Upload app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load a public model for general text classification (no auth required)
+classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
+
+def detect_phishing(email_text):
+    try:
+        result = classifier(email_text[:512])[0]
+        label = result["label"]
+        score = result["score"]
+
+        # Simple logic to flag phishing-like emails
+        phishing_keywords = ["account", "bank", "verify", "password", "login", "update", "click", "urgent", "confirm"]
+        email_lower = email_text.lower()
+
+        if any(word in email_lower for word in phishing_keywords) or score < 0.6:
+            return f"⚠️ Phishing Detected (Confidence: {score:.2f}) — Reason: Suspicious keywords found"
+        else:
+            return f"✅ Safe Email (Confidence: {score:.2f})"
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=detect_phishing,
+    inputs=gr.Textbox(lines=10, label="Paste Email Content Here"),
+    outputs=gr.Textbox(label="Prediction Result"),
+    title="🧠 AI-Powered Phishing Email Detector",
+    description="Detects phishing emails based on text classification and keyword intelligence."
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)