Update app.py

app.py

@@ -25,13 +25,7 @@ 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
-    abstract_preprocessed = preprocessing.transform([abstract])
-    # Make prediction
-    prediction = svm_classifier.predict(abstract_preprocessed)
-    return prediction
+
 
 # Create sidebar
 
@@ -39,22 +33,40 @@ def predict_category(abstract):
 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
-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("")
+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
+        df['category'] = df['Abstract'].apply(predict_category)
+        st.dataframe(df)
+        # Function to predict the category for a given abstract
+        def predict_category(abstract):
+        # Preprocess the abstract
+        abstract_preprocessed = preprocessing.transform([abstract])
+        # Make prediction
+        prediction = svm_classifier.predict(abstract_preprocessed)
+        return prediction
+
+    with tab3:
+        #===download result===
+        # Create a download button
+        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)