app.py

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

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()
gr.Interface(fn = infer, inputs = inputs, outputs = outputs, title=referenceTime, description=description,live=True).launch()