my last straw

.gitignore

@@ -0,0 +1,4 @@
+.venv/
+.env
+__pycache__/
+*.pyc

README.md

@@ -11,4 +11,3 @@ short_description: 'Demo: Model to predict O3 and NO2 concentrations in Utrecht'
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
- hhhrhehheehehehe

app.py

@@ -1,31 +1,28 @@
-import time
 import altair as alt
-import joblib
-import numpy as np
 import pandas as pd
-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, run_model
+import streamlit as st
+from src.helper_functions import custom_metric_box, pollution_box
+from src.models_loading import run_model
 
 st.set_page_config(
     page_title="Utrecht Pollution Dashboard",
     page_icon="🏂��🌱",
     layout="wide",
-    initial_sidebar_state="expanded")
+    initial_sidebar_state="expanded",
+)
 
 alt.themes.enable("dark")
 
-prediction = run_model()  # Assuming you have a function run_model()
+test_predictions = run_model("O3")
 
 # App Title
-st.title("Utrecht Pollution Dashboard 🌱")
+st.title("Utrecht Pollution Dashboard🌱")
 
-col1, col2 = st.columns((1,1))
+col1, col2 = st.columns((1, 1))
 # Create a 3-column layout
 with col1:
-    st.subheader('Current Weather')
+    st.subheader("Current Weather")
     col1, col2, col3 = st.columns(3)
 
     # First column
@@ -43,10 +40,10 @@ with col1:
         custom_metric_box(label="Solar Radiation", value="200 W/m²", delta="-20 W/m²")
         custom_metric_box(label="Wind Speed", value="15 km/h", delta="-2 km/h")
 
-    st.subheader('Current Pollution Levels')
-    col1, col2 = st.columns((1,1))
+    st.subheader("Current Pollution Levels")
+    col1, col2 = st.columns((1, 1))
     # Display the prediction
-    #st.write(f'Predicted Pollution Level: {prediction[0]:.2f}')
+    # st.write(f'Predicted Pollution Level: {prediction[0]:.2f}')
     with col1:
         pollution_box(label="O<sub>3</sub>", value="37 µg/m³", delta="+2 µg/m³")
     with col2:
@@ -54,7 +51,9 @@ with col1:
 
 # 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()
+dates_future = pd.date_range(
+    start=pd.Timestamp.today() + pd.Timedelta(days=1), periods=3
+).to_list()
 
 # O3 and NO2 values for the past 7 days
 o3_past_values = [30, 32, 34, 33, 31, 35, 36]
@@ -70,61 +69,71 @@ o3_values = o3_past_values + o3_future_values
 no2_values = no2_past_values + no2_future_values
 
 # Create a DataFrame
-df = pd.DataFrame({
-    'Date': dates,
-    'O3': o3_values,
-    'NO2': no2_values
-})
+df = pd.DataFrame({"Date": dates, "O3": o3_values, "NO2": no2_values})
 
-st.subheader('O3 and NO2 Prediction')
+st.subheader("O3 and NO2 Prediction")
 # Create two columns for two separate graphs
 subcol1, subcol2 = st.columns(2)
 # Plot O3 in the first subcolumn
 with subcol1:
     fig_o3 = go.Figure()
-    fig_o3.add_trace(go.Scatter(x=df['Date'], y=df['O3'],
-                                mode='lines+markers',
-                                name='O3',
-                                line=dict(color='rgb(0, 191, 255)', width=4)))  # Bright blue
+    fig_o3.add_trace(
+        go.Scatter(
+            x=df["Date"],
+            y=df["O3"],
+            mode="lines+markers",
+            name="O3",
+            line=dict(color="rgb(0, 191, 255)", width=4),
+        )
+    )  # Bright blue
     # Add a vertical line for predictions (today's date)
     fig_o3.add_shape(
         dict(
             type="line",
-            x0=pd.Timestamp.today(), x1=pd.Timestamp.today(),
-            y0=min(o3_values), y1=max(o3_values),
+            x0=pd.Timestamp.today(),
+            x1=pd.Timestamp.today(),
+            y0=min(o3_values),
+            y1=max(o3_values),
             line=dict(color="White", width=3, dash="dash"),
         )
     )
     fig_o3.update_layout(
-        plot_bgcolor='rgba(0, 0, 0, 0)',  # Transparent background
-        paper_bgcolor='rgba(0, 0, 0, 0)',  # Transparent paper background
+        plot_bgcolor="rgba(0, 0, 0, 0)",  # Transparent background
+        paper_bgcolor="rgba(0, 0, 0, 0)",  # Transparent paper background
         yaxis_title="O3 Concentration (µg/m³)",
         font=dict(size=14),
-        hovermode="x unified"
+        hovermode="x unified",
     )
     st.plotly_chart(fig_o3)
 
 # Plot NO2 in the second subcolumn
 with subcol2:
     fig_no2 = go.Figure()
-    fig_no2.add_trace(go.Scatter(x=df['Date'], y=df['NO2'],
-                                    mode='lines+markers',
-                                    name='NO2',
-                                    line=dict(color='rgb(255, 20, 147)', width=4)))  # Bright pink
+    fig_no2.add_trace(
+        go.Scatter(
+            x=df["Date"],
+            y=df["NO2"],
+            mode="lines+markers",
+            name="NO2",
+            line=dict(color="rgb(255, 20, 147)", width=4),
+        )
+    )  # Bright pink
     # Add a vertical line for predictions (today's date)
     fig_no2.add_shape(
         dict(
             type="line",
-            x0=pd.Timestamp.today(), x1=pd.Timestamp.today(),
-            y0=min(no2_values), y1=max(no2_values),
+            x0=pd.Timestamp.today(),
+            x1=pd.Timestamp.today(),
+            y0=min(no2_values),
+            y1=max(no2_values),
             line=dict(color="White", width=3, dash="dash"),
         )
     )
     fig_no2.update_layout(
-        plot_bgcolor='rgba(0, 0, 0, 0)',  # Transparent background
-        paper_bgcolor='rgba(0, 0, 0, 0)',  # Transparent paper background
+        plot_bgcolor="rgba(0, 0, 0, 0)",  # Transparent background
+        paper_bgcolor="rgba(0, 0, 0, 0)",  # Transparent paper background
         yaxis_title="NO2 Concentration (µg/m³)",
         font=dict(size=14),
-        hovermode="x unified"
+        hovermode="x unified",
     )
-    st.plotly_chart(fig_no2)
+    st.plotly_chart(fig_no2)

requirements.txt

@@ -5,4 +5,5 @@ joblib  # or pickle if you're using that to load the model
 scikit-learn # for mock model
 altair
 matplotlib
-plotly
+plotly
+huggingface-hub

scalers/target_scaler_NO2.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:255a0d1dd1d8673ce03e838e9fc1a7df4dab1248ca70f6cb73b66aea83ed6316
+size 1023

scalers/target_scaler_O3.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2ad485897b59228f1c1efd8c76cc2fa771d10efd379297f163ceba32dbacbab6
+size 1023

helper_functions.py → src/helper_functions.py

@@ -1,22 +1,4 @@
 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):

src/models_loading.py

@@ -0,0 +1,37 @@
+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