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#from models import *
#from preprocess_data import *
from utils import extract_sentences_by_intent, train_model_with_chkpt, batch_predict_and_save
from time import time
import logging
from config import *





if __name__ == '__main__':
    # train_model_with_chkpt(epochs=5, batch_size=16, lr=2e-3,
    #             save_model=True,
    #             save_path='gnn_model_checkpoint.pt',
    #             resume=True)

    from transformers import BertTokenizer
    from preprocess_data import SentenceDataset
    from models import SentenceExtractionModel
    from utils import train_sentence_extractor

    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    dataset = SentenceDataset("data/Fin_ExBERT_train_val_data.xlsx", tokenizer)


    model = SentenceExtractionModel(
        base_model_name=MODEL_NAME,
        backbone='finexbert'
    )

    # train_sentence_extractor(
    #     model,
    #     dataset,
    #     output_dir="checkpoints/sentence_extractor",
    #     val_split=0.3,
    #     epochs=10,
    #     batch_size=16,
    #     lr=3e-4,
    #     device=DEVICE,
    #     unfreeze_after_epoch=4
    # )
    #
    # from transformers import BertTokenizer
    # from models import SentenceExtractionModel
    # from utils import demo_on_random_val
    #
    # tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
    # model = SentenceExtractionModel(
    #     base_model_name=MODEL_NAME,
    #     backbone='finexbert'
    # )
    #
    # demo_on_random_val(
    #     model,
    #     tokenizer,
    #     excel_path="data/Fin_ExBERT_test_set.xlsx",
    #     ckpt_path="checkpoints/sentence_extractor/best_model.pth",
    #     device="cuda",  # or "cpu"
    #     temperature=1,
    # )

    batch_predict_and_save(
        model,
        tokenizer,
        excel_path="data/Fin_ExBERT_test_set.xlsx",
        ckpt_path="checkpoints/sentence_extractor/best_model.pth",
        output_path="results/predictions_sample200.xlsx",
        n_samples=200,
        temperature=1.0,
        device="cuda"
    )

    sample_transcript = """
    Agent: Hello, thank you for calling Acme Financial Services. My name is Priya. How can I help you today?
    Customer: Hi Priya, I’m considering opening a new savings account with you.
    Agent: Absolutely—our savings account offers 4% interest per annum. Do you have a balance in mind?
    Customer: Yes, I’d like to deposit ₹50,000 initially, and then I’m interested in investing another ₹2 lakh in mutual funds over the next month.
    Agent: Great, we have several mutual fund options. Are you more growth-oriented or looking for steady income?
    Customer: I want to focus on growth. Also, could you tell me about your home loan rates? I may need a ₹30 lakh mortgage in the next six months.
    Agent: Certainly—we currently offer home loan rates starting at 6.8%. Do you already own property or are you planning to buy?
    Customer: Planning to buy. Finally, I’d like to apply for a credit card with a high cashback—maybe one that gives 2% on all spends.
    Agent: We have a Platinum Cashback Card at 1.5%, and our Signature Cashback Card at 2%. Would you like me to initiate the application?
    Customer: Yes please, go ahead with the Signature Cashback Card, and send me the home-loan documents via email.
    Agent: Done. You’ll receive an email shortly. Is there anything else I can help you with?
    Customer: No, that’s all for today—thank you!
    """

    complex_transcript = """
    Agent: Good morning, thank you for calling Maple Grove Bank. This is Rahul speaking—how may I assist you today?
    Customer: Hi Rahul, I’ve been reviewing my financial goals for the next five years and want to discuss a mix of savings, investments, and insurance.
    Agent: Absolutely. Would you like to start with your current cash savings or jump straight into investment products?
    Customer: Let’s begin with savings: I’d like to open a high-yield savings account with at least ₹1 lakh to start, and then set up an automatic top-up of ₹10,000 each month.
    Agent: Great choice. We have our “Plus Savings” account at 4.2% APY. Next, investments—are you looking at mutual funds, stocks, or retirement plans?
    Customer: I’m particularly interested in tax-saving ELSS mutual funds and a more conservative retirement pension plan. Also, could you explain your term insurance offerings?
    Agent: Sure—our ELSS options include Fund A (equity-heavy) and Fund B (balanced). For term cover, we have 20-year plans up to ₹50 lakhs. Any preference?
    Customer: I want a balanced ELSS with a 3-year lock-in, and term insurance of ₹30 lakhs for 25 years. After that, I may need advice on buying a second home—so let’s also discuss mortgage pre-approval.
    Agent: Understood. For a ₹30 lakh home loan, current interest rates start at 6.9%. We can pre-approve you based on your income. Shall I proceed?
    Customer: Yes, please initiate the home-loan pre-approval. And lastly, I’d like to apply for a debit card with no annual fee and a co-branded credit card offering travel rewards.
    Agent: Certainly—our “Freedom” debit card has no fee, and the “SkyMiles” credit card gives 2 airline miles per ₹100 spent. Would you like to complete those applications now?
    Customer: Yes, go ahead with both. Also, can you set up a quarterly portfolio review call with a financial advisor?
    Agent: Absolutely. I’ll schedule a review every three months starting next quarter. You’ll get email confirmations shortly.
    Customer: Perfect—that covers all my needs. Thanks for your help!
    Agent: My pleasure! Have a great day and feel free to call back anytime.
    """

    # premise_input = "personA is on the stage giving a speech."
    # hypothesis_input = "personA is using a microphone."
    # prediction, _ = predict_fin_nli(premise=premise_input, hypothesis=hypothesis_input, model_path='gnn_model_checkpoint.pt')
    # print("Prediction:", prediction)
    # print('Final layer logits:', _)

    ################################

    # start = time()
    # results = extract_sentences_by_intent(
    #     complex_transcript,
    #     intent="customer tells about own financial condition",#"customer states specific financial product requests and planning preferences",
    #     #"agent provides assistance", #"customer states their financial needs",
    #     threshold=0.60,
    #     top_k=10,
    #     convo_focus='customer'
    # )
    # end = time()
    #
    # logging.info('Prediction Done in {:.2f}sec'.format(end - start))
    #
    # for sentence, score in results:
    #     print(f"{score:.2f} → {sentence}")

	#from models import *
	#from preprocess_data import *
	from utils import extract_sentences_by_intent, train_model_with_chkpt, batch_predict_and_save
	from time import time
	import logging
	from config import *





	if __name__ == '__main__':
	# train_model_with_chkpt(epochs=5, batch_size=16, lr=2e-3,
	# save_model=True,
	# save_path='gnn_model_checkpoint.pt',
	# resume=True)

	from transformers import BertTokenizer
	from preprocess_data import SentenceDataset
	from models import SentenceExtractionModel
	from utils import train_sentence_extractor

	tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
	dataset = SentenceDataset("data/Fin_ExBERT_train_val_data.xlsx", tokenizer)


	model = SentenceExtractionModel(
	base_model_name=MODEL_NAME,
	backbone='finexbert'
	)

	# train_sentence_extractor(
	# model,
	# dataset,
	# output_dir="checkpoints/sentence_extractor",
	# val_split=0.3,
	# epochs=10,
	# batch_size=16,
	# lr=3e-4,
	# device=DEVICE,
	# unfreeze_after_epoch=4
	# )
	#
	# from transformers import BertTokenizer
	# from models import SentenceExtractionModel
	# from utils import demo_on_random_val
	#
	# tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
	# model = SentenceExtractionModel(
	# base_model_name=MODEL_NAME,
	# backbone='finexbert'
	# )
	#
	# demo_on_random_val(
	# model,
	# tokenizer,
	# excel_path="data/Fin_ExBERT_test_set.xlsx",
	# ckpt_path="checkpoints/sentence_extractor/best_model.pth",
	# device="cuda", # or "cpu"
	# temperature=1,
	# )

	batch_predict_and_save(
	model,
	tokenizer,
	excel_path="data/Fin_ExBERT_test_set.xlsx",
	ckpt_path="checkpoints/sentence_extractor/best_model.pth",
	output_path="results/predictions_sample200.xlsx",
	n_samples=200,
	temperature=1.0,
	device="cuda"
	)

	sample_transcript = """
	Agent: Hello, thank you for calling Acme Financial Services. My name is Priya. How can I help you today?
	Customer: Hi Priya, I’m considering opening a new savings account with you.
	Agent: Absolutely—our savings account offers 4% interest per annum. Do you have a balance in mind?
	Customer: Yes, I’d like to deposit ₹50,000 initially, and then I’m interested in investing another ₹2 lakh in mutual funds over the next month.
	Agent: Great, we have several mutual fund options. Are you more growth-oriented or looking for steady income?
	Customer: I want to focus on growth. Also, could you tell me about your home loan rates? I may need a ₹30 lakh mortgage in the next six months.
	Agent: Certainly—we currently offer home loan rates starting at 6.8%. Do you already own property or are you planning to buy?
	Customer: Planning to buy. Finally, I’d like to apply for a credit card with a high cashback—maybe one that gives 2% on all spends.
	Agent: We have a Platinum Cashback Card at 1.5%, and our Signature Cashback Card at 2%. Would you like me to initiate the application?
	Customer: Yes please, go ahead with the Signature Cashback Card, and send me the home-loan documents via email.
	Agent: Done. You’ll receive an email shortly. Is there anything else I can help you with?
	Customer: No, that’s all for today—thank you!
	"""

	complex_transcript = """
	Agent: Good morning, thank you for calling Maple Grove Bank. This is Rahul speaking—how may I assist you today?
	Customer: Hi Rahul, I’ve been reviewing my financial goals for the next five years and want to discuss a mix of savings, investments, and insurance.
	Agent: Absolutely. Would you like to start with your current cash savings or jump straight into investment products?
	Customer: Let’s begin with savings: I’d like to open a high-yield savings account with at least ₹1 lakh to start, and then set up an automatic top-up of ₹10,000 each month.
	Agent: Great choice. We have our “Plus Savings” account at 4.2% APY. Next, investments—are you looking at mutual funds, stocks, or retirement plans?
	Customer: I’m particularly interested in tax-saving ELSS mutual funds and a more conservative retirement pension plan. Also, could you explain your term insurance offerings?
	Agent: Sure—our ELSS options include Fund A (equity-heavy) and Fund B (balanced). For term cover, we have 20-year plans up to ₹50 lakhs. Any preference?
	Customer: I want a balanced ELSS with a 3-year lock-in, and term insurance of ₹30 lakhs for 25 years. After that, I may need advice on buying a second home—so let’s also discuss mortgage pre-approval.
	Agent: Understood. For a ₹30 lakh home loan, current interest rates start at 6.9%. We can pre-approve you based on your income. Shall I proceed?
	Customer: Yes, please initiate the home-loan pre-approval. And lastly, I’d like to apply for a debit card with no annual fee and a co-branded credit card offering travel rewards.
	Agent: Certainly—our “Freedom” debit card has no fee, and the “SkyMiles” credit card gives 2 airline miles per ₹100 spent. Would you like to complete those applications now?
	Customer: Yes, go ahead with both. Also, can you set up a quarterly portfolio review call with a financial advisor?
	Agent: Absolutely. I’ll schedule a review every three months starting next quarter. You’ll get email confirmations shortly.
	Customer: Perfect—that covers all my needs. Thanks for your help!
	Agent: My pleasure! Have a great day and feel free to call back anytime.
	"""

	# premise_input = "personA is on the stage giving a speech."
	# hypothesis_input = "personA is using a microphone."
	# prediction, _ = predict_fin_nli(premise=premise_input, hypothesis=hypothesis_input, model_path='gnn_model_checkpoint.pt')
	# print("Prediction:", prediction)
	# print('Final layer logits:', _)

	################################

	# start = time()
	# results = extract_sentences_by_intent(
	# complex_transcript,
	# intent="customer tells about own financial condition",#"customer states specific financial product requests and planning preferences",
	# #"agent provides assistance", #"customer states their financial needs",
	# threshold=0.60,
	# top_k=10,
	# convo_focus='customer'
	# )
	# end = time()
	#
	# logging.info('Prediction Done in {:.2f}sec'.format(end - start))
	#
	# for sentence, score in results:
	# print(f"{score:.2f} → {sentence}")