Delete ‏‏app.py

‏‏app.py

@@ -1,126 +0,0 @@
-import streamlit as st
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import fitz
-import os
-
-# Load Longformer model and tokenizer
-longformer_model = AutoModelForSequenceClassification.from_pretrained("Reem333/Longformer")
-longformer_tokenizer = AutoTokenizer.from_pretrained("allenai/longformer-base-4096")
-
-# Load BERT model and tokenizer
-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):
-    try:
-        max_length = 4096 if "longformer" in str(model) else 512
-        truncated_text = text[:max_length]
-
-        inputs = tokenizer(truncated_text, return_tensors="pt", padding=True, truncation=True, max_length=max_length)
-        with torch.no_grad():
-            outputs = model(**inputs)
-            logits = outputs.logits
-            predicted_class = torch.argmax(logits, dim=1).item()
-        return predicted_class
-    except Exception as e:
-        st.error(f"Error during prediction: {e}")
-        return None
-
-# Setup for uploaded files directory
-uploaded_files_dir = "uploaded_files"
-os.makedirs(uploaded_files_dir, exist_ok=True)
-
-# Color mapping for class levels
-class_colors = {
-    0: "#d62728",  # Level 1
-    1: "#ff7f0e",  # Level 2
-    2: "#2ca02c",  # Level 3
-    3: "#1f77b4"   # Level 4
-}
-
-# Streamlit page configuration
-st.set_page_config(page_title="Paper Citation Classifier", page_icon="logo.png")
-
-# Sidebar content
-with st.sidebar:
-    st.image("logo.png", width=70)
-    st.markdown('<div style="position: absolute; left: 5px;"></div>', unsafe_allow_html=True)
-    st.markdown("# Paper Citation Classifier")
-    st.markdown("---")
-    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.
-    ''')
-    st.markdown("### Class Levels:")
-    st.markdown("- Level 1: Highly 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("---")
-    st.markdown('Tabuk University')
-
-st.title("Check Your Paper Now!")
-
-# Main content
-option = st.radio("Select input type:", ("Text", "PDF"))
-
-if option == "Text":
-    title_input = st.text_area("Enter Title:")
-    abstract_input = st.text_area("Enter Abstract:")
-    full_text_input = st.text_area("Enter Full Text:")
-    affiliations_input = st.text_area("Enter Affiliations:")
-    keywords_input = st.text_area("Enter Keywords:")
-    options = ['cs', "AI"]
-
-    selected_category = st.selectbox("Select WoS categories:", options)
-    if selected_category == "Other":
-        custom_category = st.text_input("Enter custom category:")
-        selected_category = custom_category if custom_category else "Other"
-
-    combined_text = f"{title_input} [SEP] {keywords_input} [SEP] {abstract_input} [SEP] {selected_category} [SEP] {affiliations_input} [SEP] {full_text_input}"
-
-    if st.button("Predict"):
-        if not any([title_input, abstract_input, keywords_input, full_text_input, affiliations_input]):
-            st.warning("Please enter paper text.")
-        else:
-            with st.spinner("Predicting..."):
-                longformer_class = predict_class(combined_text, longformer_model, longformer_tokenizer)
-                bert_class = predict_class(combined_text, bert_model, bert_tokenizer)
-                if longformer_class is not None and bert_class is not None:
-                    class_labels = ["Level 1", "Level 2", "Level 3", "Level 4"]
-
-                    st.text("Longformer Predicted Class:")
-                    for i, label in enumerate(class_labels):
-                        if i == longformer_class:
-                            st.markdown(
-                                f'<div style="background-color: {class_colors[longformer_class]}; padding: 10px; border-radius: 5px; color: white; font-weight: bold;">{label}</div>',
-                                unsafe_allow_html=True
-                            )
-                        else:
-                            st.text(label)
-
-                    st.text("BERT Predicted Class:")
-                    for i, label in enumerate(class_labels):
-                        if i == bert_class:
-                            st.markdown(
-                                f'<div style="background-color: {class_colors[bert_class]}; padding: 10px; border-radius: 5px; color: white; font-weight: bold;">{label}</div>',
-                                unsafe_allow_html=True
-                            )
-                        else:
-                            st.text(label)
-