Fixes bug

app.py

@@ -1,16 +1,14 @@
 import streamlit as st
 
 from emotion_model import emotion_predict
-from pre_processing_data import user_capture
 
 name = st.text_input("Enter your sentence here")
 if (st.button('Submit')):
     result = name.title()
     try:
         result_check = emotion_predict(result)
-        user_capture(result, result_check)
     except Exception as E:
         result_check = "Error"
         print(E)
-
     st.success(result_check)
+    user_response = st.text_input("Please give your emotion that you are thinking is correct")

emotion_model.py

@@ -3,7 +3,7 @@ import tensorflow as tf
 import tensorflow_addons as tfa
 
 from constance_data import emotion_track_list, decode_cut_list
-from pre_processing_data import preprocessing_data, pre_processing_data_2, text_transform
+from pre_processing_data import preprocessing_data, pre_processing_data_2, text_transform, user_capture
 
 
 def emotion_predict(sentence: str):
@@ -12,6 +12,7 @@ def emotion_predict(sentence: str):
     model = tf.keras.models.load_model("model/nlp_surrey_coursework_hunglenhat")
     model.compile(loss='sparse_categorical_crossentropy',
                   optimizer=tfa.optimizers.AdamW(learning_rate=lr, weight_decay=wd), metrics=['accuracy'])
+    sentence_temp = sentence
     sentence = pre_processing_data_2(sentence)
     if not sentence:
         sentence = preprocessing_data(sentence)
@@ -23,4 +24,5 @@ def emotion_predict(sentence: str):
         print(E)
     index_max = np.argmax(sentence)
     result = emotion_track_list[decode_cut_list[index_max]]
+    user_capture(sentence_temp, result)
     return result

pre_processing_data.py

@@ -86,13 +86,15 @@ def preprocessing_data(string_text):
     return string_output
 
 
-def user_capture(user_input, emotion_predict):
+def user_capture(user_input, emotion_prd):
     dataframe_capture = pd.read_csv('user_logs.csv')
     user_input_logs = pd.DataFrame({
         "user_input": [user_input],
-        "emotion_predict": [emotion_predict],
+        "emotion_predict": [emotion_prd],
         "time_logs": [datetime.now()],
     })
 
     dataframe_capture = pd.concat([dataframe_capture, user_input_logs], ignore_index=True)
     dataframe_capture.to_csv("user_logs.csv", index=False)
+    print("Done Recorded")
+    return None