removed empty_df during fitting process

app.py

@@ -261,26 +261,24 @@ def build_pipeline(text, model_name=selected_model):
                             ])
 
     
-    empty_df = spark.createDataFrame([[""]]).toDF("text")
+    text_df = spark.createDataFrame([[text]]).toDF("text")
 
     if model_name=="nerdl_fewnerd_100d":
-        pipeline_model= fewnerd_pipeline.fit(empty_df)
+        pipeline_model= fewnerd_pipeline.fit(text_df)
 
     elif model_name=="ner_conll_elmo":
-        pipeline_model=  elmo_pipeline.fit(empty_df)
+        pipeline_model=  elmo_pipeline.fit(text_df)
 
     elif model_name=="ner_mit_movie_complex_distilbert_base_cased":
-        pipeline_model= movie_pipeline.fit(empty_df)
+        pipeline_model= movie_pipeline.fit(text_df)
 
     elif model_name=="ner_conll_albert_large_uncased":
-        pipeline_model= albert_pipeline.fit(empty_df)
+        pipeline_model= albert_pipeline.fit(text_df)
 
     elif model_name=="onto_100":
-        pipeline_model= onto_pipeline.fit(empty_df)
+        pipeline_model= onto_pipeline.fit(text_df)
 
-
-    text_df= spark.createDataFrame(pd.DataFrame({"text": [text]}))
-    result= pipeline_model.transform(text_df).toPandas()
+    result = pipeline_model.transform(text_df).toPandas()
 
     return result