Created global temperature time series app

README.md

@@ -1,5 +1,5 @@
 ---
-title: Air Quality
+title: Global Temperature Time Series
 emoji: 🔥
 colorFrom: blue
 colorTo: pink
@@ -8,7 +8,7 @@ sdk_version: 1.42.2
 app_file: app.py
 pinned: false
 license: mit
-short_description: An unsupervised learning approach to air quality data.
+short_description: An unsupervised learning approach to global temperature data.
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,145 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.cluster import KMeans
+import joblib
+
+@st.cache_resource
+def load_dataset(path: str) -> pd.DataFrame:
+    """ 
+    Opens up a `.csv` file as our main dataset.
+    
+    Args:
+        path (str): Path to the dataset to be opened
+        
+    Returns:
+        pd.DataFrame: A pandas dataframe
+    """
+    
+    df = pd.read_csv(path)
+    
+    dates = pd.to_datetime(df['Date'])
+    df['Date'] = dates
+
+    df['Year'] = df['Date'].dt.year
+    df['Month'] = df['Date'].dt.month
+    df['Day'] = df['Date'].dt.day
+
+    df.drop('Date', axis=1, inplace=True)
+    
+    return df
+
+@st.cache_resource
+def load_model(model_path: str) -> KMeans:
+    return joblib.load(model_path)
+
+def process_data(input_X: pd.DataFrame) -> np.array:
+    """ 
+    Processes user input data into usable form for the KMeans model to predict.
+    
+    Args:
+        input_X (pd.DataFrame): Input data in dataframe format with one instance.
+    
+    Returns:
+        np.array: An numpy array for the KMeans model to predict
+    """
+    
+    input_X = input_X.copy()
+    
+    # Split up the dates
+    if 'Date' in input_X.columns:
+        dates = pd.to_datetime(input_X['Date'])
+        input_X['Date'] = dates
+
+        input_X['Year'] = input_X['Date'].dt.year
+        input_X['Month'] = input_X['Date'].dt.month
+        input_X['Day'] = input_X['Date'].dt.day
+
+        input_X.drop('Date', axis=1, inplace=True)
+    
+    input_X = pd.get_dummies(input_X, prefix=['Source'], dtype=int)
+    
+    for col in ['Source_GCAG', 'Source_GISTEMP']:
+        if col not in input_X.columns:
+            input_X[col] = 0
+
+    # Reorder columns to ensure correct order
+    input_X = input_X[['Mean', 'Year', 'Month', 'Day', 'Source_GCAG', 'Source_GISTEMP']]
+   
+    arr_X = input_X.to_numpy() 
+    
+    return arr_X
+    
+def plot_clusters(model: KMeans, X: np.array, input_X: np.array) -> None:
+    """
+    Plots the predicted class to the clusters.
+    
+    Args:
+        model (KMeans): A KMeans model trained on X input
+        X (np.array): The numpy array version of the dataset
+        input_X (np.array): The numpy array of the input 
+        
+    Returns:
+        None
+    """
+    centroids = model.cluster_centers_
+    labels = model.labels_
+    
+    fig = plt.figure(figsize=(10,6))
+    ax = fig.subplots()
+
+    for cluster in range(3):
+        cluster_points = X[labels == cluster]
+        plt.scatter(cluster_points[:, 0], cluster_points[:, 1], label=f'Cluster {cluster}')
+
+    # Plot centroids
+    ax.scatter(centroids[:, 0], centroids[:, 1], s=200, c='black', marker='X', label='Centroids')
+    
+    predictions = model.predict(input_X)
+    
+    st.write(f"Predicted Cluster: {predictions[0]}")
+
+    # Highlight the predicted cluster and point
+    ax.scatter(input_X[:, 0], input_X[:, 1], s=300, c='red', marker='P', label=f'Predicted Cluster: {predictions[0]}')
+
+    ax.set_title('K-Means Clustering with Predicted Point')
+    ax.legend()
+    st.pyplot(fig)
+
+def main():
+    st.title("Air Quality")
+    df = load_dataset("./monthly_csv.csv")
+    
+    tab1, tab2 = st.tabs(["KMeans Prediction", "About the Dataset"])
+    
+    with tab1: 
+        st.header("Input Data")
+        
+        source = st.selectbox("Choose your source platform.", tuple(df['Source'].unique()))
+        mean_temp = st.slider("Choose your avg. temp", df['Mean'].min(), df['Mean'].max())
+        date = st.date_input("Choose date to monitor air quality", min_value="1980-01-01", max_value=None)
+        
+        
+        model = None
+        if st.button("Predict Input!"):
+            d = pd.DataFrame({"Source": [source],
+                            "Mean": [mean_temp],
+                            "Date": [date]
+                            })
+            input_X = process_data(d)
+            
+            model = load_model('./models/kmeans_model.pkl')
+            if model is not None:
+                processed_df = process_data(df)
+                plot_clusters(model, processed_df, input_X)
+                
+    with tab2:
+        st.caption("Global Temperature Time Series. Data are included from the GISS Surface Temperature (GISTEMP) analysis and the global component of Climate at a Glance (GCAG). Two datasets are provided: 1) global monthly mean and 2) annual mean temperature anomalies in degrees Celsius from 1880 to the present.")
+        
+        st.write("Citation:")
+        st.caption("GISTEMP: NASA Goddard Institute for Space Studies (GISS) Surface Temperature Analysis, Global Land-Ocean Temperature Index.")
+        st.caption("NOAA National Climatic Data Center (NCDC), global component of Climate at a Glance (GCAG).)") 
+
+if __name__ == "__main__":
+    main()

models/kmeans_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f9bc03e7f03869ceda435deca4a487b4fa06f937b97f40404b6f760ddab5d495
+size 14023

monthly_csv.csv

@@ -0,0 +1,3289 @@
+Source,Date,Mean
+GCAG,2016-12-06,0.7895
+GISTEMP,2016-12-06,0.81
+GCAG,2016-11-06,0.7504
+GISTEMP,2016-11-06,0.93
+GCAG,2016-10-06,0.7292
+GISTEMP,2016-10-06,0.89
+GCAG,2016-09-06,0.8767
+GISTEMP,2016-09-06,0.87
+GCAG,2016-08-06,0.8998
+GISTEMP,2016-08-06,0.98
+GCAG,2016-07-06,0.8687
+GISTEMP,2016-07-06,0.83
+GCAG,2016-06-06,0.9006
+GISTEMP,2016-06-06,0.76
+GCAG,2016-05-06,0.8738
+GISTEMP,2016-05-06,0.93
+GCAG,2016-04-06,1.0733
+GISTEMP,2016-04-06,1.09
+GCAG,2016-03-06,1.2245
+GISTEMP,2016-03-06,1.3
+GCAG,2016-02-06,1.1921
+GISTEMP,2016-02-06,1.35
+GCAG,2016-01-06,1.0569
+GISTEMP,2016-01-06,1.17
+GCAG,2015-12-06,1.1219
+GISTEMP,2015-12-06,1.11
+GCAG,2015-11-06,0.9655
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+GISTEMP,1880-09-06,-0.17
+GCAG,1880-08-06,-0.0725
+GISTEMP,1880-08-06,-0.08
+GCAG,1880-07-06,-0.1491
+GISTEMP,1880-07-06,-0.24
+GCAG,1880-06-06,-0.1692
+GISTEMP,1880-06-06,-0.29
+GCAG,1880-05-06,-0.0738
+GISTEMP,1880-05-06,-0.14
+GCAG,1880-04-06,-0.0499
+GISTEMP,1880-04-06,-0.27
+GCAG,1880-03-06,-0.1357
+GISTEMP,1880-03-06,-0.18
+GCAG,1880-02-06,-0.1229
+GISTEMP,1880-02-06,-0.21
+GCAG,1880-01-06,0.0009
+GISTEMP,1880-01-06,-0.3

requirements.txt

@@ -0,0 +1,4 @@
+pandas==2.2.3
+streamlit==1.42.0
+scikit-learn==1.6.1
+matplotlib==3.10.0