Update app.py

app.py

@@ -18,16 +18,26 @@ model = joblib.load(model_dir + "/air_model3.pkl")
 
 def forecast():
     
-    x = [ 0.        , 24        , -0.68645433, -0.06804887, -0.31264014,
-       -0.13749569, -0.32063957, -0.2942814 , -0.18460245, -0.41253886,
-        0.06395449,  0.71276574, -0.36466156, -1.03879548, -0.65985627,
-        0        ,  0        ,  0.12254366,  0.39172671,  0.34205118,
-        0.21383452, -1.0216134 ,  0.40277851, -0.34577169, -0.36832646,
-       -0.7210296 ,  0        ]
-
-    res = model.predict(np.asarray(x).reshape(-1, 1))
+    fs = project.get_feature_store() 
+    feature_view = fs.get_feature_view(
+        name = 'miami_data_air_quality_fv',
+        version = 1
+    )
+    train_data = feature_view.get_training_data(1)[0]
+    train_data = train_data.drop(labels = 'city_y',axis =1)
+    train_data = train_data.rename(columns = {'city_x':'city'})
+    train_data = train_data.sort_values(by="date", ascending=True).reset_index(drop=True)
+    train_data["aqi_next_day"] = train_data.groupby('city')['aqi'].shift(1)
     
-    return model_dir
+    train_data.head(5)
+    X = train_data.drop(columns=["date"]).fillna(0)
+    y = X.pop("aqi_next_day")
+    X = X.drop(columns =['city','conditions']).fillna(0)
+    
+    today_data = X[1:2]
+    y = model.predict(today_data)
+    
+    return y
 
 demo = gr.Interface(
     fn=forecast,