import matplotlib.pyplot as plt
import pandas as pd
from .utils import undo_hyperlink

def plot_avg_correlation(df1, df2):
    """
    Plots the "average" column for each unique model that appears in both dataframes.
    
    Parameters:
    - df1: pandas DataFrame containing columns "model" and "average".
    - df2: pandas DataFrame containing columns "model" and "average".
    """
    # Identify the unique models that appear in both DataFrames
    common_models = pd.Series(list(set(df1['model']) & set(df2['model'])))
    
    # Set up the plot
    plt.figure(figsize=(13, 6), constrained_layout=True)

    # axes from 0 to 1 for x and y 
    plt.xlim(0.475, 0.8)
    plt.ylim(0.475, 0.8)

    # larger font (16)
    plt.rcParams.update({'font.size': 12, 'axes.labelsize': 14,'axes.titlesize': 14})
    # plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)
    # plt.tight_layout()
    # plt.margins(0,0)

    for model in common_models:
        # Filter data for the current model
        df1_model_data = df1[df1['model'] == model]['average'].values
        df2_model_data = df2[df2['model'] == model]['average'].values
        
        # Plotting
        plt.scatter(df1_model_data, df2_model_data, label=model)
        m_name = undo_hyperlink(model)
        if m_name == "No text found":
            m_name = "Random"
        # Add text above each point like 
        # plt.text(x[i] + 0.1, y[i] + 0.1, label, ha='left', va='bottom')
        plt.text(df1_model_data - .005, df2_model_data, m_name, horizontalalignment='right', verticalalignment='center')

    # add correlation line to scatter plot
    # first, compute correlation
    corr = df1['average'].corr(df2['average'])
    # add correlation line based on corr
    


    plt.xlabel('HERM Eval. Set Avg.', fontsize=16)
    plt.ylabel('Pref. Test Sets Avg.', fontsize=16)
    # plt.legend(title='Model', bbox_to_anchor=(1.05, 1), loc='upper left')
    return plt