Update app.py

app.py

@@ -1,4 +1,3 @@
-import streamlit as st
 import gradio as gr
 import hopsworks
 import joblib
@@ -16,35 +15,33 @@ project = hopsworks.login()
 fs = project.get_feature_store()
 	
 
-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)
+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)
+    weather_df = weather_df.drop(columns=["precipprob", "uvindex", "date","city","conditions"]).fillna(0)
 	    
-mr = project.get_model_registry()
-model = mr.get_model("lond_gradient_boost_model", version=1)
-model_dir = model.download()
-model = joblib.load(model_dir + "/lond_model.pkl")
+    mr = project.get_model_registry()
+    model = mr.get_model("lond_gradient_boost_model", version=1)
+    model_dir = model.download()
+    model = joblib.load(model_dir + "/lond_model.pkl")
 	    
-preds = model.predict(weather_df)
+    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 London", inputs="text", outputs="text")
 	
-predictions = ''
-for k in range(7):
-    predictions += "Predicted AQI on  " + (datetime.now() + timedelta(days=k)).strftime('%Y-%m-%d') + ":      " + str(int(preds[k]))+"\n"
-	    
-
-air_pollution_level = ['Good', 'Moderate', 'Unhealthy for sensitive Groups','Unhealthy' ,'Very Unhealthy', 'Hazardous']
-poll_level = get_aplevel(preds.T.reshape(-1, 1), air_pollution_level)
-
-next_week_datetime = [today + datetime.timedelta(days=d) for d in range(7)]
-next_week_str = [f"{days.strftime('%A')}, {days.strftime('%Y-%m-%d')}" for days in next_week_datetime]
-
-df = pd.DataFrame(data=[preds, poll_level], index=["AQI", "Air pollution level"], columns=next_week_str)
-
-st.write("Here they are!")
-st.dataframe(df.style.apply(get_color, subset=(["Air pollution level"], slice(None))))
 
-st.button("Re-run")
-    
+	    
+if __name__ == "__main__":
+	demo.launch()