Update app.py

app.py

@@ -7,23 +7,13 @@ from scripts.torch_ner_pipe import make_torch_entity_recognizer
 
 def main(models: str = None, default_text: str = None):
     st.title('NER Predictor')
-    #st.header('Enter the characteristics of the diamond:')
-    #carat = st.number_input('Carat Weight:', min_value=0.1, max_value=10.0, value=1.0)
-    #cut = st.selectbox('Cut Rating:', ['Fair', 'Good', 'Very Good', 'Premium', 'Ideal'])
-    #color = st.selectbox('Color Rating:', ['J', 'I', 'H', 'G', 'F', 'E', 'D'])
-    #clarity = st.selectbox('Clarity Rating:', ['I1', 'SI2', 'SI1', 'VS2', 'VS1', 'VVS2', 'VVS1', 'IF'])
-    #depth = st.number_input('Diamond Depth Percentage:', min_value=0.1, max_value=100.0, value=1.0)
-    #table = st.number_input('Diamond Table Percentage:', min_value=0.1, max_value=100.0, value=1.0)
-    #x = st.number_input('Diamond Length (X) in mm:', min_value=0.1, max_value=100.0, value=1.0)
-    #y = st.number_input('Diamond Width (Y) in mm:', min_value=0.1, max_value=100.0, value=1.0)
-    #z = st.number_input('Diamond Height (Z) in mm:', min_value=0.1, max_value=100.0, value=1.0)
     
     models = ["training/model-best","training_trf/model-best"]
     default_text = "The patient had surgery."
     models = [name.strip() for name in models.split(",")]
     labels = ["person", "problem", "pronoun", "test", "treatment"]
 
-    #if st.button('Predict Price'):
+    #if st.button('Predict entities'):
     spacy_streamlit.visualize(models, default_text, visualizers=["ner"], ner_labels=labels)