Update app.py

app.py

@@ -36,21 +36,16 @@ clear = st.button("Clear text input", on_click=clear_text)
 
 if submit:
     if text is not None:
-        df = []
-        df = pd.DataFrame(df, columns=['abstract'])
-        df.loc[0] = [text]
         nlp = English()
         sentencizer = nlp.add_pipe("sentencizer")
-        doc = nlp(df['abstract'][0])
+        doc = nlp(text)
         abstract_lines = [str(sent) for sent in list(doc.sents)]
-        total_lines_in_sample = len(abstract_lines)
-        sample_lines = []
-        for i, line in enumerate(abstract_lines):
-            sample_dict = {}
-            sample_dict["text"] = str(line)
-            sample_dict["line_number"] = i
-            sample_dict["total_lines"] = total_lines_in_sample - 1
-            sample_lines.append(sample_dict)
+
+        sample_lines = [{
+            "text": str(line),
+            "line_number": i,
+            "total_lines": len(abstract_lines) - 1
+        } for i, line in enumerate(abstract_lines)]
 
         test = pd.DataFrame(sample_lines)
         testing_sentences = test['text'].tolist()
@@ -60,9 +55,8 @@ if submit:
         new_model_probs = new_model.predict(testing_dataset)
         new_model_preds = tf.argmax(new_model_probs, axis=1)
         test_pred_classes = [label_encoder.classes_[pred] for pred in new_model_preds]
-        test["prediction"] = test_pred_classes  # create column with test prediction class names
+        test["prediction"] = test_pred_classes
         test["pred_prob"] = tf.reduce_max(new_model_probs, axis=1).numpy()
-        dict_abstract = enumerate(abstract_lines)
 
-        for i, line in dict_abstract:
+        for i, line in enumerate(abstract_lines):
             st.write(f'{test_pred_classes[i]} : {line}')