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import gradio as gr
import plotly.graph_objects as go
import json
import requests
import os
from PIL import Image
import hopsworks
import joblib
import pandas as pd
import numpy as np
API_KEY = os.getenv("API-KEY-TOMTOM")
# Log into hopsworks
project = hopsworks.login()
fs = project.get_feature_store()
mr = project.get_model_registry()
model = mr.get_model("sthlm_incidents_model", version=4)
model_dir = model.download()
model = joblib.load(model_dir + "/sthlm_model.pkl")
print("Model downloaded")
def predict(code, magnitudeOfDelay, hour, iconCategory, latitude, longitude, month):
# Change the magnitude delay to an integer based on the index
if magnitudeOfDelay == 'Unknown':
magnitudeOfDelay = 0
elif magnitudeOfDelay == 'Minor':
magnitudeOfDelay = 1
elif magnitudeOfDelay == 'Moderate':
magnitudeOfDelay = 2
elif magnitudeOfDelay == 'Major':
magnitudeOfDelay = 3
elif magnitudeOfDelay == 'Undefined':
magnitudeOfDelay = 4
# Change the icon category to an integer based on the index
if iconCategory == 'Unknown':
iconCategory = 0
elif iconCategory == 'Accident':
iconCategory = 1
elif iconCategory == 'Fog':
iconCategory = 2
elif iconCategory == 'Dangerous Conditions':
iconCategory = 3
elif iconCategory == 'Rain':
iconCategory = 4
elif iconCategory == 'Ice':
iconCategory = 5
elif iconCategory == 'Jam':
iconCategory = 6
elif iconCategory == 'Lane Closed':
iconCategory = 7
elif iconCategory == 'Road Closed':
iconCategory = 8
elif iconCategory == 'Road Works':
iconCategory = 9
elif iconCategory == 'Wind':
iconCategory = 10
elif iconCategory == 'Flooding':
iconCategory = 11
elif iconCategory == 'Broken Down Vehicle':
iconCategory = 14
# Create a row from the input
row = {
'code': int(code),
'hour': int(hour),
'iconCategory': int(iconCategory),
'latitude': latitude,
'longitude': longitude,
'magnitudeOfDelay': int(magnitudeOfDelay),
'month': int(month)
}
# Create a df from the row
df_row = pd.DataFrame(row, index=[0])
# make the features lower case
df_row.columns = df_row.columns.str.lower()
df_row.columns = df_row.columns.str.replace(' ', '_')
# Get the prediction
prediction = model.predict(df_row)[0]
return prediction
demo = gr.Interface(
fn =predict,
title="Stockholm Incident Prediction",
description="Predicts the duration of a traffic incident in Stockholm",
allow_flagging="never",
inputs=[
gr.inputs.Radio(["101", "108", "115", "122", "201", "500", "701", "1101"]),
gr.inputs.Radio(["Unknown", "Minor", "Moderate", "Major", "Undefined"]),
gr.inputs.Slider(0, 23, label="Hour"),
gr.inputs.Radio(["Unknown", "Accident", "Fog", "Dangerous Conditions", "Rain", "Ice", "Jam", "Lane Closed", "Road Closed", "Road Works", "Wind", "Flooding", "Broken Down Vehicle"], label="Icon Category"),
gr.inputs.Slider(59.25, 59.40, label="Latitude"),
gr.inputs.Slider(18.00, 18.16, label="Longitude"),
gr.inputs.Slider(1, 12, label="Month")
],
outputs=[
gr.outputs.Textbox(label="Duration")
])
demo.launch() |