Upload 6 files

prediction.py

@@ -19,29 +19,29 @@ def get_feeling(number):
 
 # Load the model function
 def load_model():
-    return tf.keras.models.load_model('model.keras', custom_objects={'KerasLayer': tf_hub.KerasLayer})
+  return tf.keras.models.load_model('model.keras', custom_objects={'KerasLayer': tf_hub.KerasLayer})
 
-st.session_state.the_model = load_model()
+if 'the_model' not in st.session_state:
+  with st.spinner("Loading the model, please wait..."):
+    st.session_state.the_model = load_model()
 
 def app():
   st.header('Prediction', divider='rainbow')
 
   user_input = st.text_input("Enter your text here:")
-  if 'the_model' not in st.session_state:
-    with st.spinner("Loading the model, please wait..."):
-      if st.button('Predict', type="secondary"):
-        data = {
-          "text_processed": [
-              user_input
-          ]
-        }
-        df = pd.DataFrame(data)
-        with st.spinner("Making prediction..."):
-          # Replace with your preprocessing and prediction code
-          predictions = st.session_state.the_model.predict(df)
-          predicted_class = np.argmax(predictions, axis=1)
-          the_sentiment = predicted_class[0]
+  if st.button('Predict', type="secondary"):
+    data = {
+      "text_processed": [
+          user_input
+      ]
+    }
+    df = pd.DataFrame(data)
+    with st.spinner("Making prediction..."):
+      # Replace with your preprocessing and prediction code
+      predictions = st.session_state.the_model.predict(df)
+      predicted_class = np.argmax(predictions, axis=1)
+      the_sentiment = predicted_class[0]
 
-          st.write(f"We have predicted that the sentiment of this text is {get_feeling(the_sentiment)}")
-      else:
-        st.write("Click the button to predict!")
+      st.write(f"We have predicted that the sentiment of this text is {get_feeling(the_sentiment)}")
+  else:
+    st.write("Click the button to predict!")