Update app.py

app.py

@@ -7,14 +7,6 @@ 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
@@ -30,8 +22,6 @@ def predict_class(text):
         st.error(f"Error during prediction: {e}")
         return None
 
-uploaded_files_dir = "uploaded_files"
-os.makedirs(uploaded_files_dir, exist_ok=True)
 
 class_colors = {
     0: "#d62728",  # Level 1
@@ -63,71 +53,36 @@ with st.sidebar:
 
 st.title("Check Your Paper Now!")
 
-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=["Nursing", "Physics", "Maths", "Chemical", "Nuclear", "Engineering" ,"Other"]
+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=["Nursing", "Physics", "Maths", "Chemical", "Nuclear", "Engineering" ,"Other"]
     
-    selected_category = st.selectbox("Select WoS categories:", options, index= None)
-    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] {' [SEP] '.join(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..."):
-                predicted_class = predict_class(combined_text)
-                if predicted_class is not None:
-                    class_labels = ["Level 1", "Level 2", "Level 3", "Level 4"]
-
-                    st.text("Predicted Class:")
-                    for i, label in enumerate(class_labels):
-                        if i == predicted_class:
-                            st.markdown(
-                                f'<div style="background-color: {class_colors[predicted_class]}; padding: 10px; border-radius: 5px; color: white; font-weight: bold;">{label}</div>',
-                                unsafe_allow_html=True
-                            )
-                        else:
-                            st.text(label)
-
-elif option == "PDF":
-    uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
-
-    if uploaded_file is not None:
-        with st.spinner("Processing PDF..."):
-            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 from PDF Text"):
-                if not file_text.strip():
-                    st.warning("Please upload a PDF with text content.")
-                else:
-                    with st.spinner("Predicting..."):
-                        predicted_class = predict_class(file_text)
-                        if predicted_class is not None:
-                            class_labels = ["Level 1", "Level 2", "Level 3", "Level 4"]
-                            st.text("**Predicted Class:**")
-                            for i, label in enumerate(class_labels):
-                                if i == predicted_class:
-                                    st.markdown(
-                                        f'<div style="background-color: {class_colors[predicted_class]}; padding: 10px; border-radius: 5px; color: white; font-weight: bold;">{label}</div>',
-                                        unsafe_allow_html=True
-                                    )
-                                else:
-                                    st.text(label)
+selected_category = st.selectbox("Select WoS categories:", options, index= None)
+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] {' [SEP] '.join(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..."):
+            predicted_class = predict_class(combined_text)
+            if predicted_class is not None:
+                class_labels = ["Level 1", "Level 2", "Level 3", "Level 4"]
+
+                st.text("Predicted Class:")
+                for i, label in enumerate(class_labels):
+                    if i == predicted_class:
+                        st.markdown(
+                            f'<div style="background-color: {class_colors[predicted_class]}; padding: 10px; border-radius: 5px; color: white; font-weight: bold;">{label}</div>',
+                            unsafe_allow_html=True
+                        )
+                    else:
+                        st.text(label)