Update app.py

app.py

@@ -48,7 +48,7 @@ index_to_class = dict((v,k) for k,v in class_to_index.items())
 names_to_ids = lambda labels: np.array([class_to_index.get(x) for x in labels])
 train_labels = names_to_ids(labels)
 
-@spaces.GPU(duration=150)
+@spaces.GPU(duration=125)
 def load_model():
    
 
@@ -76,21 +76,21 @@ def load_model():
         ]
     )
     
-    test_tweets, test_labels = get_tweet(test)
-    test_seq = get_sequences(tokenizer, test_tweets)
-    test_labels = names_to_ids(test_labels)
+    # test_tweets, test_labels = get_tweet(test)
+    # test_seq = get_sequences(tokenizer, test_tweets)
+    # test_labels = names_to_ids(test_labels)
     
-    _ = model.evaluate(test_seq, test_labels)
-    i = random.randint(0, len(test_labels) - 1)
-    print('Sentence:', test_tweets[i])
-    print('Emotion:', index_to_class[test_labels[i]]) 
-    p = model.predict(np.expand_dims(test_seq[i],axis=0))[0]
-    pred_class = index_to_class[np.argmax(p).astype('uint8')]
-
-    print('Predicted Emotion:', pred_class)
+    # _ = model.evaluate(test_seq, test_labels)
+    # i = random.randint(0, len(test_labels) - 1)
+    # print('Sentence:', test_tweets[i])
+    # print('Emotion:', index_to_class[test_labels[i]]) 
+    # p = model.predict(np.expand_dims(test_seq[i],axis=0))[0]
+    # pred_class = index_to_class[np.argmax(p).astype('uint8')]
+
+    # print('Predicted Emotion:', pred_class)
     
-    preds = (model.predict(test_seq) > 0.5).astype("int32")
-    print(preds)
+    # preds = (model.predict(test_seq) > 0.5).astype("int32")
+    # print(preds)
     
     return model