Update app.py

app.py

@@ -71,8 +71,12 @@ from huggingface_hub import HfFileSystem
 from tensorflow.keras.models import Sequential, model_from_json
 import tensorflow_datasets as tfds
 import tensorflow as tf
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+import spacy
+nlp = spacy.load('en_core_web_lg')
 
 tfds.disable_progress_bar()
+MAX_SEQUENCE_LENGTH = 500
 
 # dataset = load_dataset('Seetha/Visualization', streaming=True)
 # df = pd.DataFrame.from_dict(dataset['train'])
@@ -164,13 +168,21 @@ def main():
 
   # pipeline_test_output = loaded_vectorizer.transform(class_list)
   # predicted = loaded_model.predict(pipeline_test_output)
+  text_embedding = np.zeros((len(word_index) + 1, 300))
+  for word, i in word_index.items():
+      text_embedding[i] = nlp(word).vector
   json_file = open('model.json', 'r')
   loaded_model_json = json_file.read()
   json_file.close()
   loaded_model = model_from_json(loaded_model_json)
   # load weights into new model
   loaded_model.load_weights("model.h5")
-    
+
+  loss = tf.keras.losses.CategoricalCrossentropy() #from_logits=True
+  loaded_model.compile(loss=loss,optimizer=tf.keras.optimizers.Adam(1e-4))
+
+  predictions = loaded_model.predict(pad_sequences(tokenizer.texts_to_sequences(class_list),maxlen=MAX_SEQUENCE_LENGTH))
+  predicted = np.argmax(predictions,axis=1)
   pred1 = predicted
   level0 = []
   count =0