fixed model id

app.py

@@ -21,6 +21,53 @@ def load_trained_model():
     ner_pipeline = pipeline("ner", model=model, tokenizer = tokenizer)
     return ner_pipeline
 
+def load_data():
+    from datasets import load_dataset
+    dat_CW = load_dataset("surrey-nlp/PLOD-CW")
+
+
+def render_entities(tokens, entities):
+    """
+    Renders a page with a 2-column table showing the entity corresponding to each token.
+    """
+    # Page configuration
+    st.set_page_config(page_title="NER Token Entities", layout="centered")
+
+    # Custom CSS for chilled and cool theme
+    st.markdown("""
+        <style>
+        body {
+            font-family: 'Arial', sans-serif;
+            background-color: #f0f0f5;
+            color: #333333;
+        }
+        table {
+            width: 100%;
+            border-collapse: collapse;
+        }
+        th, td {
+            padding: 12px;
+            text-align: left;
+            border-bottom: 1px solid #dddddd;
+        }
+        th {
+            background-color: #4CAF50;
+            color: white;
+        }
+        tr:hover {
+            background-color: #f5f5f5;
+        }
+        </style>
+        """, unsafe_allow_html=True)
+
+    # Title and description
+    st.title("Token Entities Table")
+    st.write("This table shows the entity corresponding to each token in a cool and chilled theme.")
+
+    # Create the table
+    table_data = {"Token": tokens, "Entity": entities}
+    st.table(table_data)
+
 def prep_page():
     model = load_trained_model()
 
@@ -70,11 +117,7 @@ def prep_page():
         
         st.markdown(styled_text, unsafe_allow_html=True)
 
-def load_data():
-    from datasets import load_dataset
-    dat_CW = load_dataset("surrey-nlp/PLOD-CW")
-    
-
+        render_entities(text, entities)
 
 if __name__ == '__main__':