import gradio as gr
from huggingface_hub import from_pretrained_keras
import pandas as pd
import numpy as np
import json

f = open('scaler.json')
scaler = json.load(f)

def normalize_data(data):
    df_test_value = (data - scaler["mean"]) / scaler["std"]
    return df_test_value

def plot_test_data(df_test_value):
    fig, ax = plt.subplots()
    df_test_value.plot(legend=False, ax=ax)
    return fig

def get_anomalies(df_test_value):
    # Create sequences from test values.
    x_test = create_sequences(df_test_value.values)
    model = from_pretrained_keras("remeajayi/timeseries-anomaly-detection")

    # Get test MAE loss.
    x_test_pred = model.predict(x_test)
    test_mae_loss = np.mean(np.abs(x_test_pred - x_test), axis=1)
    test_mae_loss = test_mae_loss.reshape((-1))

    # Detect all the samples which are anomalies.
    anomalies = test_mae_loss > threshold
    return anomalies

def plot_anomalies(df_test_value, data, anomalies):
    # data i is an anomaly if samples [(i - timesteps + 1) to (i)] are anomalies
    anomalous_data_indices = []
    for data_idx in range(TIME_STEPS - 1, len(df_test_value) - TIME_STEPS + 1):
        if np.all(anomalies[data_idx - TIME_STEPS + 1 : data_idx]):
            anomalous_data_indices.append(data_idx)
    df_subset = data.iloc[anomalous_data_indices]
    fig, ax = plt.subplots()
    data.plot(legend=False, ax=ax)
    df_subset.plot(legend=False, ax=ax, color="r")
    return fig
                                      
def master(file):
    # read file
    data = pd.read_csv(file, parse_dates=True, index_col="timestamp")
    df_test_value = normalize_data(data)
    # plot input test data
    plot1 = plot_test_data(df_test_value)
    # predict
    anomalies = get_anomalies(df_test_value)
    #plot anomalous data points
    plot2 = plot_anomalies(df_test_value, data, anomalies)
    return plot2

iface = gr.Interface(master,
gr.inputs.File(label="csv file"),
outputs=['plot'], 
examples=["art_daily_jumpsup.csv"], title="Anomaly detection of timeseries data",
description = "Anomaly detection of timeseries data.",
        article = "Author: <a href=\"https://www.linkedin.com/in/olohireme-ajayi/\">Reme Ajayi</a>"
)


iface.launch()