put run_model function into helpers

app.py

@@ -1,5 +1,4 @@
 import time
-
 import altair as alt
 import joblib
 import numpy as np
@@ -8,11 +7,7 @@ import streamlit as st
 from sklearn.linear_model import LinearRegression
 import matplotlib.pyplot as plt
 import plotly.graph_objects as go
-from helper_functions import custom_metric_box, pollution_box
-import plotly.graph_objects as go
-import streamlit as st
-import pandas as pd
-import numpy as np
+from helper_functions import custom_metric_box, pollution_box, run_model
 
 st.set_page_config(
     page_title="Utrecht Pollution Dashboard",
@@ -21,24 +16,12 @@ st.set_page_config(
     initial_sidebar_state="expanded")
 
 alt.themes.enable("dark")
+
+prediction = run_model()  # Assuming you have a function run_model()
+
 # App Title
 st.title("Utrecht Pollution Dashboard 🌱")
 
-@st.cache_resource(ttl=6*300)  # Reruns every 6 hours
-def run_model():
-    # Load or train your model (pretrained model in this case)
-    model = joblib.load("linear_regression_model.pkl")
-    
-    # Static input values
-    input_data = pd.DataFrame({
-        'Temperature': [20.0],
-        'Wind Speed': [10.0],
-        'Humidity': [50.0]
-    })
-    
-    # Run the model with static input
-    prediction = model.predict(input_data)
-    return prediction
 col1, col2 = st.columns((1,1))
 # Create a 3-column layout
 with col1:
@@ -69,8 +52,6 @@ with col1:
     with col2:
         pollution_box(label="NO<sub>2</sub>", value="28 µg/m³", delta="+3 µg/m³")
 
-
-prediction = run_model()  # Assuming you have a function run_model()
 # Sample data (replace with your actual data)
 dates_past = pd.date_range(end=pd.Timestamp.today(), periods=7).to_list()
 dates_future = pd.date_range(start=pd.Timestamp.today() + pd.Timedelta(days=1), periods=3).to_list()

helper_functions.py

@@ -1,5 +1,22 @@
 import streamlit as st
+import joblib
+import pandas as pd
 
+@st.cache_resource(ttl=6*300)  # Reruns every 6 hours
+def run_model():
+    # Load or train your model (pretrained model in this case)
+    model = joblib.load("linear_regression_model.pkl")
+    
+    # Static input values
+    input_data = pd.DataFrame({
+        'Temperature': [20.0],
+        'Wind Speed': [10.0],
+        'Humidity': [50.0]
+    })
+    
+    # Run the model with static input
+    prediction = model.predict(input_data)
+    return prediction
 
 # Custom function to create styled metric boxes with subscripts, smaller label, and larger metric
 def custom_metric_box(label, value, delta):