fixed model

app.py

@@ -9,12 +9,12 @@ st.write(x, 'squared is', x * x)
 @st.cache_resource()
 def load_trained_model():
     
-    tokenizer = AutoTokenizer.from_pretrained("LampOfSocrates/bert-base-cased-sourav")
-    model = AutoModelForTokenClassification.from_pretrained("LampOfSocrates/bert-base-cased-sourav")
+    tokenizer = AutoTokenizer.from_pretrained("LampOfSocrates/bert-cased-plodcw-sourav")
+    model = AutoModelForTokenClassification.from_pretrained("LampOfSocrates/bert-cased-plodcw-sourav")
     # Mapping labels
-    label_map = model.config.id2label
+    id2label = model.config.id2label
     # Print the label mapping
-    print(label_map)
+    print(f"Can recognise the following labels {id2label}")
 
     # Load the NER model and tokenizer from Hugging Face
     #ner_pipeline = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english")
@@ -38,10 +38,10 @@ def prep_page():
     
         # Create a dictionary to map entity labels to colors
         label_colors = {
-            'ORG': 'lightblue',
-            'PER': 'lightgreen',
-            'LOC': 'lightcoral',
-            'MISC': 'lightyellow'
+            'B-LF': 'lightblue',
+            'B-O': 'lightgreen',
+            'B-AC': 'lightcoral',
+            'I-LF': 'lightyellow'
         }
     
         # Prepare the HTML output with styled entities