Added gui

README.md

@@ -4,7 +4,7 @@ emoji: 📚
 colorFrom: green
 colorTo: blue
 sdk: gradio
-sdk_version: 4.14.0
+sdk_version: 4.10.0
 app_file: app.py
 pinned: false
 ---

app.py

@@ -0,0 +1,71 @@
+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("stockholm_incidents_model", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/stckhlm_inc_model.pkl")
+print("Model downloaded")
+
+
+def predict(magnitudeOfDelay, hour, iconCategory, latitude, longitude, month):
+    # Create a row from the input
+    row = {
+        'magnitudeOfDelay': magnitudeOfDelay,
+        'hour': hour,
+        'iconCategory': iconCategory,
+        'latitude': latitude,
+        'longitude': longitude,
+        'month': month
+    }
+
+    # Create a df from the row
+    df_row = pd.DataFrame(row, index=[0])
+
+    # change the order to code	hour	iconCategory	latitude	longitude	magnitudeOfDelay	month	duration
+    df_row = df_row[['magnitudeOfDelay', 'hour', 'iconCategory', 'latitude', 'longitude', 'month']]
+
+    # 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]
+    
+    # Remove the log transformation
+    prediction = prediction[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.Slider(0, 60, label="Magnitude of Delay"),
+        gr.inputs.Slider(0, 23, label="Hour"),
+        gr.inputs.Radio(["Accident", "Construction", "Congestion", "Disabled Vehicle", "Mass Transit", "Miscellaneous", "Other News", "Planned Event", "Road Hazard", "Roadwork", "Traffic Flow", "Weather"], 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()

requirements.txt

@@ -0,0 +1,6 @@
+numpy
+pandas
+plotly
+hopsworks
+joblib
+scikit-learn