Upload app.py

app.py

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+import json
+import numpy as np
+import gradio as gr
+import tensorflow as tf
+from tensorflow import keras
+from huggingface_hub.keras_mixin import from_pretrained_keras
+
+class CustomNonPaddingTokenLoss(keras.losses.Loss):
+    def __init__(self, name="custom_ner_loss"):
+        super().__init__(name=name)
+        
+    def call(self, y_true, y_pred):
+        loss_fn = keras.losses.SparseCategoricalCrossentropy(
+                    from_logits=True, reduction=keras.losses.Reduction.NONE
+        )
+        
+        loss = loss_fn(y_true, y_pred)
+        mask = tf.cast((y_true > 0), dtype=tf.float32)
+        
+        loss = loss * mask
+        return tf.reduce_sum(loss) / tf.reduce_sum(mask)
+
+def lowercase_and_convert_to_ids(tokens):
+    tokens = tf.strings.lower(tokens)
+    
+    return lookup_layer(tokens)
+
+def tokenize_and_convert_to_ids(text):
+    tokens = text.split()
+    return lowercase_and_convert_to_ids(tokens)
+
+
+def ner_tagging(text_1):
+
+	with open("vocab.json",'r') as f:
+		vocab = json.load(f)
+
+	with open('mapping.json','r') as f:
+		mapping = json.load(f)
+
+	ner_model = from_pretrained_keras("keras-io/ner-with-transformers",
+                                  custom_objects={'CustomNonPaddingTokenLoss':CustomNonPaddingTokenLoss},
+                                  compile=False)  
+
+	lookup_layer = keras.layers.StringLookup(vocabulary=vocab['tokens'])
+
+	sample_input = tokenize_and_convert_to_ids(text_1)
+	sample_input = tf.reshape(sample_input, shape=[1, -1])
+	output = ner_model.predict(sample_input)
+	prediction = np.argmax(output, axis=-1)[0]
+
+	prediction = [mapping[str(i)] for i in prediction]
+
+	return prediction
+
+text_1 = gr.inputs.Textbox(lines=5)
+
+ner_tag = gr.outputs.Textbox()
+
+iface = gr.Interface(ner_tagging, 
+	inputs=text_1,outputs=ner_tag, examples=[['EU rejects German call to boycott British lamb .'], 
+	["Wednesday's U.S. Open draw ceremony revealed that both title holders should run into their first serious opposition in the third round."]])
+
+iface.launch()