fixes #19 : issue with encoders not being defined when data is not lo…

…aded

src/api.py

@@ -20,10 +20,6 @@
 # for algorithm predection (at least that's the plan)
 fml = fmlearn()
 
-# doing this so that dataload and training of the model happens once at application start.
-fml.load_data()
-fml.train()
-
 # Create a Metric
 @metrics_api.route('', methods=[POST])
 def add_metric():
@@ -115,9 +111,13 @@ def predict_fmlearn():
 
     data = {}
 
-    if fml._new_recs == math.inf:
+    if fml._new_recs == math.inf or len(utils.get_df_from_db()) <= fml.MAX_NEW_RECORDS:
         data['response'] = 'Model not trained!'
         return json.dumps(data)
+
+    if not fml.is_model_trained():
+        # doing this so that data load and training of the model happens once at application has enough data.
+        fml.load_data_and_train()
 
     # fetching the encoder for target type for encoding the input data
     tt_encoder = fml.get_encoders()[utils.TARGET_TYPE]