Create app.py

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+import hopsworks
+import joblib
+import pandas as pd
+import numpy as np
+import json
+import time
+from datetime import timedelta, datetime
+
+
+from functions import *
+
+
+project = hopsworks.login()
+fs = project.get_feature_store()
+	
+
+def air_quality(city):
+    weather_df = pd.DataFrame()
+    for i in range(8):
+        weather_data = get_weather_df([get_weather_data((datetime.now() + timedelta(days=i)).strftime("%Y-%m-%d"))])
+        weather_df = weather_df.append(weather_data)
+        
+    weather_df = weather_df.drop(columns=["feelslikemin", "feelslikemax", "precipprob", "snowdepth", "uvindex", "date","city","conditions"]).fillna(0)
+	    
+    mr = project.get_model_registry()
+    model = mr.get_model("gradient_boost_model", version=1)
+    model_dir = model.download()
+    model = joblib.load(model_dir + "/model.pkl")
+	    
+    preds = model.predict(weather_df)
+	
+    predictions = ''
+    for k in range(7):
+        predictions += "Predicted AQI on  " + (datetime.now() + timedelta(days=k)).strftime('%Y-%m-%d') + ":      " + str(int(preds[k]))+"\n"
+	        
+        print(predictions)
+    return predictions
+	
+    
+demo = gr.Interface(fn=air_quality, title="Air quality predictor",
+description="Input a value to get next weeks AQI prediction for Malmo", inputs="text", outputs="text")
+	
+
+	    
+if __name__ == "__main__":
+	demo.launch()