Update app.py

app.py

@@ -12,17 +12,6 @@ longformer_tokenizer = AutoTokenizer.from_pretrained("allenai/longformer-base-40
 bert_model = AutoModelForSequenceClassification.from_pretrained("Reem333/BERT")
 bert_tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased")
 
-# Function to extract text from PDF
-def extract_text_from_pdf(file_path):
-    text = ''
-    try:
-        with fitz.open(file_path) as pdf_document:
-            for page_number in range(pdf_document.page_count):
-                page = pdf_document.load_page(page_number)
-                text += page.get_text()
-    except Exception as e:
-        st.error(f"Error reading PDF file: {e}")
-    return text
 
 # Function to predict the class of the text using a specified model and tokenizer
 def predict_class(text, model, tokenizer):
@@ -64,13 +53,13 @@ with st.sidebar:
     st.markdown("## About")
     st.markdown('''
     This tool classifies paper citations into different levels based on their number of citations.
-    Powered by Fine-Tuned [Longformer model](https://huggingface.co/REEM-ALRASHIDI/LongFormer-Paper-Citaion-Classifier) and BERT model with custom data.
+    Powered by Fine-Tuned [Longformer model](https://huggingface.co/Reem333/LongFormer) and BERT model with custom data.
     ''')
     st.markdown("### Class Levels:")
-    st.markdown("- Level 1: Highly cited papers")
+    st.markdown("- Level 1: Low cited papers")
     st.markdown("- Level 2: Average cited papers")
     st.markdown("- Level 3: More cited papers")
-    st.markdown("- Level 4: Low cited papers")
+    st.markdown("- Level 4: Highly cited papers")
     st.markdown("---")
     st.markdown('Tabuk University')