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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 | |
| 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) | |
| row.dropna(axis=0, inplace=True) | |
| return row | |
| model = joblib.load("./traffic_model.pkl") | |
| def infer(input_dataframe): | |
| return pd.DataFrame(model.predict(input_dataframe)) | |
| 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" | |
| 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"])] | |
| gr.Interface(fn = infer, inputs = inputs, outputs = outputs, title=title, description=description, examples=[get_row()]).launch() |