Update app.py

app.py

@@ -71,7 +71,7 @@ def predict(location_name,lat, lon):
             ## Coordinate
             cord_df = pd.DataFrame({"Latitude":[lat],
                                     "Longitude":[lon]})
-            print("==================== cord_df SHAPE", cord_df.shape)
+            #print("==================== cord_df SHAPE", cord_df.shape)
             ## PCA dimension reduction
             # later reload the pickle file
             sdc_reload = pk.load(open("data/sdc.pkl",'rb'))
@@ -82,7 +82,7 @@ def predict(location_name,lat, lon):
             principalComponents = pca_reload .transform(X_pca)
             principalDf = pd.DataFrame(data =principalComponents[:,:4],
                            columns = ["PC1","PC2","PC3","PC4"])
-            print("==================== principalDf SHAPE", principalDf.shape)
+            #print("==================== principalDf SHAPE", principalDf.shape)
 
             # vegetation index calculation
             X = indices(X)
@@ -90,11 +90,11 @@ def predict(location_name,lat, lon):
             tab = list(range(12))
             X_index = X.drop(X.iloc[:,tab],axis=1)
 
-            print("=============SHAPE1",X_index.shape)
+            #print("=============SHAPE1",X_index.shape)
 
             # Create predictive features
             X_final =pd.concat([cord_df,principalDf,X_index],axis=1)
-            print("=============SHAPE2",X_final.shape)
+            #print("=============SHAPE2",X_final.shape)
 
             # load the model from disk
             filename = "data/finalized_model3.sav"