import sklearn
import gradio as gr
import joblib
import pandas as pd
import datasets
import requests
import json
import dateutil.parser as dp
import pandas as pd
from huggingface_hub import hf_hub_url, cached_download
import time
import datetime

title = "Stockholm Highway E4 Real Time Traffic Prediction"
description = "Stockholm E4 (59°23'44.7"" N 17°59'00.4""E) highway real time traffic prediction, updated in every hour"

inputs = [gr.Dataframe(row_count = (1, "fixed"), col_count=(7,"fixed"), label="Input Data", interactive=1)]

outputs = [gr.Dataframe(row_count = (1, "fixed"), col_count=(1, "fixed"), label="Predictions", headers=["Congestion Level"])]

model = joblib.load("./traffic_model.pkl")

response_smhi = requests.get(
                'https://opendata-download-metanalys.smhi.se/api/category/mesan1g/version/2/geotype/point/lon/17.983/lat/59.3957/data.json')
json_response_smhi = json.loads(response_smhi.text)

def infer(input_dataframe):
  return pd.DataFrame(model.predict(input_dataframe))
    
referenceTime = dp.parse(json_response_smhi["referenceTime"]).timestamp()


def get_time():
    return datetime.datetime.now()

#with gr.Blocks() as demo:
#    with gr.Row():
#        with gr.Column():
#            c_time2 = gr.Textbox(label="Current Time refreshed every second")           

#    demo.load(lambda: datetime.datetime.now(), None, c_time2, every=1)

    
with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            gr.Dataframe(row_count = (1, "fixed"), col_count=(7,"fixed"), 
                       headers=["referenceTime", "t", "ws", "prec1h", "fesn1h", "vis", "confidence"], 
                       # datatype=["timestamp", "float", "float", "float", "float", "float"],
                       label="Input Data", interactive=1)
        with gr.Column():
            c_time2 = gr.Textbox(label="Current Time refreshed every second")
    
            
        with gr.Column():
            gr.Dataframe(row_count = (1, "fixed"), col_count=(1, "fixed"), label="Predictions", headers=["Congestion Level"])
            
    demo.load(lambda: datetime.datetime.now(), None, c_time2, every=1)
    
    with gr.Row():
        btn_sub = gr.Button(value="Submit")
    
    btn_sub.click(infer, inputs = inputs, outputs = outputs)

demo.queue().launch()