Update app.py

app.py

@@ -1,23 +1,25 @@
-
 import gradio as gr
 import tensorflow as tf
 import tensorflow_hub as hub
+import tensorflow_text as text
 
+new_model = tf.keras.models.load_model('my_model.h5', custom_objects={'KerasLayer': hub.KerasLayer})
+categories = ('Spam', 'Not Spam')
 
-# def is_cat(x) : return x[0].isupper()
-
-options = tf.saved_model.LoadOptions(experimental_io_device='/job:localhost')
-learn = tf.keras.models.load_model('my_model.h5', custom_objects={'KerasLayer': hub.KerasLayer}, options=options)
-
-
-categories = ('Spam', 'Not spam')
 
 def classify_review(review):
-    pred,idx,probs = learn.predict(review)
-    return dict(zip(categories, map(float, probs)))
-
-review = gr.inputs.Textbox()
+    if review.strip() != "":
+        prob = new_model.predict([review])
+        spam_probability = float(prob[0][0])
+        output_label = f"Probability of being spam: {spam_probability}"
+        return output_label
+    else:
+        return "Enter a review first"
+    
+
+review = gr.inputs.Textbox(label="Review")
 label = gr.outputs.Label()
+
 examples = [
     """Congratulations! You have been selected as the lucky winner of our exclusive offer. 
     Get a chance to win a free vacation package by participating in our online survey. 
@@ -33,4 +35,6 @@ examples = [
 ]
 
 intf = gr.Interface(fn=classify_review, inputs=review, outputs=label, examples=examples)
-intf.launch(inline=False)
+
+if __name__ == "__main__":
+    intf.launch(inline=False, share=True)