import gradio as gr
from src.utils import LLMHandler, initialize_newsletter, integrate_personalized_text, build_context, build_prompt
from src.utils_api import get_recommendations 
import yaml
import logging
import argparse
import os
import tempfile


# logging.basicConfig(filename='logs/app.log', encoding='utf-8', level=logging.info)
logging.basicConfig(level=logging.INFO)


def main():

    # get arguments with argparse
    parser = argparse.ArgumentParser(description='Newsletter Generator')
    parser.add_argument('--config-file', type=str, default='./config/config.yaml', help='Path to the configuration file.')
    args = parser.parse_args()

    logging.info("Starting the Newsletter Generator app...")

    # Load configuration from YAML file
    logging.info("Loading configuration from config.yaml...")
    with open(args.config_file, "r") as file:
        config = yaml.safe_load(file)

        # setup
        #try:
        #    os.environ["RECOMMENDER_URL"] = config['recommender_api']['base_url']
        #    os.environ["RECOMMENDER_KEY"] = config['recommender_api']['key']
        #    os.environ["OPENAI_KEY"] = config['llm']['api_key']
        #except:
        #    pass


        llm_settings = config['llm']
        config['llm']['api_key'] = os.environ["OPENAI_KEY"]
        newsletter_meta_info = config['newsletter']
        
        logging.debug(f"Configuration loaded: {config}")

    # Initialize the LLM handler
    llm_handler = LLMHandler(**llm_settings)
    logging.info(f"LLM handler initialized with the following settings: {config['llm']}")


    # Define the function to generate the newsletter using the OpenAI API
    def generate_newsletter(
            customer_id,  
            model_name,
            temperature,
            max_tokens,
            system_message, 
            textual_preferences,
            progress=gr.Progress()
            ):


        # get recommendations
        progress(0.1, "Fetching Client History...")
        logging.info("Getting recommendations...")
        customer_info, recommendations, transactions = get_recommendations(
            customer_id,
            max_recs=newsletter_meta_info['max_recommendations'], 
            max_transactions=newsletter_meta_info['max_recents_items']) 
        logging.debug(f"Recommendations: {recommendations}")
        logging.debug(f"Transactions: {transactions}")
        print("cusomter info", customer_info)


        # Load the html template and replace the placeholders for images with the actual content
        logging.info("Initializing newsletter template...")
        progress(0.5, "Initializing personalized content...")
        newsletter_text = initialize_newsletter(newsletter_meta_info, transactions, recommendations)

        # Build context from the user preferences, the recommendations and the transactions
        context = build_context(
            recommendations, 
            transactions, 
            textual_preferences, 
            customer_info)
        logging.info(f"Context: {context}")

        # Build the prompt for the LLM
        progress(0.7, "Generating personalized content...")
        prompt = build_prompt(context)
        logging.info(f"Prompt: {prompt}")
        

        # Generate the newsletter
        sections = llm_handler.generate( 
                        prompt, 
                        model_name, 
                        temperature, 
                        max_tokens, 
                        system_message)
        logging.info(f"Sections: {sections}")


        # Intergrate personalized text
        logging.info("Integrating personalized text...")
        newsletter_text = integrate_personalized_text(newsletter_text, customer_info, sections)

        # Save HTML to a temporary file for download
        with tempfile.NamedTemporaryFile(delete=False, suffix=".html") as temp_file:
            temp_file.write(newsletter_text.encode("utf-8"))
            temp_file_path = temp_file.name
        progress(1.0)
        return newsletter_text, temp_file_path

    logging.info("Creating interface...")

    with gr.Blocks() as demo:
        # Header Section
        gr.Markdown("## AI-Powered Newsletter for Fashion Brands", elem_id="header")
        
        # Input Section
        with gr.Row():
            customer_id = gr.Dropdown(
                label="Customer ID", 
                #value="04a183a27a6877e560e1025216d0a3b40d88668c68366da17edfb18ed89c574c", 
                interactive=True,
                choices=[
                    ("customer 1", "04a183a27a6877e560e1025216d0a3b40d88668c68366da17edfb18ed89c574c"),
                    ("customer 2", "1abaca5cd299000720538c70ba2ed246db6731bce924b5b4ca81770a47842656"),
                    ("customer 3", "1741b0d1b2c29994084b7312001c1b11ab8b112b3fd05ac765f4d232afdc4eaf")
                ]
            )
        
        with gr.Row():
            textual_preferences = gr.Textbox(
                label="Newsletter Preferences", 
                placeholder="Enter rich newsletter preferences."
            )
        
        # Advanced Settings
        with gr.Accordion("⚙️ Advanced Settings", open=False):
            with gr.Row():
                model_name = gr.Dropdown(
                    label="LLM Model", 
                    choices=["gpt-3.5-turbo", "gpt-4o"], 
                    value=llm_handler.model_name
                )
                temperature = gr.Slider(
                    label="Temperature", 
                    minimum=0.0, 
                    maximum=1.0, 
                    step=0.05, 
                    value=llm_handler.default_temperature
                )
            with gr.Row():
                max_tokens = gr.Number(
                    label="Max Tokens", 
                    value=llm_handler.default_max_tokens, 
                    precision=0
                )
                system_message = gr.Textbox(
                    label="System Message", 
                    placeholder="Enter a custom system message (optional).", 
                    value=llm_handler.default_system_message,
                    visible=False
                )
        
        # User Context (Hidden by Default)
        with gr.Accordion("🧑‍💻 User Context", open=False, visible=False):
            pass  # Placeholder for future user context integration.
        
        # Output Section
        with gr.Row():
            generate_button = gr.Button("Generate Personalized Newsletter", variant="primary")
            download = gr.DownloadButton("Download")
        
        newsletter_output = gr.HTML(
            label="Generated Newsletter", 
            value="<br><br><br><br><br>", 
            min_height=500
        )
        
        # Event Binding
        generate_button.click(
            fn=generate_newsletter, 
            inputs=[
                customer_id,
                model_name,
                temperature,
                max_tokens,
                system_message,
                textual_preferences
            ], 
            outputs=[newsletter_output, download]
        )

    # Launch App
    demo.queue().launch(
        share=config['app']['share'], 
        server_port=config['app']['server_port']
    )


    # Gradio interface for the app
"""    logging.info("Creating interface...")
    with gr.Blocks() as demo:
        gr.Markdown("### Newsletter Generator")
        
        customer_id = gr.Textbox(label="Client ID", value="04a183a27a6877e560e1025216d0a3b40d88668c68366da17edfb18ed89c574c")
        textual_preferences = gr.Textbox(label="Newsletter preferences", placeholder="The newsletter should be catchy.")
        
        # llm_preferences = gr.Textbox(label="LLM Preferences", placeholder="Enter LLM preferences.", visible=False)
        # create an openable block for the llm preferences
        with gr.Accordion("LLM Preferences", open=False):
            model_name = gr.Dropdown(label="Model Name", choices=["gpt-3.5-turbo", "gpt-4o"], value=llm_handler.model_name)
            temperature = gr.Slider(label="Temperature", minimum=0.0, maximum=1.0, step=0.05, value=llm_handler.default_temperature)
            max_tokens = gr.Number(label="Max Tokens", value=llm_handler.default_max_tokens)
            system_message = gr.Textbox(label="System Message", placeholder="Enter the system message or Leave Blank.", value=llm_handler.default_system_message)
        
        with gr.Accordion("User Context", open=False, visible=False):
            # get profiled user context
            pass
        
        generate_button = gr.Button("Generate Newsletter")
        
        # create a button to open the  newsletter in a new tab
        download = gr.DownloadButton(label="Download Newsletter")
 
        newsletter_output = gr.HTML(label="Generated Newsletter", min_height="500", value="<br><br><br><br><br>")
        
        generate_button.click(
            fn=generate_newsletter, 
            inputs=[
                customer_id,
                # llm preferences
                model_name,
                temperature,
                max_tokens,
                system_message, 
                # newsletter preferences
                textual_preferences], 
            outputs=[newsletter_output, download]
        )
    
    demo.queue().launch(share=config['app']['share'], server_port=config['app']['server_port'])"""

if __name__ == "__main__":
    main()