import gradio as gr
import pandas as pd
from sklearn.metrics import classification_report

def timestamp_wise_evaluation(anomalies_cases, anomalies_news, threshold):
    print(f"Classification report for threshold {threshold} (timestamp-wise evaluation):")
    print(classification_report(anomalies_cases, anomalies_news))

def tolerance_based_evaluation(anomalies_cases, anomalies_news, cases_df, news_df, threshold):
    Tp = 0
    Fp = 0
    Fn = 0
    Tn = 0
    for i in range(len(news_df)):
        news_an = news_df.iloc[i][anomalies_news]
        if news_an == 1:
            if i == len(news_df) - 1:
                if cases_df.iloc[i][anomalies_cases] == 1:
                    Tp += 1
                else:
                    Fp += 1
            elif i == len(news_df) - 2: 
                if cases_df.iloc[i][anomalies_cases] == 1 or cases_df.iloc[i+1][anomalies_cases] == 1:
                    Tp += 1
                else:
                    Fp += 1
            else:
                if cases_df.iloc[i][anomalies_cases] == 1 or cases_df.iloc[i+1][anomalies_cases] == 1 or cases_df.iloc[i+2][anomalies_cases] == 1:
                    Tp += 1
                else:
                    Fp += 1
        else:
            if i == len(news_df) - 1:
                if cases_df.iloc[i][anomalies_cases] == 1:
                    Fn += 1
                else:
                    Tn += 1
            elif i == len(news_df) - 2: 
                if cases_df.iloc[i][anomalies_cases] == 1:
                    Fn += 1
                else:
                    Tn += 1
            else:
                if cases_df.iloc[i][anomalies_cases] == 1:
                    Fn += 1
                else:
                    Tn += 1
    print(f"Tolerance-based evaluation for method {threshold}:")
    print(f"True Positives: {Tp}, False Positives: {Fp}, False Negatives: {Fn}, True Negatives: {Tn}")
    precision = Tp / (Tp + Fp) 
    recall = Tp / (Tp + Fn)
    f1 = 2 * (precision * recall) / (precision + recall)
    print(f"Precision: {precision}, Recall: {recall}, F1: {f1}")


def prepare_time_series_dataframe(df):
    """Prepare dataframe for time series analysis by setting datetime index and renaming columns"""
    df.set_index(df.columns[0], inplace=True)
    
    try:
        df.index = pd.to_datetime(df.index)
    except ValueError:
        raise ValueError("The first column of the CSV file must be a datetime column.")
    
    df.rename(columns={df.columns[0]: "news"}, inplace=True)
    return df

def update_controls(method):
    """
    Updates the interactivity of control elements based on the selected method.
    
    Args:
        method (str): The selected anomaly detection method
    
    Returns:
        dict: Update configuration for Gradio components
    """
    is_lstm = method == "LSTM"
    return [
        gr.update(interactive=is_lstm),
        gr.update(interactive=is_lstm)
    ]