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Real_Time_Traffic_Prediction
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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
import pandas as pd

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"), 
                       headers=["referenceTime", "t", "ws", "prec1h", "fesn1h", "vis", "confidence"], 
                       # datatype=["timestamp", "float", "float", "float", "float", "float"],
                       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))

def get_row():
    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": referenceTime, 
                        "t": t, 
                        "ws": ws, 
                        "prec1h": prec1h, 
                        "fesn1h": fesn1h, 
                        "vis": vis, 
                        "confidence": confidence}
    
    row = pd.DataFrame([row], columns=row.keys())
    print(row)

    return row

gr.Interface(fn = infer, inputs = inputs, outputs = outputs, title=title, description=description, examples=[get_row()]).launch()