Update app.py

app.py

@@ -1,154 +1,154 @@
-import gradio as gr
-import re
-from urllib.parse import urlparse
-import csv
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
-import torch
-#URL model
-url_tokenizer = AutoTokenizer.from_pretrained("najla45/phishing_detection_fine_tuned_bert")
-url_model = AutoModelForSequenceClassification.from_pretrained("najla45/phishing_detection_fine_tuned_bert")
-url_classifier = pipeline("text-classification", model=url_model, tokenizer=url_tokenizer)
-
-#email model
-email_tokenizer = AutoTokenizer.from_pretrained("cybersectony/phishing-email-detection-distilbert_v2.4.1")
-email_model = AutoModelForSequenceClassification.from_pretrained("cybersectony/phishing-email-detection-distilbert_v2.4.1")
-
-#logic for checking the state of url
-def is_phishing_url(url):
-    suspicious_keywords = ['secure', 'account', 'update', 'free', 'login', 'verify', 'banking']
-    domain = urlparse(url).netloc
-    path = urlparse(url).path
-
-    score = 0
-    if re.match(r'https?://\d{1,3}(\.\d{1,3}){3}', url):
-        score += 2
-    if '-' in domain:
-        score += 1
-    if not url.startswith("https://"):
-        score += 3
-    if any(keyword in url.lower() for keyword in suspicious_keywords):
-        score += 2
-    if len(url) > 75:
-        score += 1
-    if '@' in url:
-        score += 2
-
-    return score
-
-#logic checking for phishing email
-def predict_email(email_text):
-    inputs = email_tokenizer(email_text, return_tensors="pt", truncation=True, max_length=512)
-    with torch.no_grad():
-        outputs = email_model(**inputs)
-        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)[0].tolist()
-
-    labels = {
-        "legitimate_email": probs[0],
-        "phishing_url": probs[1],
-        "legitimate_url": probs[2],
-        "phishing_url_alt": probs[3]
-    }
-
-    max_label, max_score = max(labels.items(), key=lambda x: x[1])
-    return max_label, max_score, labels
-
-
-#LOGGING ALL DATA TO CSV FILE
-import os
-LOG_FILE = os.path.join(os.path.dirname(__file__), "phishing_log.csv")
-def log_to_csv(url, rule_score, bert_label, bert_score, final_decision):
-    try:
-        file_exists = os.path.isfile(LOG_FILE)
-        with open(LOG_FILE, "a", newline='') as f:
-            writer = csv.writer(f)
-            if not file_exists:
-                writer.writerow(["Input", "Rule Score", "BERT Label", "Confidence", "Final Decision"])
-            writer.writerow([url, rule_score, bert_label, f"{bert_score:.2f}", final_decision])
-    except Exception as e:
-        print(f"Error writing to CSV: {e}")
-
-
-#Combining URL and email checking logic
-def combined_phishing_detector(url, input_type, log=True):
-    if input_type == "URL":
-        rule_score = is_phishing_url(url)
-        rule_result = "Phishing" if rule_score >= 3 else "Safe"
-
-        bert_result = url_classifier(url)[0]
-        label_map = {"LABEL_0": "safe", "LABEL_1": "phishing"}
-        bert_label = label_map.get(bert_result["label"].upper(), "unknown")
-        bert_score = bert_result["score"]
-
-        final_decision = "Phishing" if rule_result == "Phishing" and bert_label == "phishing" and bert_score > 0.75 else "Safe"
-
-    elif input_type == "Email/Message":
-        bert_label, bert_score, bert_probs = predict_email(url)
-        rule_score = "N/A"
-        rule_result = "Not Applicable"
-        final_decision = "Phishing" if bert_label.startswith("phishing") and bert_score > 0.7 else "Safe"
-
-    # ✅ This part was missing in your message
-    if log:
-        log_to_csv(url, rule_score, bert_label, bert_score, final_decision)
-
-    return url, rule_score, bert_label, bert_score, final_decision
-
-
-
-def run_detector(text, input_type):
-    url,rule_score, bert_label, bert_score,final_decision = combined_phishing_detector(text, input_type,log=True)
-    
-    # Add emoji based on result
-    if final_decision.lower() == "phishing":
-        emoji =  "🚨"   # warning
-    elif final_decision.lower() == "safe":
-        emoji = "✅"  # check mark
-    else:
-        emoji = "❓"
-
-    message = (
-        f"{emoji} Result: {final_decision}\n"
-        f"📊 Rule Score: {rule_score}\n"
-        f"🤖 BERT Label: {bert_label}\n"
-        f"🔍 Confidence: {bert_score:.2f}"
-    )
-    return message,LOG_FILE
-
-#---GUI-----
-gr.HTML("""
-<link href="https://fonts.googleapis.com/css2?family=Poppins:wght@400;500;600;700&display=swap" rel="stylesheet">
-<h1 style='text-align:center; color:white; font-family: "Poppins", sans-serif;'>🔐 Phishing URL & Email Detector (BERT + Rules) 🔐</h1>
-""")
-
-with gr.Blocks(css="""
-.gradio-container {
-    background-image: url('https://c8.alamy.com/comp/M79X4X/cyber-security-buzzwords-phishing-alert-with-blue-numbers-in-background-M79X4X.jpg');
-    background-size: cover;
-    background-position: center;
-    background-repeat: no-repeat;
-    font-family: 'Poppins', sans-serif;
-    color: white;
-}
-input, textarea, button, label, .gr-box, .gr-button, .gr-textbox, .gr-radio, .gr-file {
-    font-family: 'Poppins', sans-serif !important;
-    color: white;
-}
-""") as demo:
-
-    gr.HTML("<h1 style='text-align:center; color:white;'>🔐 Phishing URL & Email Detector (BERT + Rules) 🔐</h1>")
-
-    with gr.Row():
-        input_text = gr.Textbox(label="Enter URL or Email", lines=5)
-        input_type = gr.Radio(["URL", "Email/Message"], label="Input Type")
-
-    result_output = gr.Textbox(label="Detection Result", lines=4, interactive=False)
-    log_file_output = gr.File(label="Download Log File")
-
-    detect_button = gr.Button("Detect")
-
-    detect_button.click(fn=run_detector, inputs=[input_text, input_type], outputs=[result_output, log_file_output])
-
-    
-demo.launch(share=True)
-
-
+import gradio as gr
+import re
+from urllib.parse import urlparse
+import csv
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import torch
+#URL model
+url_tokenizer = AutoTokenizer.from_pretrained("najla45/phishing_detection_fine_tuned_bert")
+url_model = AutoModelForSequenceClassification.from_pretrained("najla45/phishing_detection_fine_tuned_bert")
+url_classifier = pipeline("text-classification", model=url_model, tokenizer=url_tokenizer)
+
+#email model
+email_tokenizer = AutoTokenizer.from_pretrained("cybersectony/phishing-email-detection-distilbert_v2.4.1")
+email_model = AutoModelForSequenceClassification.from_pretrained("cybersectony/phishing-email-detection-distilbert_v2.4.1")
+
+#logic for checking the state of url
+def is_phishing_url(url):
+    suspicious_keywords = ['secure', 'account', 'update', 'free', 'login', 'verify', 'banking']
+    domain = urlparse(url).netloc
+    path = urlparse(url).path
+
+    score = 0
+    if re.match(r'https?://\d{1,3}(\.\d{1,3}){3}', url):
+        score += 2
+    if '-' in domain:
+        score += 1
+    if not url.startswith("https://"):
+        score += 3
+    if any(keyword in url.lower() for keyword in suspicious_keywords):
+        score += 2
+    if len(url) > 75:
+        score += 1
+    if '@' in url:
+        score += 2
+
+    return score
+
+#logic checking for phishing email
+def predict_email(email_text):
+    inputs = email_tokenizer(email_text, return_tensors="pt", truncation=True, max_length=512)
+    with torch.no_grad():
+        outputs = email_model(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)[0].tolist()
+
+    labels = {
+        "legitimate_email": probs[0],
+        "phishing_url": probs[1],
+        "legitimate_url": probs[2],
+        "phishing_url_alt": probs[3]
+    }
+
+    max_label, max_score = max(labels.items(), key=lambda x: x[1])
+    return max_label, max_score, labels
+
+
+#LOGGING ALL DATA TO CSV FILE
+import os
+LOG_FILE = os.path.join(os.path.dirname(__file__), "phishing_log.csv")
+def log_to_csv(url, rule_score, bert_label, bert_score, final_decision):
+    try:
+        file_exists = os.path.isfile(LOG_FILE)
+        with open(LOG_FILE, "a", newline='') as f:
+            writer = csv.writer(f)
+            if not file_exists:
+                writer.writerow(["Input", "Rule Score", "BERT Label", "Confidence", "Final Decision"])
+            writer.writerow([url, rule_score, bert_label, f"{bert_score:.2f}", final_decision])
+    except Exception as e:
+        print(f"Error writing to CSV: {e}")
+
+
+#Combining URL and email checking logic
+def combined_phishing_detector(url, input_type, log=True):
+    if input_type == "URL":
+        rule_score = is_phishing_url(url)
+        rule_result = "Phishing" if rule_score >= 3 else "Safe"
+
+        bert_result = url_classifier(url)[0]
+        label_map = {"LABEL_0": "safe", "LABEL_1": "phishing"}
+        bert_label = label_map.get(bert_result["label"].upper(), "unknown")
+        bert_score = bert_result["score"]
+
+        final_decision = "Phishing" if rule_result == "Phishing" and bert_label == "phishing" and bert_score > 0.75 else "Safe"
+
+    elif input_type == "Email/Message":
+        bert_label, bert_score, bert_probs = predict_email(url)
+        rule_score = "N/A"
+        rule_result = "Not Applicable"
+        final_decision = "Phishing" if bert_label.startswith("phishing") and bert_score > 0.7 else "Safe"
+
+    
+    if log:
+        log_to_csv(url, rule_score, bert_label, bert_score, final_decision)
+
+    return url, rule_score, bert_label, bert_score, final_decision
+
+
+
+def run_detector(text, input_type):
+    url,rule_score, bert_label, bert_score,final_decision = combined_phishing_detector(text, input_type,log=True)
+    
+    # Add emoji based on result
+    if final_decision.lower() == "phishing":
+        emoji =  "🚨"   # warning
+    elif final_decision.lower() == "safe":
+        emoji = "✅"  # check mark
+    else:
+        emoji = "❓"
+
+    message = (
+        f"{emoji} Result: {final_decision}\n"
+        f"📊 Rule Score: {rule_score}\n"
+        f"🤖 BERT Label: {bert_label}\n"
+        f"🔍 Confidence: {bert_score:.2f}"
+    )
+    return message,LOG_FILE
+
+#---GUI-----
+gr.HTML("""
+<link href="https://fonts.googleapis.com/css2?family=Poppins:wght@400;500;600;700&display=swap" rel="stylesheet">
+<h1 style='text-align:center; color:white; font-family: "Poppins", sans-serif;'>🔐 Phishing URL & Email Detector (BERT + Rules) 🔐</h1>
+""")
+
+with gr.Blocks(css="""
+.gradio-container {
+    background-image: url('https://c8.alamy.com/comp/M79X4X/cyber-security-buzzwords-phishing-alert-with-blue-numbers-in-background-M79X4X.jpg');
+    background-size: cover;
+    background-position: center;
+    background-repeat: no-repeat;
+    font-family: 'Poppins', sans-serif;
+    color: white;
+}
+input, textarea, button, label, .gr-box, .gr-button, .gr-textbox, .gr-radio, .gr-file {
+    font-family: 'Poppins', sans-serif !important;
+    color: white;
+}
+""") as demo:
+
+    gr.HTML("<h1 style='text-align:center; color:white;'>🔐 Phishing URL & Email Detector (BERT + Rules) 🔐</h1>")
+
+    with gr.Row():
+        input_text = gr.Textbox(label="Enter URL or Email", lines=5)
+        input_type = gr.Radio(["URL", "Email/Message"], label="Input Type")
+
+    result_output = gr.Textbox(label="Detection Result", lines=4, interactive=False)
+    log_file_output = gr.File(label="Download Log File")
+
+    detect_button = gr.Button("Detect")
+
+    detect_button.click(fn=run_detector, inputs=[input_text, input_type], outputs=[result_output, log_file_output])
+
+    
+demo.launch(share=True)
+
+