Update app.py

app.py

@@ -0,0 +1,166 @@
+import gradio as gr
+import string
+import re
+import numpy as np
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.layers import TextVectorization
+
+strip_chars = pickle.load(open('strip_chars.pkl', 'rb'))
+
+vocab_size = 15000
+sequence_length = 20
+batch_size = 64
+
+class TransformerEncoder(layers.Layer):
+    def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):
+        super(TransformerEncoder, self).__init__(**kwargs)
+        self.embed_dim = embed_dim
+        self.dense_dim = dense_dim
+        self.num_heads = num_heads
+        self.attention = layers.MultiHeadAttention(
+            num_heads=num_heads, key_dim=embed_dim
+        )
+        self.dense_proj = keras.Sequential(
+            [layers.Dense(dense_dim, activation="relu"), layers.Dense(embed_dim),]
+        )
+        self.layernorm_1 = layers.LayerNormalization()
+        self.layernorm_2 = layers.LayerNormalization()
+        self.supports_masking = True
+
+    def call(self, inputs, mask=None):
+        if mask is not None:
+            padding_mask = tf.cast(mask[:, tf.newaxis, tf.newaxis, :], dtype="int32")
+        attention_output = self.attention(
+            query=inputs, value=inputs, key=inputs, attention_mask=padding_mask
+        )
+        proj_input = self.layernorm_1(inputs + attention_output)
+        proj_output = self.dense_proj(proj_input)
+        return self.layernorm_2(proj_input + proj_output)
+
+
+class PositionalEmbedding(layers.Layer):
+    def __init__(self, sequence_length, vocab_size, embed_dim, **kwargs):
+        super(PositionalEmbedding, self).__init__(**kwargs)
+        self.token_embeddings = layers.Embedding(
+            input_dim=vocab_size, output_dim=embed_dim
+        )
+        self.position_embeddings = layers.Embedding(
+            input_dim=sequence_length, output_dim=embed_dim
+        )
+        self.sequence_length = sequence_length
+        self.vocab_size = vocab_size
+        self.embed_dim = embed_dim
+
+    def call(self, inputs):
+        length = tf.shape(inputs)[-1]
+        positions = tf.range(start=0, limit=length, delta=1)
+        embedded_tokens = self.token_embeddings(inputs)
+        embedded_positions = self.position_embeddings(positions)
+        return embedded_tokens + embedded_positions
+
+    def compute_mask(self, inputs, mask=None):
+        return tf.math.not_equal(inputs, 0)
+
+
+class TransformerDecoder(layers.Layer):
+    def __init__(self, embed_dim, latent_dim, num_heads, **kwargs):
+        super(TransformerDecoder, self).__init__(**kwargs)
+        self.embed_dim = embed_dim
+        self.latent_dim = latent_dim
+        self.num_heads = num_heads
+        self.attention_1 = layers.MultiHeadAttention(
+            num_heads=num_heads, key_dim=embed_dim
+        )
+        self.attention_2 = layers.MultiHeadAttention(
+            num_heads=num_heads, key_dim=embed_dim
+        )
+        self.dense_proj = keras.Sequential(
+            [layers.Dense(latent_dim, activation="relu"), layers.Dense(embed_dim),]
+        )
+        self.layernorm_1 = layers.LayerNormalization()
+        self.layernorm_2 = layers.LayerNormalization()
+        self.layernorm_3 = layers.LayerNormalization()
+        self.supports_masking = True
+
+    def call(self, inputs, encoder_outputs, mask=None):
+        causal_mask = self.get_causal_attention_mask(inputs)
+        if mask is not None:
+            padding_mask = tf.cast(mask[:, tf.newaxis, :], dtype="int32")
+            padding_mask = tf.minimum(padding_mask, causal_mask)
+
+        attention_output_1 = self.attention_1(
+            query=inputs, value=inputs, key=inputs, attention_mask=causal_mask
+        )
+        out_1 = self.layernorm_1(inputs + attention_output_1)
+
+        attention_output_2 = self.attention_2(
+            query=out_1,
+            value=encoder_outputs,
+            key=encoder_outputs,
+            attention_mask=padding_mask,
+        )
+        out_2 = self.layernorm_2(out_1 + attention_output_2)
+
+        proj_output = self.dense_proj(out_2)
+        return self.layernorm_3(out_2 + proj_output)
+
+    def get_causal_attention_mask(self, inputs):
+        input_shape = tf.shape(inputs)
+        batch_size, sequence_length = input_shape[0], input_shape[1]
+        i = tf.range(sequence_length)[:, tf.newaxis]
+        j = tf.range(sequence_length)
+        mask = tf.cast(i >= j, dtype="int32")
+        mask = tf.reshape(mask, (1, input_shape[1], input_shape[1]))
+        mult = tf.concat(
+            [tf.expand_dims(batch_size, -1), tf.constant([1, 1], dtype=tf.int32)],
+            axis=0,
+        )
+        return tf.tile(mask, mult)
+
+custom_objects={'TransformerEncoder': TransformerEncoder, 'TransformerDecoder': TransformerDecoder, 'PositionalEmbedding':PositionalEmbedding}
+transformer = keras.models.load_model("model.h5", custom_objects=custom_objects)
+
+def custom_standardization(input_string):
+    lowercase = tf.strings.lower(input_string)
+    return tf.strings.regex_replace(lowercase, "[%s]" % re.escape(strip_chars), "")
+
+
+eng_vectorization = TextVectorization(
+    max_tokens=vocab_size, output_mode="int", output_sequence_length=sequence_length,
+)
+spa_vectorization = TextVectorization(
+    max_tokens=vocab_size,
+    output_mode="int",
+    output_sequence_length=sequence_length + 1,
+    standardize=custom_standardization,
+)
+train_eng_texts = [pair[0] for pair in train_pairs]
+train_spa_texts = [pair[1] for pair in train_pairs]
+eng_vectorization.adapt(train_eng_texts)
+spa_vectorization.adapt(train_spa_texts)
+
+inputs = gr.inputs.Textbox(lines=1, label="Text in Spanish")
+outputs = [gr.outputs.Textbox(label="Translated text in Quechua")]
+
+def get_translate(input_sentence):
+  spa_vocab = spa_vectorization.get_vocabulary()
+  spa_index_lookup = dict(zip(range(len(spa_vocab)), spa_vocab))
+  max_decoded_sentence_length = 20
+  tokenized_input_sentence = eng_vectorization([input_sentence])
+  decoded_sentence = "[start]"
+  for i in range(max_decoded_sentence_length):
+      tokenized_target_sentence = spa_vectorization([decoded_sentence])[:, :-1]
+      predictions = transformer([tokenized_input_sentence, tokenized_target_sentence])
+
+      sampled_token_index = np.argmax(predictions[0, i, :])
+      sampled_token = spa_index_lookup[sampled_token_index]
+      decoded_sentence += " " + sampled_token
+
+      if sampled_token == "[end]":
+          break
+  return decoded_sentence.replace("[start]", "").replace("[end]", "")
+iface=gr.Interface(fn=get_translate,inputs=inputs , outputs=outputs, title='Sakil EnglishToGerman Translator APP')
+
+iface.launch(debug=True)