelper, "subject"
        )
        self.sub_subject_cached_embedding_helper = (
            create_cached_embedding_helper_for_type(
                self.sentenceTransformerHelper, "sub_subject"
            )
        )
        self.name_cached_embedding_helper = create_cached_embedding_helper_for_type(
            self.sentenceTransformerHelper, "name"
        )
        self.abstract_cached_embedding_helper = create_cached_embedding_helper_for_type(
            self.sentenceTransformerHelper, "abstract"
        )

        # Load map data from CSV files (assuming they exist)
        self._load_map_data()

        print("Models and data loaded successfully")

    def _load_map_data(self):
        """Load all mapping data from CSV files"""
        try:
            import pandas as pd

            # Load unit map data
            unit_map_file = os.path.join(DATA_DIR, "unitMapData.csv")
            if os.path.exists(unit_map_file):
                self.df_unit_map_data = pd.read_csv(unit_map_file)
                print(f"Loaded unit map data: {len(self.df_unit_map_data)} entries")

            # Load subject map data
            subject_map_file = os.path.join(DATA_DIR, "subjectMapData.csv")
            if os.path.exists(subject_map_file):
                self.df_subject_map_data = pd.read_csv(subject_map_file)
                print(
                    f"Loaded subject map data: {len(self.df_subject_map_data)} entries"
                )

            # Load standard subject map data
            standard_subject_map_file = os.path.join(
                DATA_DIR, "standardSubjectMapData.csv"
            )
            if os.path.exists(standard_subject_map_file):
                self.df_standard_subject_map_data = pd.read_csv(
                    standard_subject_map_file
                )
                print(
                    f"Loaded standard subject map data: {len(self.df_standard_subject_map_data)} entries"
                )

            # Load sub subject map data
            sub_subject_map_file = os.path.join(DATA_DIR, "subSubjectMapData.csv")
            if os.path.exists(sub_subject_map_file):
                self.df_sub_subject_map_data = pd.read_csv(sub_subject_map_file)
                print(
                    f"Loaded sub subject map data: {len(self.df_sub_subject_map_data)} entries"
                )

            # Load name map data
            name_map_file = os.path.join(DATA_DIR, "nameMapData.csv")
            if os.path.exists(name_map_file):
                self.df_name_map_data = pd.read_csv(name_map_file)
                print(f"Loaded name map data: {len(self.df_name_map_data)} entries")

            # Load abstract map data
            abstract_map_file = os.path.join(DATA_DIR, "abstractMapData.csv")
            if os.path.exists(abstract_map_file):
                self.df_abstract_map_data = pd.read_csv(abstract_map_file)
                print(
                    f"Loaded abstract map data: {len(self.df_abstract_map_data)} entries"
                )
                print(
                    f"DEBUG: Abstract map data columns: {list(self.df_abstract_map_data.columns)}"
                )
                print(f"DEBUG: Abstract map data sample:")
                print(self.df_abstract_map_data.head(3).to_string())
            else:
                print(f"DEBUG: Abstract map file not found: {abstract_map_file}")

            # Load name and subject map data
            name_and_subject_map_file = os.path.join(
                DATA_DIR, "nameAndSubjectMapData.csv"
            )
            if os.path.exists(name_and_subject_map_file):
                self.df_name_and_subject_map_data = pd.read_csv(
                    name_and_subject_map_file
                )
                print(
                    f"Loaded name and subject map data: {len(self.df_name_and_subject_map_data)} entries"
                )

            # Load sub subject and name map data
            sub_subject_and_name_map_file = os.path.join(
                DATA_DIR, "subSubjectAndNameMapData.csv"
            )
            if os.path.exists(sub_subject_and_name_map_file):
                self.df_sub_subject_and_name_map_data = pd.read_csv(
                    sub_subject_and_name_map_file
                )
                print(
                    f"Loaded sub subject and name map data: {len(self.df_sub_subject_and_name_map_data)} entries"
                )

        except Exception as e:
            print(f"Error loading map data: {e}")

    def save_all_caches(self):
        """Save all cached embeddings"""
        try:
            if self.unit_cached_embedding_helper:
                save_cached_embeddings_by_type(
                    self.unit_cached_embedding_helper, "unit"
                )
            if self.subject_cached_embedding_helper:
                save_cached_embeddings_by_type(
                    self.subject_cached_embedding_helper, "subject"
                )
            if self.sub_subject_cached_embedding_helper:
                save_cached_embeddings_by_type(
                    self.sub_subject_cached_embedding_helper, "sub_subject"
                )
            if self.name_cached_embedding_helper:
                save_cached_embeddings_by_type(
                    self.name_cached_embedding_helper, "name"
                )
            if self.abstract_cached_embedding_helper:
                save_cached_embeddings_by_type(
                    self.abstract_cached_embedding_helper, "abstract"
                )

            # Print cache statistics summary
            print("\n" + "=" * 60)
            print("EMBEDDING CACHE PERFORMANCE SUMMARY")
            print("=" * 60)

            total_cache_size = 0
            if self.unit_cached_embedding_helper:
                unit_size = len(
                    self.unit_cached_embedding_helper._cached_sentence_embeddings
                )
                total_cache_size += unit_size
                print(f"Unit cache: {unit_size} embeddings")

            if self.subject_cached_embedding_helper:
                subject_size = len(
                    self.subject_cached_embedding_helper._cached_sentence_embeddings
                )
                total_cache_size += subject_size
                print(f"Subject cache: {subject_size} embeddings")

            if self.sub_subject_cached_embedding_helper:
                sub_subject_size = len(
                    self.sub_subject_cached_embedding_helper._cached_sentence_embeddings
                )
                total_cache_size += sub_subject_size
                print(f"Sub-subject cache: {sub_subject_size} embeddings")

            if self.name_cached_embedding_helper:
                name_size = len(
                    self.name_cached_embedding_helper._cached_sentence_embeddings
                )
                total_cache_size += name_size
                print(f"Name cache: {name_size} embeddings")

            if self.abstract_cached_embedding_helper:
                abstract_size = len(
                    self.abstract_cached_embedding_helper._cached_sentence_embeddings
                )
                total_cache_size += abstract_size
                print(f"Abstract cache: {abstract_size} embeddings")

            print(f"Total cached embeddings: {total_cache_size}")
            print("=" * 60)

        except Exception as e:
            print(f"Error saving caches: {e}")


# Global instance (singleton)
sentence_transformer_service = SentenceTransformerService()