import datetime
import joblib
from math import radians

import pandas as pd
import numpy as np
import plotly.express as px
from matplotlib import pyplot
import warnings

import hopsworks
from sml import synthetic_data

import streamlit as st

import folium
from streamlit_folium import st_folium
import json

start_date = (datetime.datetime.now() - datetime.timedelta(hours=200)) 
end_date = (datetime.datetime.now()) 

synthetic_data.set_random_seed(12345)
credit_cards = [cc["cc_num"] for cc in synthetic_data.generate_list_credit_card_numbers()]
lat = 0
long = 0

warnings.filterwarnings("ignore")

project = hopsworks.login()
fs = project.get_feature_store()

@st.cache(allow_output_mutation=True, suppress_st_warning=True)
def retrieve_dataset(fv, start_date, end_date):
    st.write(36 * "-")
    print_fancy_header('\n💾 Dataset Retrieving...')
    batch_data = fv.get_batch_data(start_time = start_date, end_time = end_date)
    batch_data.drop(["tid", "cc_num", "datetime"], axis = 1, inplace=True)
    return batch_data


@st.cache(suppress_st_warning=True, allow_output_mutation=True)
def get_feature_view():
    fv = fs.get_feature_view("cc_trans_fraud", 1)
    return fv


@st.cache(allow_output_mutation=True,suppress_st_warning=True)
def get_model(project = project):
    mr = project.get_model_registry()
    model = mr.get_model("cc_fraud", version = 1)
    model_dir = model.download()
    return joblib.load(model_dir + "/cc_fraud_model.pkl")

def explore_data(batch_data):
    st.write(36 * "-")
    print_fancy_header('\n👁 Data Exploration...')
    labels = ["Suspected of Fraud", "Not Suspected of Fraud"]
    unique, counts = np.unique(batch_data.fraud.values, return_counts=True)
    values = counts.tolist()

    def plot_pie(values, labels):
        fig = px.pie(values=values, names=labels, title='Distribution of predicted fraud transactions')
        return fig

    fig1 = plot_pie(values, labels)
    st.plotly_chart(fig1)


def print_fancy_header(text, font_size=24):
    res = f'<span style="color:#ff5f27; font-size: {font_size}px;">{text}</span>'
    st.markdown(res, unsafe_allow_html=True)

def transform_preds(predictions):
    return ['Fraud' if pred == 1 else 'Not Fraud' for pred in predictions]    

progress_bar = st.sidebar.header('⚙️ Working Progress')
progress_bar = st.sidebar.progress(0)
st.title('🆘 Fraud transactions detection 🆘')

st.write(36 * "-")
print_fancy_header('\n📡 Connecting to Hopsworks Feature Store...')

st.write(36 * "-")
print_fancy_header('\n🤖 Connecting to Model Registry on Hopsworks...')
model = get_model(project)
st.write(model)
st.write("✅ Connected!")

progress_bar.progress(40)

st.write(36 * "-")
print_fancy_header('\n✨ Fetch batch data and predict')
fv = get_feature_view()


if st.button('📊 Make a prediction'):
    batch_data = retrieve_dataset(fv, start_date, end_date)
    st.write("✅ Retrieved!")
    progress_bar.progress(55)
    predictions = model.predict(batch_data)
    predictions = transform_preds(predictions)
    batch_data_to_explore = batch_data.copy()
    batch_data_to_explore['fraud'] = predictions
    explore_data(batch_data_to_explore)

st.button("Re-run")