import os
import json
import csv
import datetime
from email.utils import parseaddr

import gradio as gr
import pandas as pd
import numpy as np

from datasets import load_dataset
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import HfApi

from scorer import instruction_scorer
from content import format_error, format_warning, format_log, TITLE, INTRODUCTION_TEXT, SUBMISSION_TEXT, CITATION_BUTTON_LABEL, CITATION_BUTTON_TEXT, model_hyperlink

TOKEN = os.environ.get("TOKEN", None)
OWNER="ucla-contextual"
ALL_DATASET = f"{OWNER}/contextual_all"
VAL_DATASET = f"{OWNER}/contextual_val"
SUBMISSION_DATASET = f"{OWNER}/submissions_internal"
CONTACT_DATASET = f"{OWNER}/contact_info"
RESULTS_DATASET = f"{OWNER}/results"
LEADERBOARD_PATH = f"{OWNER}/leaderboard"
api = HfApi()

YEAR_VERSION = "2024"

def read_json_file(filepath):
    with open(filepath) as infile:
        data_dict = json.load(infile)
    return data_dict

def save_json_file(filepath, data_dict):
    with open(filepath, "w") as outfile:
        json.dump(data_dict, outfile)
                                
os.makedirs("scored", exist_ok=True)

all_data_files = {"overall": "contextual_all.csv"}
all_dataset = load_dataset(ALL_DATASET, data_files=all_data_files , token=TOKEN, download_mode="force_redownload", ignore_verifications=True)

val_data_files = {"val": "contextual_val.csv"}
val_dataset = load_dataset(VAL_DATASET, data_files=val_data_files , token=TOKEN, download_mode="force_redownload", ignore_verifications=True)

results_data_files = {"overall": "contextual_all_results.csv", "val": "contextual_val_results.csv"}
results = load_dataset(RESULTS_DATASET, data_files=
results_data_files, token=TOKEN, download_mode="force_redownload", ignore_verifications=True)

contacts_data_files = {"contacts": "contacts.csv"}
contact_infos = load_dataset(CONTACT_DATASET, data_files=contacts_data_files, token=TOKEN, download_mode="force_redownload", ignore_verifications=True)

def get_dataframe_from_results(results, split):
    df = results[split].to_pandas()
    df.drop(columns=['URL'], inplace=True)
    df = df.sort_values(by=["All"], ascending=False)
    return df

all_dataset_dataframe = all_dataset["overall"].to_pandas()
val_dataset_dataframe = val_dataset["val"].to_pandas()

contacts_dataframe = contact_infos["contacts"].to_pandas()

val_results_dataframe = get_dataframe_from_results(results=results, split="val")
all_results_dataframe = get_dataframe_from_results(results=results, split="overall")

def restart_space():
    api.restart_space(repo_id=LEADERBOARD_PATH, token=TOKEN)

TYPES = ["markdown", "markdown", "markdown", "number", "number", "number","number", "number", "number", "number", "number", "number"]

def add_new_eval(
    model: str,
    method: str,
    url: str,
    path_to_file: str,
    organisation: str,
    mail: str,
):
    print("printing all inputs:", model, method, url, path_to_file, organisation, mail)

    if len(model)==0:
        print("model none")
        raise gr.Error("Please provide a model name. Field empty!")
    
    if len(method)==0:
        print("method none")
        raise gr.Error("Please provide a method. Field empty!")
    
    if len(organisation)==0:
        print("org none")
        raise gr.Error("Please provide organisation information. Field empty!")
    
    # Very basic email parsing
    _, parsed_mail = parseaddr(mail)
    if not "@" in parsed_mail:
        print("email here")
        raise gr.Error("Please provide a valid email address.")
    

    # Check if the combination model/org already exists and prints a warning message if yes
    if model.lower() in set([m.lower() for m in results["val"]["Model"]]) and organisation.lower() in set([o.lower() for o in results["val"]["Organisation"]]):
        print("model org combo here")
        raise gr.Error("This model has been already submitted.")

    if path_to_file is None:
        print("file missing here")
        raise gr.Error("Please attach a file.")

    tmp_file_output = read_json_file(path_to_file.name)
    
    if len(tmp_file_output.keys())!=1:
        print("file format wrong here")
        raise gr.Error("Submission file format incorrect. Please refer to the format description!")
    
    tmp_output_key = list(tmp_file_output.keys())[0]
    if len(tmp_file_output[tmp_output_key].keys())!=100:
        print("file not 100 here")
        raise gr.Error("File must contain exactly 100 predictions.")
    
    # Save submitted file
    time_atm = datetime.datetime.today()
    api.upload_file(
        repo_id=SUBMISSION_DATASET, 
        path_or_fileobj=path_to_file.name, 
        path_in_repo=f"{organisation}/{model}/{YEAR_VERSION}_raw_{time_atm}.json",
        repo_type="dataset", 
        token=TOKEN
    )

    # Compute score
    file_path = path_to_file.name        
    scores = instruction_scorer(val_dataset_dataframe, file_path , model)

    path_or_fileobj=f"scored/{organisation}_{model}.json"
    save_json_file(path_or_fileobj, scores)

    # Save scored file
    api.upload_file(
        repo_id=SUBMISSION_DATASET, 
        path_or_fileobj=path_or_fileobj,
        path_in_repo=f"{organisation}/{model}/{YEAR_VERSION}_scored_{time_atm}.json", 
        repo_type="dataset", 
        token=TOKEN
    )

    # Actual submission
    eval_entry = {
        "Model": model,
        "Method":method,
        "Organisation": organisation,
        "URL": url,
        "All":scores["average"],
        "Time":scores["time"],
        "Shopping":scores["shopping"],
        "Navigation":scores["navigation-transportation"],
        "Abstract":scores["abstract"],
        "Application Usage":scores["app"],
        "Web Usage":scores["web"],
        "Infographic":scores["infographics"],
        "Miscellaneous Natural Scenes": scores["misc"]
    }

    val_results_dataframe = get_dataframe_from_results(results=results, split="val")
    val_results_dataframe = pd.concat([val_results_dataframe, pd.DataFrame([eval_entry])], ignore_index=True)
    val_results_dataframe.to_csv('contextual_val_results.csv', index=False)

    api.upload_file(
        repo_id=RESULTS_DATASET, 
        path_or_fileobj="contextual_val_results.csv",
        path_in_repo=f"contextual_val_results.csv", 
        repo_type="dataset", 
        token=TOKEN
    )

    contact_info = {
        "Model": model,
        "URL": url,
        "Organisation": organisation,
        "Mail": mail,
    }

    contacts_dataframe = contact_infos["contacts"].to_pandas()
    contacts_dataframe = pd.concat([contacts_dataframe, pd.DataFrame([contact_info])], ignore_index=True)
    contacts_dataframe.to_csv('contacts.csv', index=False)

    api.upload_file(
        repo_id=CONTACT_DATASET, 
        path_or_fileobj="contacts.csv",
        path_in_repo=f"contacts.csv", 
        repo_type="dataset", 
        token=TOKEN
    )

    return format_log(f"Model {model} submitted by {organisation} successfully! \nPlease refresh the val leaderboard, and wait a bit to see the score displayed")


def refresh():
    results_data_files = {"overall": "contextual_all_results.csv", "val": "contextual_val_results.csv"}
    results = load_dataset(RESULTS_DATASET, data_files=
    results_data_files, token=TOKEN, download_mode="force_redownload", ignore_verifications=True)
    val_results_dataframe = get_dataframe_from_results(results=results, split="val")
    all_results_dataframe = get_dataframe_from_results(results=results, split="overall")
    return val_results_dataframe, all_results_dataframe

def upload_file(files):
    file_paths = [file.name for file in files]
    return file_paths


demo = gr.Blocks()
with demo:
    gr.HTML(TITLE)
    # gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Row():
        with gr.Accordion("🧐 Introduction", open=False):
            gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Row():
        with gr.Accordion("🎯 Submission Guidelines", open=False):
            gr.Markdown(SUBMISSION_TEXT, elem_classes="markdown-text")

    with gr.Row():
        with gr.Accordion("📙 Citation", open=False):
            citation_button = gr.TextArea(
                value=CITATION_BUTTON_TEXT,
                label=CITATION_BUTTON_LABEL,
                elem_id="citation-button",
            ) 
    with gr.Tab("Results: Test"):
        leaderboard_table_all = gr.components.Dataframe(
            value=all_results_dataframe, datatype=TYPES, interactive=False,
            column_widths=["20%"] 
        )
    with gr.Tab("Results: Val"):
        leaderboard_table_val = gr.components.Dataframe(
            value=val_results_dataframe, datatype=TYPES, interactive=False,
            column_widths=["20%"] 
        )

    refresh_button = gr.Button("Refresh")
    refresh_button.click(
        refresh,
        inputs=[],
        outputs=[
            leaderboard_table_val,
            leaderboard_table_all,
        ],
    )
    with gr.Accordion("Submit a new model for evaluation"):
        with gr.Row():
            with gr.Column():
                model_name_textbox = gr.Textbox(label="Model name", type='text')
                method_textbox = gr.Textbox(label="Method (LMM or Aug LLM or any other)", type='text')
                url_textbox = gr.Textbox(label="URL to model information", type='text')
            with gr.Column():
                organisation = gr.Textbox(label="Organisation", type='text')
                mail = gr.Textbox(label="Contact email (will be stored privately, & used if there is an issue with your submission)", type='email')
                file_output = gr.File()


        submit_button = gr.Button("Submit Eval")
        submission_result = gr.Markdown()
        submit_button.click(
            add_new_eval,
            [
                model_name_textbox,
                method_textbox,
                url_textbox,
                file_output,
                organisation,
                mail
            ],
            submission_result,
        )

scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=3600)
scheduler.start()
demo.launch(debug=True)