added tabs

app.py

@@ -6,25 +6,6 @@ import pickle
 import requests
 import base64
 
-@st.cache_data(ttl=3600)
-def read_model(url):
-    response = requests.get(url)
-    open("temp.pkl", "wb").write(response.content)
-    with open("temp.pkl", "rb") as f:
-        svm_classifier = pickle.load(f)
-    return svm_classifier
-
-
-def read_tf(url):
-    response = requests.get(url)
-    open("temp.pkl", "wb").write(response.content)
-    with open("temp.pkl", "rb") as f:
-        preprocessing = pickle.load(f)
-    return preprocessing
-
-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")
-
 
 
 # Create sidebar
@@ -33,8 +14,7 @@ preprocessing = read_tf("https://github.com/manika-lamba/ml/raw/main/preprocessi
 st.sidebar.header("Choose CSV File with 'Abstract' field")
 uploaded_file = st.sidebar.file_uploader("", type=["csv"])
 
-st.sidebar.header("Download Results")
-st.sidebar.text("Download the tagged results as a CSV file.")
+
 
 
 
@@ -60,6 +40,25 @@ with tab2:
         st.dataframe(df)
     
 # Function to predict the category for a given abstract
+@st.cache_data(ttl=3600)
+def read_model(url):
+    response = requests.get(url)
+    open("temp.pkl", "wb").write(response.content)
+    with open("temp.pkl", "rb") as f:
+        svm_classifier = pickle.load(f)
+    return svm_classifier
+
+
+def read_tf(url):
+    response = requests.get(url)
+    open("temp.pkl", "wb").write(response.content)
+    with open("temp.pkl", "rb") as f:
+        preprocessing = pickle.load(f)
+    return preprocessing
+
+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")
+
 def predict_category(abstract):
 # Preprocess the abstract
     abstract_preprocessed = preprocessing.transform([abstract])
@@ -70,6 +69,8 @@ def predict_category(abstract):
 with tab3:
 #===download result===
 # 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)