feat(py): add a button to prevent automatic inference

app.py

@@ -52,6 +52,7 @@ def clean_text(text):
 
 st.title('Welcome to my twitter airline sentiment analysis !', anchor='center')
 airline_tweet = st.text_input('Enter your english airline tweet here, press enter, and wait for the model to predict the sentiment of your review:', '@AmericanAirline My flight was great! :)')
+
 tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased', num_labels=2)
 
 encoded_input = tokenizer(
@@ -83,13 +84,16 @@ y = Dense(2, activation = 'softmax')(out) #Here 2 because we got 2 categories to
 model = tf.keras.Model(inputs=bert_inputs, outputs=y)
 
 model.load_weights('sentiment_weights.h5')
-prediction = model.predict({'input_ids' : encoded_input['input_ids'],'input_mask' : encoded_input['attention_mask']})
-encoded_dict = {0: 'negative', 1: 'positive'}
-
-if np.argmax(prediction) == 0:
-    st.write(f'Sentiment predicted : {encoded_dict[np.argmax(prediction)]}')
-    st.write(f'I\'m sorry you had a bad experience with our company :( , please accept our apologies')
-else:
-    st.write(f'Sentiment predicted : {encoded_dict[np.argmax(prediction)]}\n')
-    st.write('Glad your flight was good ! Hope to see you soon :)')
+
+st.button('Predict sentiment')
+
+if st.button('Predict sentiment'):
+    prediction = model.predict({'input_ids': encoded_input['input_ids'], 'input_mask': encoded_input['attention_mask']})
+    encoded_dict = {0: 'negative', 1: 'positive'}
+    if np.argmax(prediction) == 0:
+        st.write(f'Sentiment predicted : {encoded_dict[np.argmax(prediction)]}')
+        st.write(f'I\'m sorry you had a bad experience with our company :( , please accept our apologies')
+    else:
+        st.write(f'Sentiment predicted : {encoded_dict[np.argmax(prediction)]}')
+        st.write('Glad your flight was good ! Hope to see you soon :)')