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import sklearn
import gradio as gr
import joblib
import pandas as pd
import datasets
import requests
import json
import dateutil.parser as dp
title = "Stoclholm 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")
def infer(input_dataframe):
return pd.DataFrame(model.predict(input_dataframe))
response_tomtom = requests.get(
'https://api.tomtom.com/traffic/services/4/flowSegmentData/absolute/10/json?key=azGiX8jKKGxCxdsF1OzvbbWGPDuInWez&point=59.39575,17.98343')
json_response_tomtom = json.loads(response_tomtom.text) # get json response
currentSpeed = json_response_tomtom["flowSegmentData"]["currentSpeed"]
freeFlowSpeed = json_response_tomtom["flowSegmentData"]["freeFlowSpeed"]
congestionLevel = currentSpeed/freeFlowSpeed
confidence = json_response_tomtom["flowSegmentData"]["confidence"] # Reliability of the traffic data, by percentage
# Get weather data from SMHI, updated hourly
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)
# weather data manual https://opendata.smhi.se/apidocs/metanalys/parameters.html#parameter-wsymb
referenceTime = dp.parse(json_response_smhi["referenceTime"]).timestamp()
t = json_response_smhi["timeSeries"][0]["parameters"][0]["values"][0] # Temperature
ws = json_response_smhi["timeSeries"][0]["parameters"][4]["values"][0] # Wind Speed
prec1h = json_response_smhi["timeSeries"][0]["parameters"][6]["values"][0] # Precipation last hour
fesn1h = json_response_smhi["timeSeries"][0]["parameters"][8]["values"][0] # Snow precipation last hour
vis = json_response_smhi["timeSeries"][0]["parameters"][9]["values"][0] # Visibility
row = [referenceTime, t, ws, prec1h, fesn1h, vis, confidence, congestionLevel]
gr.Interface(fn = infer, inputs = inputs, outputs = outputs, title=title, description=description, examples=[row]).launch() |