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app.py

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+import streamlit as st
+import hopsworks
+import joblib
+from datetime import date
+import pandas as pd
+from datetime import timedelta, datetime
+from functions import *
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+import folium
+from streamlit_folium import st_folium, folium_static
+import json
+import time
+from branca.element import Figure
+
+
+def fancy_header(text, font_size=24):
+    res = f'<p style="color:#ff5f72; font-size: {font_size}px; text-align:center;">{text}</p>'
+    st.markdown(res, unsafe_allow_html=True)
+
+st.set_page_config(layout="wide")
+
+st.title('Air Quality Prediction Project🌩')
+
+st.write(36 * "-")
+fancy_header('\n Connecting to Hopsworks Feature Store...')
+
+project = hopsworks.login()
+
+st.write("Successfully connected!✔️")
+
+st.write(36 * "-")
+fancy_header('\n Getting data from Feature Store...')
+
+today = date.today()
+##########################城市####################
+city = "Guangzhou"
+df_weather = get_weather_data_weekly(city, today)
+df_weather.date = df_weather.date.apply(timestamp_2_time)
+df_weather_x = df_weather.drop(columns=["date"]).fillna(0)
+df_weather_nn=np.array(df_weather_x)
+scaler = StandardScaler()
+scaler.fit(df_weather_x)
+
+df_weather_use=scaler.transform(df_weather_x)
+
+df_weather_use_1= pd.DataFrame(df_weather_use)
+
+#preds_zzz = model.predict(df_weather_use_1).astype(int)
+
+st.write(36 * "-")
+
+########################根据模型名称进行修改#####################
+mr = project.get_model_registry()
+model = mr.get_model("air_quality_modal_choosed", version=1)
+model_dir = model.download()
+model = joblib.load(model_dir + "/air_quality_model_choosed.pkl")
+
+st.write("-" * 36)
+
+
+preds = model.predict(df_weather_use_1).astype(int)
+pollution_level = get_aplevel(preds.T.reshape(-1, 1))
+
+next_week = [f"{(today + timedelta(days=d)).strftime('%Y-%m-%d')},{(today + timedelta(days=d)).strftime('%A')}" for d in range(8)]
+
+df = pd.DataFrame(data=[preds, pollution_level], index=["AQI", "Air pollution level"], columns=next_week)
+###########如果报错AQI这个修改成preds的标签##################
+
+st.write(df)
+
+st.button("Re-run")
+
+

functions.py

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+import requests
+import os
+import joblib
+import pandas as pd
+import datetime
+import numpy as np
+import time
+from sklearn.preprocessing import OrdinalEncoder
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+
+def decode_features(df, feature_view):
+    """Decodes features in the input DataFrame using corresponding Hopsworks Feature Store transformation functions"""
+    df_res = df.copy()
+
+    import inspect
+
+
+    td_transformation_functions = feature_view._batch_scoring_server._transformation_functions
+
+    res = {}
+    for feature_name in td_transformation_functions:
+        if feature_name in df_res.columns:
+            td_transformation_function = td_transformation_functions[feature_name]
+            sig, foobar_locals = inspect.signature(td_transformation_function.transformation_fn), locals()
+            param_dict = dict([(param.name, param.default) for param in sig.parameters.values() if param.default != inspect._empty])
+            if td_transformation_function.name == "min_max_scaler":
+                df_res[feature_name] = df_res[feature_name].map(
+                    lambda x: x * (param_dict["max_value"] - param_dict["min_value"]) + param_dict["min_value"])
+
+            elif td_transformation_function.name == "standard_scaler":
+                df_res[feature_name] = df_res[feature_name].map(
+                    lambda x: x * param_dict['std_dev'] + param_dict["mean"])
+            elif td_transformation_function.name == "label_encoder":
+                dictionary = param_dict['value_to_index']
+                dictionary_ = {v: k for k, v in dictionary.items()}
+                df_res[feature_name] = df_res[feature_name].map(
+                    lambda x: dictionary_[x])
+    return df_res
+
+
+def get_model(project, model_name, evaluation_metric, sort_metrics_by):
+    """Retrieve desired model or download it from the Hopsworks Model Registry.
+    In second case, it will be physically downloaded to this directory"""
+    TARGET_FILE = "model.pkl"
+    list_of_files = [os.path.join(dirpath,filename) for dirpath, _, filenames \
+                     in os.walk('.') for filename in filenames if filename == TARGET_FILE]
+
+    if list_of_files:
+        model_path = list_of_files[0]
+        model = joblib.load(model_path)
+    else:
+        if not os.path.exists(TARGET_FILE):
+            mr = project.get_model_registry()
+            # get best model based on custom metrics
+            model = mr.get_best_model(model_name,
+                                      evaluation_metric,
+                                      sort_metrics_by)
+            model_dir = model.download()
+            model = joblib.load(model_dir + "/model.pkl")
+
+    return model
+
+    
+def get_weather_data_weekly(city: str, start_date: datetime) -> pd.DataFrame:
+    next7days_weather=pd.read_csv('https://weather.visualcrossing.com/VisualCrossingWebServices/rest/services/timeline/Guangzhou/next7days?unitGroup=metric&include=days&key=5WNL2M94KKQ4R4F32LFV8DPE4&contentType=csv')
+########################城市名############################
+    
+    df_weather = pd.DataFrame(next7days_weather)
+    df_weather.rename(columns = {"datetime": "date"}, 
+              inplace = True)
+#########################根据模型的feature进行修改###############################
+    df_weather = df_weather.drop(labels=['stations','icon','description','conditions','sunset','sunrise','severerisk','preciptype','name','feelslikemax','temp','precipprob','windspeed','cloudcover','precip','tempmax','uvindex','solarradiation','solarenergy','winddir','moonphase','snow','snowdepth'], axis=1) 
+
+    return df_weather
+
+def get_aplevel(temps:np.ndarray) -> list:
+    boundary_list = np.array([0, 50, 100, 150, 200, 300]) # assert temps.shape == [x, 1]
+    redf = np.logical_not(temps<=boundary_list) # temps.shape[0] x boundary_list.shape[0] ndarray
+    hift = np.concatenate((np.roll(redf, -1)[:, :-1], np.full((temps.shape[0], 1), False)), axis = 1)
+    cat = np.nonzero(np.not_equal(redf,hift))
+
+    air_pollution_level = ['Good', 'Moderate', 'Unhealthy for sensitive Groups','Unhealthy' ,'Very Unhealthy', 'Hazardous']
+    level = [air_pollution_level[el] for el in cat[1]]
+    return level
+    
+def timestamp_2_time(x):
+    dt_obj = datetime.datetime.strptime(str(x), '%Y-%m-%d')
+    dt_obj = dt_obj.timestamp() * 1000
+    return int(dt_obj)

requirements.txt

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+hopsworks
+scikit-learn == 1.0.2
+numpy
+joblib
+python-dotenv
+xgboost == 0.90
+optuna
+datetime
+folium
+streamlit_folium
+streamlit