Upload app.py

app.py

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+import gradio as gr
+from datetime import timedelta, datetime
+import hopsworks
+import joblib
+from functions import *
+
+#Connect to hopsworks and get feature store
+project = hopsworks.login()
+fs = project.get_feature_store()
+
+#Function for the app
+def predict_weather(location):
+
+    #Get future weather data
+    weather_data1 = get_weather_df([get_weather_data(location, (datetime.now() + timedelta(days=1)).strftime("%Y-%m-%d"))])
+    weather_data2 = get_weather_df([get_weather_data(location, (datetime.now() + timedelta(days=2)).strftime("%Y-%m-%d"))])
+    weather_data3 = get_weather_df([get_weather_data(location, (datetime.now() + timedelta(days=3)).strftime("%Y-%m-%d"))])
+    weather_data4 = get_weather_df([get_weather_data(location, (datetime.now() + timedelta(days=4)).strftime("%Y-%m-%d"))])
+    weather_data5 = get_weather_df([get_weather_data(location, (datetime.now() + timedelta(days=5)).strftime("%Y-%m-%d"))])
+    weather_data6 = get_weather_df([get_weather_data(location, (datetime.now() + timedelta(days=6)).strftime("%Y-%m-%d"))])
+    weather_data7 = get_weather_df([get_weather_data(location, (datetime.now() + timedelta(days=7)).strftime("%Y-%m-%d"))])
+
+    weather_df = pd.concat([weather_data1, weather_data2, weather_data3, weather_data4, weather_data5, weather_data6, weather_data7], axis=0)
+
+    weather_df = weather_df.drop(columns=["precipprob", "uvindex", "date", "city", "conditions"]).fillna(0)
+    weather_df.rename(
+        columns={"pressure": "sealevelpressure"}, inplace=True)
+    print(weather_df)
+
+    #Get model
+    mr = project.get_model_registry()
+    model = mr.get_model("gradient_boost_model5", version=1)
+    model_dir = model.download()
+    model = joblib.load(model_dir + "/model5.pkl")
+
+    #Create predictions
+    preds = model.predict(weather_df)
+    print(preds)
+
+    list_of_predictions = []
+    for x in range(7):
+      list_of_predictions.append("Aqi on " + (datetime.now() + timedelta(days=x+1)).strftime('%Y-%m-%d') + ": " +  str(int(preds[x])))
+
+    return list_of_predictions
+
+#Gradio interface
+demo = gr.Interface(
+    fn=predict_weather,
+    title="Future air quality predictor",
+    description="Input the name of a location below to get future air quality predictions for that location",
+    allow_flagging="never",
+    inputs="text",
+    outputs="text"
+)
+
+demo.launch()