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import pickle
import gradio as gr
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
def transliterate_eng_hin(input_word):
#loading the saved parameters
def load_params():
with open('./params.p', mode='rb') as in_file:
return pickle.load(in_file)
def load_preprocess():
with open('./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_word.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("./dev.meta")
# tf.train.Saver.restore(sess,load_path)
loader.restore(sess, "./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))
return output
demo = gr.Interface(fn=transliterate_eng_hin, inputs="text", outputs="text")
if __name__ == "__main__":
demo.launch() |