Create app.py

app.py

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+import gradio as gr
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+import torch
+
+# Load Model & Tokenizer
+model_name = "AventIQ-AI/distilbert-spam-detection"
+tokenizer = DistilBertTokenizer.from_pretrained(model_name)
+model = DistilBertForSequenceClassification.from_pretrained(model_name)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+def predict_spam(text):
+    model.eval()
+    inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128).to(device)
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=-1)
+        pred_class = torch.argmax(probs).item()
+    
+    return "🚨 Spam" if pred_class == 1 else "✅ Not Spam"
+
+# Create Gradio Interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🚀 AI-Powered Spam Detector")
+    gr.Markdown("Enter a message below to check if it's spam or not!")
+    
+    with gr.Row():
+        input_box = gr.Textbox(placeholder="Type a message here...", lines=2)
+        output_label = gr.Label()
+    
+    button = gr.Button("🔍 Analyze")
+    button.click(predict_spam, inputs=input_box, outputs=output_label)
+
+# Launch
+if __name__ == "__main__":
+    demo.launch()