Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Load the fine-tuned model and tokenizer
+MODEL_PATH = "D:/Phishing/model"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Function to predict spam
+def predict_spam(text):
+    # 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()}
+
+    # Get model predictions
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Convert logits to label
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=1).item()
+    
+    # Interpret the prediction
+    return "Spam" if predicted_class == 1 else "Not Spam"
+
+# Gradio UI
+iface = gr.Interface(
+    fn=predict_spam,
+    inputs=gr.Textbox(label="Enter Email Content"),
+    outputs=gr.Label(label="Spam Detection Result"),
+    title="📧 Spam Email Detector",
+    description="Paste an email body and get a spam classification prediction using a Transformer model."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()