Delete app.py

app.py

@@ -1,99 +0,0 @@
-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()
-	
-feature_view = fs.get_feature_view(
-    name = 'air_quality_fv',
-    version = 3
-)
-
-
-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=["date"]).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="Next week's AQI prediction for Paris", inputs="text", outputs="text")
-	
-
-	    
-if __name__ == "__main__":
-	demo.launch()
-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=["precipprob", "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="Next week's AQI prediction for Paris", inputs="text", outputs="text")
-	
-
-	    
-if __name__ == "__main__":
-	demo.launch()