import os
import json
from sentence_transformers import SentenceTransformer
from chatbot_config import ChatbotConfig
from chatbot_model import RetrievalChatbot
from response_quality_checker import ResponseQualityChecker
from chatbot_validator import ChatbotValidator
from plotter import Plotter
from environment_setup import EnvironmentSetup
from logger_config import config_logger
from tf_data_pipeline import TFDataPipeline

logger = config_logger(__name__)

def run_chatbot_validation():
    # Initialize environment
    env = EnvironmentSetup()
    env.initialize()

    MODEL_DIR = "models"
    FAISS_INDICES_DIR = os.path.join(MODEL_DIR, "faiss_indices")
    FAISS_INDEX_PRODUCTION_PATH = os.path.join(FAISS_INDICES_DIR, "faiss_index_production.index")
    FAISS_INDEX_TEST_PATH = os.path.join(FAISS_INDICES_DIR, "faiss_index_test.index")

    # Toggle 'production' or 'test' env
    ENVIRONMENT = "production"
    if ENVIRONMENT == "test":
        FAISS_INDEX_PATH = FAISS_INDEX_TEST_PATH
        RESPONSE_POOL_PATH = FAISS_INDEX_TEST_PATH.replace(".index", "_responses.json")
    else:
        FAISS_INDEX_PATH = FAISS_INDEX_PRODUCTION_PATH
        RESPONSE_POOL_PATH = FAISS_INDEX_PRODUCTION_PATH.replace(".index", "_responses.json")

    # Load the config
    config_path = os.path.join(MODEL_DIR, "config.json")
    if os.path.exists(config_path):
        with open(config_path, "r", encoding="utf-8") as f:
            config_dict = json.load(f)
        config = ChatbotConfig.from_dict(config_dict)
        logger.info(f"Loaded ChatbotConfig from {config_path}")
    else:
        config = ChatbotConfig()
        logger.warning("No config.json found. Using default ChatbotConfig.")

    # Init SentenceTransformer
    try:
        encoder = SentenceTransformer(config.pretrained_model)
        logger.info(f"Loaded SentenceTransformer model: {config.pretrained_model}")
    except Exception as e:
        logger.error(f"Failed to load SentenceTransformer: {e}")
        return

    # Load FAISS index and response pool
    try:
        # Initialize TFDataPipeline
        data_pipeline = TFDataPipeline(
            config=config,
            tokenizer=encoder.tokenizer,
            encoder=encoder,
            response_pool=[],
            query_embeddings_cache={},
            index_type='IndexFlatIP',
            faiss_index_file_path=FAISS_INDEX_PATH
        )

        if not os.path.exists(FAISS_INDEX_PATH) or not os.path.exists(RESPONSE_POOL_PATH):
            logger.error("FAISS index or response pool file is missing.")
            return

        data_pipeline.load_faiss_index(FAISS_INDEX_PATH)
        logger.info(f"FAISS index loaded from {FAISS_INDEX_PATH}.")

        with open(RESPONSE_POOL_PATH, "r", encoding="utf-8") as f:
            data_pipeline.response_pool = json.load(f)
            logger.info(f"Response pool loaded from {RESPONSE_POOL_PATH}.")
            logger.info(f"Total responses in pool: {len(data_pipeline.response_pool)}")

        # Validate dimension consistency
        data_pipeline.validate_faiss_index()
        logger.info("FAISS index and response pool validated successfully.")
    except Exception as e:
        logger.error(f"Failed to load or validate FAISS index: {e}")
        return

    # Init QualityChecker and Validator
    try:
        chatbot = RetrievalChatbot.load_model(load_dir=MODEL_DIR, mode="inference")
        quality_checker = ResponseQualityChecker(data_pipeline=data_pipeline)
        validator = ChatbotValidator(chatbot=chatbot, quality_checker=quality_checker)  
        logger.info("ResponseQualityChecker and ChatbotValidator initialized.")

        # Run validation
        validation_metrics = validator.run_validation(num_examples=5)
        logger.info(f"Validation Metrics: {validation_metrics}")
    except Exception as e:
        logger.error(f"Validation process failed: {e}")
        return

    # Plot metrics
    try:
        plotter = Plotter(save_dir=env.training_dirs["plots"])
        plotter.plot_validation_metrics(validation_metrics)
        logger.info("Validation metrics plotted successfully.")
    except Exception as e:
        logger.error(f"Failed to plot validation metrics: {e}")

    # Run interactive chat loop
    try:
        logger.info("\nStarting interactive chat session...")
        chatbot.run_interactive_chat(quality_checker=quality_checker, show_alternatives=True)
    except Exception as e:
        logger.error(f"Interactive chat session failed: {e}")
    
    
if __name__ == "__main__":
    run_chatbot_validation()