import os
import json

from chatbot_model import ChatbotConfig, 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

logger = config_logger(__name__)

def run_interactive_chat(chatbot, quality_checker):
    """Separate function for interactive chat loop."""
    while True:
        try:
            user_input = input("You: ")
        except (KeyboardInterrupt, EOFError):
            print("\nAssistant: Goodbye!")
            break

        if user_input.lower() in ["quit", "exit", "bye"]:
            print("Assistant: Goodbye!")
            break

        response, candidates, metrics = chatbot.chat(
            query=user_input,
            conversation_history=None,
            quality_checker=quality_checker,
            top_k=10
        )

        print(f"Assistant: {response}")

        # Show alternative responses if confident
        if metrics.get("is_confident", False):
            print("\nAlternative responses:")
            for resp, score in candidates[1:4]:
                print(f"Score: {score:.4f} - {resp}")
        else:
            print("\n[Low Confidence]: Consider rephrasing your query for better assistance.")

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

    MODEL_DIR = "new_iteration/data_prep_iterative_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.")

    # Load RetrievalChatbot in 'inference' mode using the classmethod
    try:
        chatbot = RetrievalChatbot.load_model(load_dir=MODEL_DIR, mode="inference")
        logger.info("RetrievalChatbot loaded in 'inference' mode successfully.")
    except Exception as e:
        logger.error(f"Failed to load RetrievalChatbot: {e}")
        return

    # Confirm FAISS index & response pool exist
    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

    # Load specific FAISS index and response pool
    try:
        # Even though load_model might auto-load an index, we override here with the specific file
        chatbot.data_pipeline.load_faiss_index(FAISS_INDEX_PATH)
        logger.info(f"FAISS index loaded from {FAISS_INDEX_PATH}.")

        print("FAISS dimensions:", chatbot.data_pipeline.index.d)
        print("FAISS index type:", type(chatbot.data_pipeline.index))
        print("FAISS index total vectors:", chatbot.data_pipeline.index.ntotal)
        print("FAISS is_trained:", chatbot.data_pipeline.index.is_trained)

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

        print("\nTotal responses in pool:", len(chatbot.data_pipeline.response_pool))

        # Validate dimension consistency
        chatbot.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
    quality_checker = ResponseQualityChecker(data_pipeline=chatbot.data_pipeline)
    validator = ChatbotValidator(chatbot=chatbot, quality_checker=quality_checker)
    logger.info("ResponseQualityChecker and ChatbotValidator initialized.")

    # Run validation
    try:
        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
    # logger.info("\nStarting interactive chat session...")
    # run_interactive_chat(chatbot, quality_checker)

if __name__ == "__main__":
    validate_chatbot()