created app.py

app.py

@@ -0,0 +1,76 @@
+import pickle
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
+#loading the saved parameters
+def load_params():
+    with open('/kaggle/input/latest-data/params.p', mode='rb') as in_file:
+        return pickle.load(in_file)
+
+def load_preprocess():
+    with open('/kaggle/input/latest-data/preprocess.p', mode='rb') as in_file:
+        return pickle.load(in_file)
+
+#getting the source and target vocabuaries
+_, (source_vocab_to_int, target_vocab_to_int), (source_int_to_vocab, target_int_to_vocab) = load_preprocess()
+
+load_path = load_params()
+print("Loaded path:", load_path)
+print(type(load_path))
+
+batch_size = 30
+
+#converting the words to vectors of integers
+def word_to_seq(word, vocab_to_int):
+    results = []
+    for word in list(word):
+        if word in vocab_to_int:
+            results.append(vocab_to_int[word])
+        else:
+            results.append(vocab_to_int['<UNK>'])
+            
+    return results
+
+#taking user input for prediction
+print("\n Enter word to be transliterated:")
+transliterate_word = input().lower()
+
+transliterate_word = word_to_seq(transliterate_word, source_vocab_to_int)
+
+#initialising the graph
+loaded_graph = tf.Graph()
+
+#initialising the session
+tf.compat.v1.Session()
+# with tf.Session(graph=loaded_graph) as sess:
+with tf.compat.v1.Session(graph=loaded_graph) as sess:
+        
+    # Load saved model
+    loader = tf.train.import_meta_graph("/kaggle/input/latest-data/dev.meta")
+    
+#     tf.train.Saver.restore(sess,load_path)
+    loader.restore(sess, "/kaggle/input/latest-data/dev")
+
+#providing placeholder names from the loaded graph
+    input_data = loaded_graph.get_tensor_by_name('input:0')
+    logits = loaded_graph.get_tensor_by_name('predictions:0')
+    target_sequence_length = loaded_graph.get_tensor_by_name('target_sequence_length:0')
+    keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')
+
+#transliterating the given word
+    transliterate_logits = sess.run(logits, {input_data: [transliterate_word]*batch_size,
+                                         target_sequence_length: [len(transliterate_word)]*batch_size,
+                                         keep_prob: 1.0})[0]
+
+print('Input')
+print('  Word Ids:      {}'.format([i for i in transliterate_word]))
+print('  English Word: {}'.format([source_int_to_vocab[i] for i in transliterate_word]))
+
+print('\nPrediction')
+print('  Word Id:      {}'.format([i for i in transliterate_logits]))
+
+#showing the output
+output = ""
+for i in transliterate_logits:
+        if target_int_to_vocab[i]!= '<EOS>':
+                output = output + target_int_to_vocab[i]
+print('  Hindi Word:      {}'.format(output))