Create app.py

app.py

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+import tensorflow as tf
+import tensorflow_hub as hub
+import tensorflow_text as text
+import pandas as pd
+import tensorflow as tf
+import gradio as gr
+
+# Load the SavedModel
+model_path = '/content/drive/MyDrive/BERT/saved_model'
+loaded_model = tf.saved_model.load(model_path)
+
+# Retrieve the inference function (usually 'serving_default')
+infer = loaded_model.signatures['serving_default']
+
+def pre_process(input_data):
+    input_tensor = tf.constant(input_data, dtype=tf.string)
+    return input_tensor
+
+def ask(name):
+    data = pre_process(name)
+    predictions = infer(text = data)
+    output_tensor = predictions['output']
+    op = output_tensor.numpy()
+    if op[0] > 0.5:
+        return "The entered message is related to Banking"
+    else:
+        return "It is a non-banking message. May subject to be SPAM or other messages"
+
+demo = gr.Interface(fn=ask, inputs="text", outputs="text")
+demo.launch()
+
+