Delete app.py

app.py

@@ -1,65 +0,0 @@
-import streamlit as st
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import fitz
-import os
-
-model = AutoModelForSequenceClassification.from_pretrained("Reem333/Citaion-Classifier")
-tokenizer = AutoTokenizer.from_pretrained("allenai/longformer-base-4096")
-
-def extract_text_from_pdf(file_path):
-    text = ''
-    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()
-    return text
-
-def predict_class(text):
-    try:
-        max_length = 4096 
-        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
-
-uploaded_files_dir = "uploaded_files"
-os.makedirs(uploaded_files_dir, exist_ok=True)
-
-st.title("Paper Citation Classifier")
-option = st.radio("Select input type:", ("Text", "PDF"))
-
-if option == "Text":
-    text_input = st.text_area("Enter your text here:")
-    if st.button("Predict") and text_input.strip():
-        predicted_class = predict_class(text_input)
-        if predicted_class is not None:
-            class_labels = ["Level 1", "Level 2", "Level 3", "Level 4"]
-            st.text(f"Predicted Class: {class_labels[predicted_class]}")
-
-elif option == "PDF":
-    uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
-
-    if uploaded_file is not None:
-        file_path = os.path.join(uploaded_files_dir, uploaded_file.name)
-        with open(file_path, "wb") as f:
-            f.write(uploaded_file.getbuffer())
-        st.success("File uploaded successfully.")
-        st.text(f"File Path: {file_path}")
-        
-        file_text = extract_text_from_pdf(file_path)
-        st.text("Extracted Text:")
-        st.text(file_text)
-
-        if st.button("Predict"):
-            predicted_class = predict_class(file_text)
-            if predicted_class is not None:
-                class_labels = ["Level 1", "Level 2", "Level 3", "Level 4"]
-                st.text(f"Predicted Class: {class_labels[predicted_class]}")