import pandas as pd 
import gradio as gr
from io import StringIO


# Creating a sample dataframe
df = pd.DataFrame({
    "Rank" : [1, 2, 3, 4, 5], 
    "Detector" : [
       """ChatGPT_QA\n(Antoun et al., 2023)\n<a href='https://www.google.de' style="color:blue">Paper</a>""",
       """XMLMR_ChatGPT\n(Guo et al., 2023)\n<a href='https://www.google.de' style="color:blue">Paper</a>""",
       """LLMDET\n(Liu et al., 2019)\n<a href='https://www.google.de' style="color:blue">Paper</a>""",
       """Radar_Vicuna7B\n(Hu et al, 2023)\n<a href='https://www.google.de' style="color:blue">Paper</a>""",
       """GPTZero\n(Numo et al.)\n<a href='https://www.google.de' style="color:blue">WebApp</a>""",
      ], 
    "All Gen. [F1]" : [0.723, 0.563, 0.736, 0.635, 0.464], 
    "Llama-2 7B Gen. [F1]" : [0.719, 0.636, 0.622, 0.722, 0.777], 
    "GPT-4 Gen. [F1]" : [0.673, 0.435, 0.362, 0.232, 0.562],
    "GPT-3 Gen. [F1]" : [0.374, 0.335, 0.232, 0.632, 0.533],
    "Falcon 7B Gen. [F1]" : [0.445, 0.454, 0.646, 0.665, 0.464]
}) 


def process_csv_text(temp_file):
    if isinstance(temp_file, str):
      df = pd.read_csv(StringIO(temp_file), parse_dates=["Start date", "End date"])
    else:
      df = pd.read_csv(temp_file.name, parse_dates=["Start date", "End date"])
    print(df)
    return df
  
numeric_cols = df.select_dtypes(include='number').columns
traget_cols = ['All Gen. [F1]', 'Llama-2 7B Gen. [F1]', 'GPT-4 Gen. [F1]']

# Applying style to highlight the maximum value in each row
#styler = df.style.highlight_max(color = 'lightgreen', axis = 0)
styler = df.style.set_properties(**{'text-align': 'center'},subset=['Detector'])\
                  .highlight_max(color='lightgreen', axis=0, subset=traget_cols)
title = "Test"
desc = "Test gradio"
# Displaying the styled dataframe in Gradio
with gr.Blocks() as demo:
    

    gr.Markdown(
    """
    <div align="center">
    <center><h1>BUST: Benchmark for the evaluation of system detectors of LLM-Generated Text</h1></center>
    <center> Welcome to BUST, a comprehensive benchmark for evaluating synthetic text detectors, focusing on their \
      effectiveness against outputs from various Large Language Models (LLMs). BUST evaluates detectors using a wide \
      range of metrics including linguistic features, readability, and writer attitudes, aiming to identify spurious \
      signals that may influence detection. The benchmark not only ranks detectors but also analyzes their performance \
      correlations with specific metrics and provides insights into biases and robustness against different LLM outputs. \
      BUST is designed as a dynamic resource, continuously updated to stay relevant in the rapidly evolving field of \
      LLM-generated content detection.</center>
      <p>&nbsp;</p>
      <p>&nbsp;</p>
      </div>
    """)
    gr.DataFrame(styler, line_breaks=True, datatype="markdown")



    # Download testset
    gr.Markdown(
    """
    <center><h1>Download test dataset</h1></center>
    <center>...</center>
    """)


    # Upload table
    gr.Markdown(
    """
    <center><h1>Upload predictions</h1></center>
    <center>Upload your predictions. Please make sure that the file is in the right format (.csv) and contains the "id" and "prediction" columns.</center>
    """)
  
    upload_button = gr.UploadButton(label="Upload Predictions", file_types = ['.csv'], file_count = "single")    
    table = gr.Dataframe(headers=["Detector", "All Gen. [F1]", "Llama-2 7B Gen. [F1]", "GPT-4 Gen. [F1]","GPT-3 Gen. [F1]", "Falcon 7B Gen. [F1]"], type="pandas", col_count=6)

    
    upload_button.upload(fn=process_csv_text, inputs=upload_button, outputs=table, api_name="upload_csv")

    
demo.launch()