import time  # to simulate a real time data, time loop

import numpy as np  # np mean, np random
import pandas as pd  # read csv, df manipulation
import plotly.express as px  # interactive charts
import streamlit as st  # 🎈 data web app development


# PersistDataset -----
import os
import csv
import gradio as gr
from gradio import inputs, outputs
import huggingface_hub
from huggingface_hub import Repository, hf_hub_download, upload_file
from datetime import datetime

# Dataset and Token links - change awacke1 to your own HF id, and add a HF_TOKEN copy to your repo for write permissions
# This should allow you to save your results to your own Dataset hosted on HF. ---
#DATASET_REPO_URL = "https://huggingface.co/datasets/awacke1/Carddata.csv"
DATASET_REPO_URL = "https://huggingface.co/datasets/" + "awacke1/PrivateASRWithMemory.csv"
#DATASET_REPO_ID = "awacke1/Carddata.csv"
DATASET_REPO_ID = "awacke1/PrivateASRWithMemory.csv"
DATA_FILENAME = "PrivateASRWithMemory.csv"
DATA_FILE = os.path.join("data", DATA_FILENAME)
HF_TOKEN = os.environ.get("HF_TOKEN")

DataText = ""
# ---------------------------------------------

SCRIPT = """
<script>
if (!window.hasBeenRun) {
    window.hasBeenRun = true;
    console.log("should only happen once");
    document.querySelector("button.submit").click();
}
</script>
"""

@st.experimental_singleton
def get_database_session(url):
  # Create a database session object that points to the URL.
  return session
#Clear memo
#Clear all in-memory and on-disk memo caches.

@st.experimental_memo
def fetch_and_clean_data(url):
  # Fetch data from URL here, and then clean it up.
  return data

if st.checkbox("Clear All"):
    # Clear values from *all* memoized functions
    st.experimental_memo.clear()

    try:
        hf_hub_download(
            repo_id=DATASET_REPO_ID,
            filename=DATA_FILENAME,
            cache_dir=DATA_DIRNAME,
            force_filename=DATA_FILENAME
        )
    except:
        print("file not found")
    repo = Repository(local_dir="data", clone_from=DATASET_REPO_URL,use_auth_token=HF_TOKEN)  
#    return session
    print(repo)
    DataText = repo
    
    st.markdown(DataText)
        
    
def generate_html() -> str:
    with open(DATA_FILE) as csvfile:
        reader = csv.DictReader(csvfile)
        rows = []
        for row in reader:
            rows.append(row)
        rows.reverse()
        if len(rows) == 0:
            return "no messages yet"
        else:
            html = "<div class='chatbot'>"
            for row in rows:
                html += "<div>"
                html += f"<span>{row['inputs']}</span>"
                html += f"<span class='outputs'>{row['outputs']}</span>"
                html += "</div>"
            html += "</div>"
            return html
            
            
def store_message(name: str, message: str):
    if name and message:
        with open(DATA_FILE, "a") as csvfile:
            writer = csv.DictWriter(csvfile, fieldnames=["name", "message", "time"])
            writer.writerow(
                {"name": name.strip(), "message": message.strip(), "time": str(datetime.now())}
            )
        # uncomment line below to begin saving - 
        commit_url = repo.push_to_hub()
    return ""            


#st.set_page_config(
#    page_title="Real-Time Data Science Dashboard",
#    page_icon="✅",
#    layout="wide",
#)

# read csv from a github repo
dataset_url = "https://raw.githubusercontent.com/Lexie88rus/bank-marketing-analysis/master/bank.csv"

# read csv from a URL
@st.experimental_memo
def get_data() -> pd.DataFrame:
    return pd.read_csv(dataset_url)

df = get_data()

# dashboard title
st.title("Real-Time / Live Data Science Dashboard")

# top-level filters
job_filter = st.selectbox("Select the Job", pd.unique(df["job"]))

# creating a single-element container
placeholder = st.empty()

# dataframe filter
df = df[df["job"] == job_filter]

# near real-time / live feed simulation
for seconds in range(200):

    df["age_new"] = df["age"] * np.random.choice(range(1, 5))
    df["balance_new"] = df["balance"] * np.random.choice(range(1, 5))

    # creating KPIs
    avg_age = np.mean(df["age_new"])

    count_married = int(
        df[(df["marital"] == "married")]["marital"].count()
        + np.random.choice(range(1, 30))
    )

    balance = np.mean(df["balance_new"])

    with placeholder.container():

        # create three columns
        kpi1, kpi2, kpi3 = st.columns(3)

        # fill in those three columns with respective metrics or KPIs
        kpi1.metric(
            label="Age ⏳",
            value=round(avg_age),
            delta=round(avg_age) - 10,
        )
        
        kpi2.metric(
            label="Married Count 💍",
            value=int(count_married),
            delta=-10 + count_married,
        )
        
        kpi3.metric(
            label="A/C Balance ＄",
            value=f"$ {round(balance,2)} ",
            delta=-round(balance / count_married) * 100,
        )

        # create two columns for charts
        fig_col1, fig_col2 = st.columns(2)
        with fig_col1:
            st.markdown("### First Chart")
            fig = px.density_heatmap(
                data_frame=df, y="age_new", x="marital"
            )
            st.write(fig)
            
        with fig_col2:
            st.markdown("### Second Chart")
            fig2 = px.histogram(data_frame=df, x="age_new")
            st.write(fig2)

        st.markdown("### Detailed Data View")
        st.dataframe(df)
        
        time.sleep(1)