Update app.py

app.py

@@ -26,22 +26,31 @@ st.sidebar.image("logo-wordlift.png")
 language_options = {"English", "German"}
 selected_language = st.sidebar.selectbox("Select the Language", list(language_options))
 
-# Initiate the model
-model_options = {"aida_model", "wikipedia_model_with_numbers"}
-selected_model_name = st.sidebar.selectbox("Select the Model", list(model_options))
-
-# Select entity_set
-entity_set_options = {"wikidata", "wikipedia"}
-selected_entity_set = st.sidebar.selectbox("Select the Entity Set", list(entity_set_options))
+# Based on selected language, display model and entity set options
+if selected_language != "German":
+    # Only show these options for languages other than German
+    model_options = {"aida_model", "wikipedia_model_with_numbers"}
+    selected_model_name = st.sidebar.selectbox("Select the Model", list(model_options))
+    # Select entity_set
+    entity_set_options = {"wikidata", "wikipedia"}
+    selected_entity_set = st.sidebar.selectbox("Select the Entity Set", list(entity_set_options))
+else:
+    selected_model_name = None
+    selected_entity_set = None
 
 @st.cache_resource  # 👈 Add the caching decorator
-def load_model(model_name, entity_set):
-    # Load the pretrained model
-    refined_model = Refined.from_pretrained(model_name=model_name, entity_set=entity_set)
-    return refined_model
+def load_model(selected_language, model_name=None, entity_set=None):
+    if selected_language == "German":
+        # Load the German-specific model
+        nlp_model_de = spacy.load("de_core_news_sm")
+        return nlp_model_de
+    else:
+        # Load the pretrained model for other languages
+        refined_model = Refined.from_pretrained(model_name=model_name, entity_set=entity_set)
+        return refined_model
 
 # Use the cached model
-refined_model = load_model(selected_model_name, selected_entity_set)
+model = load_model(selected_language, selected_model_name, selected_entity_set)
 
 # Addi citation
 citation = """
@@ -75,17 +84,13 @@ with st.form(key='my_form'):
     text_input = st.text_area(label='Enter a sentence')
     submit_button = st.form_submit_button(label='Analyze')
 
+# When processing the text, check the language and adjust processing accordingly
 if text_input:
     if selected_language == "German":
-        doc_de = nlp_model_de(text_input)
-        entities = [(ent.text, ent.label_, ent._.kb_qid, ent._.url_wikidata) for ent in doc_de.ents]
-        # You will have to adjust the rest of the code since the format is different
-        # For the demo, we'll simply print them for now
-        for entity in entities:
-            st.write(entity)
+        doc_de = model(text_input)
+        entities = [(ent.text, ent.label_) for ent in doc_de.ents]  # Adjust this based on your needs
 
     else:
-        entities = refined_model.process_text(text_input)
     
         entities = refined_model.process_text(text_input)