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| import tensorflow as tf | |
| from transformers import TFDistilBertForSequenceClassification, DistilBertTokenizerFast | |
| import gradio as gr | |
| import random | |
| # Load the model architecture and weights | |
| model = TFDistilBertForSequenceClassification.from_pretrained("spam_detection_model/") | |
| # Load the tokenizer | |
| tokenizer = DistilBertTokenizerFast.from_pretrained("distilbert-base-uncased") | |
| # Define the process_input function | |
| def process_input(text): | |
| # Preprocess the sample text using the tokenizer | |
| encodings = tokenizer(text, truncation=True, padding=True, return_tensors="tf") | |
| # Perform inference | |
| logits = model(encodings.input_ids).logits | |
| # Convert logits to probabilities using softmax | |
| probabilities = tf.nn.softmax(logits, axis=-1) | |
| # Get the predicted class | |
| predicted_class = tf.argmax(probabilities, axis=-1).numpy()[0] | |
| # Map the predicted class to label | |
| label_mapping = { | |
| 0: '<b><div style="font-size:16px; text-align:center;">No need to worry, Not a spam message.</div></b>', | |
| 1: '<b><div style="font-size:16px; color:#ff3b5c; text-align:center;">Warning⚠️: This message has been identified as spam.</div></b>', | |
| } | |
| predicted_label = label_mapping[predicted_class] | |
| return [ | |
| { | |
| "Spam": float(probabilities.numpy()[0][1]), | |
| "Not a Spam": float(probabilities.numpy()[0][0]), | |
| }, | |
| predicted_label, | |
| ] | |
| # Define the Gradio interface | |
| title = "Spam Detector⚠️" | |
| examples = [ | |
| "Dear Customer, Your account has been compromised. Click the link below to verify your account details immediately or risk suspension. **(Example 1)**", | |
| "You've been selected as the lucky winner of our international sweepstakes! To claim your prize, reply with your full name, address, and bank details. <font color='blue' style='background-color: lightgray;'>(Example 2)</font>", | |
| "Congratulations! You've won a free iPhone X. Click the link to claim your prize.", | |
| "URGENT: Your bank account has been compromised. Click here to reset your password.", | |
| "Get rich quick! Invest in our exclusive program and earn thousands overnight.", | |
| "Your prescription refill is ready for pickup at your local pharmacy. Visit us at your convenience", | |
| "Reminder: Your monthly utility bill is due on August 20th. Please make the payment.", | |
| "You've been selected as the lucky winner of a million-dollar lottery. Reply to claim.", | |
| "Limited time offer: Double your money with our amazing investment opportunity.", | |
| "Hi, just checking in to see how you're doing. Let's catch up soon.", | |
| "Reminder: Your dentist appointment is scheduled for tomorrow at 2 PM.", | |
| "Invitation: Join us for a webinar on digital marketing strategies. Register now!", | |
| "Your application for the scholarship has been reviewed. We're pleased to inform you that you've been selected.", | |
| "Hi there! Just wanted to check in and see how you're doing.", | |
| "Reminder: Your friend's birthday is coming up. Don't forget to send them a message.", | |
| "Thank you for your purchase. Your order has been successfully processed.", | |
| "Your monthly newsletter is here! Stay updated with the latest news and updates.", | |
| "Invitation: Join us for a community clean-up event this weekend. Let's make a difference together.", | |
| "Reminder: Your scheduled appointment is tomorrow. We look forward to seeing you.", | |
| "Good news! You've earned a reward for your loyalty. Check your account for details.", | |
| "Your recent transaction has been approved. Please keep this email for your records.", | |
| "Exciting announcement: Our new store location is now open. Visit us and receive a special discount.", | |
| "Welcome to our online community! Here's how to get started and connect with others.", | |
| "Your request has been received and is being processed. We'll update you with the status soon.", | |
| "Upcoming event: Join us for a free cooking class this Saturday. Learn new recipes and techniques.", | |
| "Reminder: Don't forget to vote in the upcoming election. Your voice matters.", | |
| "Join our book club and dive into a world of fascinating stories. Here's how to join.", | |
| ] | |
| # Create Gradio components | |
| input_text = gr.Textbox( | |
| lines=3, label="Enter the SMS/Message/Email you received", autofocus=True | |
| ) | |
| output_text = gr.HTML("", label="Output") | |
| probabilities_text = gr.Label("", label="Probabilities") | |
| random.shuffle(examples) | |
| # Initialize the Gradio interface | |
| model_gui = gr.Interface( | |
| fn=process_input, | |
| inputs=input_text, | |
| outputs=[probabilities_text, output_text], | |
| title=title, | |
| examples=examples, | |
| interpretation="default", | |
| theme="shivi/calm_seafoam", | |
| css="""*{font-family:'IBM Plex Mono';}""", | |
| examples_per_page=15, | |
| ) | |
| # Launch the Gradio interface | |
| print("add '/?__theme=dark' to URL for rendering in dark theme.") | |
| model_gui.launch() | |