Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline, set_seed
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+# Set seed and define model parameters
+set_seed(42)
+checkpoint = 'bgspaditya/malurl-electra-10e'
+id2label = {0:'benign',1:'defacement',2:'malware',3:'phishing'}
+label2id = {'benign':0,'defacement':1,'malware':2,'phishing':3}
+num_labels=4
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(checkpoint, use_fast=True, force_download=True)
+model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=num_labels, id2label=id2label, label2id=label2id, force_download=True)
+
+# Define predict function
+def predict(url):
+    url_classifier = pipeline(task='text-classification', model=model, tokenizer=tokenizer)
+    result = url_classifier(url)
+    predicted_label = result[0]['label']
+    return predicted_label
+
+# Define Gradio interface
+gradio_app = gr.Interface(
+    predict,
+    inputs=gr.Textbox(label="Enter URL"),
+    outputs=gr.Label(label="Result"),
+    title="Phishing URL Detection",
+)
+
+# Launch the Gradio interface
+if __name__ == "__main__":
+    gradio_app.launch()