added app.py

app.py

@@ -0,0 +1,135 @@
+import joblib
+import gradio as gr
+import re
+
+
+
+model = joblib.load("./phishing_model.pkl")
+vectorizer = joblib.load("./phishing_tfidf_vectorizer.pkl")
+
+
+def text_clean(text):
+    if not isinstance(text, str):
+        return ""
+
+    text = text.lower()
+    text = re.sub(r"<.*?>", " ", text)
+    text = re.sub(r"https?://\S+|www\.\S+", " url ", text)
+    text = re.sub(r"\b\d{7,}\b", " ", text)
+    text = re.sub(r"[^a-z0-9@.$%\-\s]", " ", text)
+    text = re.sub(r"\s+", " ", text).strip()
+
+    return text
+
+
+def predict(email):
+    cleaned = text_clean(email)
+    vector = vectorizer.transform([cleaned])
+
+    prediction = model.predict(vector)[0]
+    probabilities = model.predict_proba(vector)[0]
+
+    label = "Phishing" if prediction == 1 else "Legitimate"
+
+    return (
+        label,
+        f"{probabilities[prediction] * 100:.2f}%",
+        {
+            "Legitimate": float(probabilities[0]),
+            "Phishing": float(probabilities[1]),
+        },
+    )
+
+
+examples = {
+    "Legitimate - Meeting": """Hi Aditya,
+
+Can we meet tomorrow at 3 PM to discuss the internship project?
+
+Thanks,
+Rahul""",
+
+    "Legitimate - GitHub": """GitHub
+
+Your pull request has been successfully merged into the main branch.
+
+View changes:
+https://github.com/example/repo""",
+
+    "Phishing - Bank": """support@secure-bank-login.xyz
+secure-bank-login.xyz
+
+Dear Customer,
+
+Your account has been temporarily suspended.
+
+Click below immediately to verify your identity.
+
+https://secure-bank-login.xyz/login""",
+
+    "Phishing - PayPal": """service@paypal-security.xyz
+paypal-security.xyz
+
+We've detected unusual activity on your PayPal account.
+
+Verify your account within 24 hours or it will be permanently limited.
+
+https://paypal-security.xyz"""
+}
+
+
+def load_example(choice):
+    return examples[choice]
+
+
+with gr.Blocks(title="Email Phishing Detector") as demo:
+
+    gr.Markdown(
+        """
+        # Email Phishing Detector
+
+        Detect whether an email is **Legitimate** or **Phishing** using a Multinomial Naive Bayes model trained on TF-IDF features.
+        """
+    )
+
+    with gr.Row():
+
+        with gr.Column(scale=3):
+            email_input = gr.Textbox(
+                label="Email Content",
+                lines=18,
+                placeholder="Paste the complete email here..."
+            )
+
+            predict_btn = gr.Button("Predict", variant="primary")
+
+        with gr.Column(scale=2):
+            prediction = gr.Textbox(label="Prediction")
+            confidence = gr.Textbox(label="Confidence")
+            probabilities = gr.Label(label="Class Probabilities")
+
+    gr.Markdown("### Try an Example")
+
+    example_dropdown = gr.Dropdown(
+        choices=list(examples.keys()),
+        value=list(examples.keys())[0],
+        label="Example Emails"
+    )
+
+    example_dropdown.change(
+        load_example,
+        inputs=example_dropdown,
+        outputs=email_input
+    )
+
+    predict_btn.click(
+        predict,
+        inputs=email_input,
+        outputs=[
+            prediction,
+            confidence,
+            probabilities
+        ]
+    )
+
+demo.launch()