Update app.py

app.py

@@ -1,46 +1,10 @@
-from sklearn.model_selection import train_test_split
-
 import streamlit as st
 import pandas as pd
 import pickle
 import requests
 import base64
 
-
-
-# Create sidebar
-
-# Create tab for choosing CSV file
-st.sidebar.header("Choose CSV File with 'Abstract' field")
-uploaded_file = st.sidebar.file_uploader("", type=["csv"])
-
-
-
-
-
-st.title("About")
-st.subheader("You can tag your input CSV file of theses and dissertations with Library Science, Archival Studies, and Information Science categories. The screen will show the output.")
-
-tab1, tab2, tab3 = st.tabs(["📈 Load Data", "📃 Tagged ETDs", "📓 Download Data"])
-
-with tab1:
-#===load data===
-    if uploaded_file is not None:
-        df = pd.read_csv(uploaded_file, encoding='latin-1')
-        st.dataframe(df)
-    
-with tab2:
-#===tagged ETDs===
-# Tag the "Abstract" column with the corresponding categories
-
-    if uploaded_file is not None:
-        df = pd.read_csv(uploaded_file, encoding='latin-1')
-        st.dataframe(df)
-        df['category'] = df['Abstract'].apply(predict_category)
-        st.dataframe(df)
-    
-# Function to predict the category for a given abstract
-@st.cache_data(ttl=3600)
+@st.cache
 def read_model(url):
     response = requests.get(url)
     open("temp.pkl", "wb").write(response.content)
@@ -59,21 +23,36 @@ def read_tf(url):
 svm_classifier = read_model("https://github.com/manika-lamba/ml/raw/main/model2.pkl")
 preprocessing = read_tf("https://github.com/manika-lamba/ml/raw/main/preprocessing.pkl")
 
+# Function to predict the category for a given abstract
 def predict_category(abstract):
-# Preprocess the abstract
+    # Preprocess the abstract
     abstract_preprocessed = preprocessing.transform([abstract])
-# Make prediction
+    # Make prediction
     prediction = svm_classifier.predict(abstract_preprocessed)
     return prediction
 
-with tab3:
-#===download result===
+# Create sidebar
+
+# Create tab for choosing CSV file
+st.sidebar.header("Choose CSV File with 'Abstract' field")
+uploaded_file = st.sidebar.file_uploader("", type=["csv"])
+
+if uploaded_file is not None:
+    df = pd.read_csv(uploaded_file, encoding='latin-1')
+    st.dataframe(df)
+    # Tag the "Abstract" column with the corresponding categories
+    df['category'] = df['Abstract'].apply(predict_category)
+    st.dataframe(df)
+
+st.sidebar.header("Download Results")
+st.sidebar.text("Download the tagged results as a CSV file.")
+
 # Create a download button
-    st.sidebar.header("Download Results")
-    st.sidebar.text("Download the tagged results as a CSV file.")
+if st.sidebar.button("Download"):
+    csv = df.to_csv(index=False)
+    b64 = base64.b64encode(csv.encode()).decode()
+    href = f'<a href="data:file/csv;base64,{b64}" download="results.csv">Download csv file</a>'
+    st.markdown(href, unsafe_allow_html=True)
 
-    if st.sidebar.button("Download"):
-        csv = df.to_csv(index=False)
-        b64 = base64.b64encode(csv.encode()).decode()
-        href = f'<a href="data:file/csv;base64,{b64}" download="results.csv">Download csv file</a>'
-        st.markdown(href, unsafe_allow_html=True)
+st.title("About")
+st.subheader("You can tag your input CSV file of theses and dissertations with Library Science, Archival Studies, and Information Science categories. The screen will show the output.")