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):
st.markdown(f"""
""", unsafe_allow_html=True)
# Custom function to create pollution metric boxes with side-by-side layout for label and value
# Custom function to create pollution metric boxes with side-by-side layout and fixed width
def pollution_box(label, value, delta):
st.markdown(f"""
""", unsafe_allow_html=True)