updated app.py file

app.py

@@ -1,76 +1,86 @@
 import pickle
+import gradio
 import tensorflow.compat.v1 as tf
 tf.disable_v2_behavior()
-#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>'])
+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:
             
-    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("./dev.meta")
         
-    # Load saved model
-    loader = tf.train.import_meta_graph("./dev.meta")
+    #     tf.train.Saver.restore(sess,load_path)
+        loader.restore(sess, "./dev")
     
-#     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]
+    #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))
 
-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]))
+    return output
 
-print('\nPrediction')
-print('  Word Id:      {}'.format([i for i in transliterate_logits]))
+demo = gr.Interface(fn=transliterate_eng_hin, inputs="text", outputs="text")
 
-#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))
+if __name__ == "__main__":
+    demo.launch()