import json
import os
from datetime import datetime

import gradio as gr
import pandas as pd

from envs import API, EVAL_REQUESTS_PATH, TOKEN, QUEUE_REPO



custom_css = """
@import url('https://fonts.googleapis.com/css2?family=Vazirmatn&display=swap');
body, .gradio-container, .gr-button, .gr-input, .gr-slider, .gr-dropdown, .gr-markdown {
    font-family: 'Vazirmatn', sans-serif !important;
}
.markdown-text {
    font-size: 16px !important;
}
#models-to-add-text {
    font-size: 18px !important;
}
#citation-button span {
    font-size: 16px !important;
}
#citation-button textarea {
    font-size: 16px !important;
}
#citation-button > label > button {
    margin: 6px;
    transform: scale(1.3);
}
#leaderboard-table {
    margin-top: 15px;
    text-align: center;
}
#leaderboard-table,
#leaderboard-table th,
#leaderboard-table td {
    text-align: center;
    vertical-align: middle;
    border-collapse: collapse;
}
#leaderboard-table td:first-child,
#leaderboard-table th:first-child {
    text-align: left;
    max-width: 600px;
}
table > thead {
    white-space: normal;
}
table > thead th,
table > tbody td {
    text-align: center;
    vertical-align: middle;
}
table > tbody td:first-child {
    text-align: left;
    max-width: 600px;
}
#leaderboard-table-lite {
    margin-top: 15px;
}
#search-bar-table-box > div:first-child {
    background: none;
    border: none;
}
#search-bar {
    padding: 0px;
}
.tab-buttons button {
    font-size: 20px;
}
#scale-logo {
    border-style: none !important;
    box-shadow: none;
    display: block;
    margin-left: auto;
    margin-right: auto;
    max-width: 600px;
}
#scale-logo .download {
    display: none;
}
#filter_type {
    border: 0;
    padding-left: 0;
    padding-top: 0;
}
#filter_type label {
    display: flex;
}
#filter_type label > span {
    margin-top: var(--spacing-lg);
    margin-right: 0.5em;
}
#filter_type label > .wrap {
    width: 103px;
}
#filter_type label > .wrap .wrap-inner {  
    padding: 2px;
}
#filter_type label > .wrap .wrap-inner input {
    width: 1px;
}
#filter-columns-type {
    border: 0;
    padding: 0.5;
}
#filter-columns-size {
    border: 0;
    padding: 0.5;
}
#box-filter > .form {
    border: 0;
}
"""


ABOUT_TEXT = f"""
# Persian Text Embedding Benchmark (v1.0.0)
"""


SUBMIT_TEXT = """## Submitting a Model for Evaluation

> To submit your open-source model for evaluation, follow these steps:
> 
> 1. **Ensure your model is on Hugging Face**: Your model must be publicly available on [Hugging Face](https://huggingface.co/).
> 
> 2. **Submit Request**: Send a request with your model's Hugging Face identifier.
> 
> 3. **Manual Queue**: Please note that the evaluation process is currently handled manually. Submissions will be queued and processed as soon as possible.
> 
> 4. **Results**: Once the evaluation is complete, your model’s results will be updated on the leaderboard.
> 
> We appreciate your patience and contributions to the Persian LM ecosystem!
"""


PART_LOGO = """
<img src="https://avatars.githubusercontent.com/u/39557177?v=4" style="width:30%;display:block;margin-left:auto;margin-right:auto"> 
<h1 style="font-size: 28px; margin-bottom: 2px;">Part DP AI</h1>
"""


def load_jsonl(input_file):
    data = []
    with open(input_file, 'r') as f:
        for line in f:
            data.append(json.loads(line))
    return data


def jsonl_to_dataframe(input_file):
    data = load_jsonl(input_file)
    return pd.DataFrame(data)


def sort_dataframe_by_column(df, column_name):
    if column_name not in df.columns:
        raise ValueError(f"Column '{column_name}' does not exist in the DataFrame.")
    return df.sort_values(by=column_name, ascending=False).reset_index(drop=True)


def add_average_column_to_df(df,columns_to_average, index=3, average_column_name="Average Accuracy"):
    average_column = df[columns_to_average].mean(axis=1)
    df.insert(index, average_column_name, average_column)
    return df


def model_hyperlink(link, model_name):
    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'


def make_clickable_model(model_name):
    link = f"https://huggingface.co/{model_name}"
    return model_hyperlink(link, model_name)


def center_align_markdown(text):
    return f'<div align="center">{text}</div>'


def apply_markdown_format_for_columns(df, model_column_name):
    columns = list(df.columns)
    df[model_column_name] = df[model_column_name].apply(make_clickable_model)
    # for column in columns:
    #     if column != model_column_name:
    #         df[column] = df[column].apply(center_align_markdown)
    return df


def submit(model_name, model_id, contact_email):
    if model_name == "" or model_id == "" or contact_email == "":
        gr.Info("Please fill all the fields")
        return


    try:
        user_name = ""
        if "/" in model_id:
            user_name = model_id.split("/")[0]
            model_path = model_id.split("/")[1]

        eval_entry = {
            "model_name": model_name,
            "model_id": model_id,
            "contact_email": contact_email,
        }

        # Get the current timestamp to add to the filename
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")

        OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
        os.makedirs(OUT_DIR, exist_ok=True)

        # Add the timestamp to the filename
        out_path = f"{OUT_DIR}/{user_name}_{model_path}_{timestamp}.json"

        with open(out_path, "w") as f:
            f.write(json.dumps(eval_entry))

        print("Uploading eval file")
        API.upload_file(
            path_or_fileobj=out_path,
            path_in_repo=out_path.split("eval-queue/")[1],
            repo_id=QUEUE_REPO,
            repo_type="dataset",
            commit_message=f"Add {model_name} to eval queue",
        )

        gr.Info("Successfully submitted", duration=10)
        # Remove the local file
        os.remove(out_path)
    except Exception as e:
        gr.Error(f"Error submitting the model: {e}")