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app.py

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+import gradio as gr
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+
+# Load the tokenizer and model
+model_name = "AventIQ-AI/bert-spam-detection"
+tokenizer = BertTokenizer.from_pretrained(model_name)
+model = BertForSequenceClassification.from_pretrained(model_name)
+
+# Set the model to evaluation mode and move it to the appropriate device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+# Define the prediction function
+def predict_spam(text):
+    """Predicts whether a given text is spam or not."""
+    # Tokenize input text
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    inputs = {key: value.to(device) for key, value in inputs.items()}
+
+    # Perform inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probabilities = torch.softmax(logits, dim=1)
+        prediction = torch.argmax(probabilities, dim=1).item()
+        confidence = probabilities[0][prediction].item()
+
+    # Map prediction to label
+    label_map = {0: "Not Spam", 1: "Spam"}
+    result = f"Prediction: {label_map[prediction]}\nConfidence: {confidence:.2f}"
+    return result
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_spam,
+    inputs=gr.Textbox(label="📧 Input Text", placeholder="Enter the email or message content here...", lines=5),
+    outputs=gr.Textbox(label="🔍 Spam Detection Result"),
+    title="🛡️ BERT-Based Spam Detector",
+    description="Enter the content of an email or message to determine whether it's Spam or Not Spam.",
+    examples=[
+        ["Congratulations! You've won a $1,000,000 lottery. Click here to claim your prize."],
+        ["Hey, are we still meeting for lunch tomorrow?"],
+        ["URGENT: Your account has been compromised. Reset your password immediately by clicking this link."],
+        ["Don't miss out on our exclusive offer! Buy one, get one free on all items."],
+        ["Can you send me the report by end of the day? Thanks!"]
+    ],
+    theme="compact",
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+torch
+transformers
+gradio
+sentencepiece