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import os
import joblib
import pandas as pd
import streamlit as st
from dotenv import load_dotenv
from huggingface_hub import hf_hub_download, login
def load_model(particle):
load_dotenv()
login(token=os.getenv("HUGGINGFACE_DOWNLOAD_TOKEN"))
repo_id = f"elisaklunder/Utrecht-{particle}-Forecasting-Model"
if particle == "O3":
file_name = "O3_svr_model.pkl"
elif particle == "NO2":
file_name == "hehehe"
model_path = hf_hub_download(repo_id=repo_id, filename=file_name)
model = joblib.load(model_path)
return model
@st.cache_resource(ttl=6 * 300) # Reruns every 6 hours
def run_model(particle):
model = load_model(particle)
# 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
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