Update app.py

app.py

@@ -1,67 +1,67 @@
-import torch
-from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
-import gradio as gr
-import re
-import nltk
-from nltk.tokenize import word_tokenize
-from nltk.corpus import stopwords
-from nltk.stem import WordNetLemmatizer
-
-# Download NLTK resources (optional if already available)
-nltk.download('punkt_tab')
-nltk.download('stopwords')
-nltk.download('wordnet')
-
-# Preprocessing setup
-stop_words = set(stopwords.words('english'))
-lemmatizer = WordNetLemmatizer()
-
-def preprocess_text(text):
-    # Remove non-alphabetic characters
-    text = re.sub(r'[^A-Za-z\s]', '', text)
-    # Remove URLs
-    text = re.sub(r'http\S+|www\S+|https\S+', '', text)
-    # Remove extra spaces
-    text = re.sub(r'\s+', ' ', text).strip()
-    # Lowercase
-    text = text.lower()
-    # Tokenize
-    tokens = word_tokenize(text)
-    # Remove stopwords
-    tokens = [word for word in tokens if word not in stop_words]
-    # Lemmatize
-    tokens = [lemmatizer.lemmatize(word) for word in tokens]
-    return ' '.join(tokens)
-
-# Load tokenizer and model
-tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
-model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
-
-# Load trained phishing detection model
-model.load_state_dict(torch.load("phishing_model.pth", map_location=torch.device("cpu")))
-model.eval()
-
-# Label mapping
-idx2label = {0: "phishing", 1: "legitimate"}
-
-# Prediction function
-def predict(text):
-    clean_text = preprocess_text(text)
-    inputs = tokenizer(clean_text, return_tensors="pt", truncation=True, padding=True, max_length=128)
-    with torch.no_grad():
-        outputs = model(**inputs)
-        probs = torch.nn.functional.softmax(outputs.logits, dim=1)[0].numpy()
-
-    return {idx2label[i]: float(round(probs[i], 4)) for i in range(2)}
-
-# Gradio UI
-interface = gr.Interface(
-    fn=predict,
-    inputs=gr.Textbox(lines=4, placeholder="Enter a suspicious message or account description..."),
-    outputs=gr.Label(num_top_classes=2),
-    title="🛡️ Phishing Account Detector",
-    description="Detects whether an account or message is likely phishing or legitimate using a custom DistilBERT model."
-)
-
-if __name__ == "__main__":
-    interface.launch()
+import torch
+from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+import gradio as gr
+import re
+import nltk
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+
+# Download NLTK resources (optional if already available)
+nltk.download('punkt_tab')
+nltk.download('stopwords')
+nltk.download('wordnet')
+
+# Preprocessing setup
+stop_words = set(stopwords.words('english'))
+lemmatizer = WordNetLemmatizer()
+
+def preprocess_text(text):
+    # Remove non-alphabetic characters
+    text = re.sub(r'[^A-Za-z\s]', '', text)
+    # Remove URLs
+    text = re.sub(r'http\S+|www\S+|https\S+', '', text)
+    # Remove extra spaces
+    text = re.sub(r'\s+', ' ', text).strip()
+    # Lowercase
+    text = text.lower()
+    # Tokenize
+    tokens = word_tokenize(text)
+    # Remove stopwords
+    tokens = [word for word in tokens if word not in stop_words]
+    # Lemmatize
+    tokens = [lemmatizer.lemmatize(word) for word in tokens]
+    return ' '.join(tokens)
+
+# Load tokenizer and model
+tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
+model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=2)
+
+# Load trained phishing detection model
+model.load_state_dict(torch.load("best_model.pth", map_location=torch.device("cpu")))
+model.eval()
+
+# Label mapping
+idx2label = {0: "phishing", 1: "legitimate"}
+
+# Prediction function
+def predict(text):
+    clean_text = preprocess_text(text)
+    inputs = tokenizer(clean_text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=1)[0].numpy()
+
+    return {idx2label[i]: float(round(probs[i], 4)) for i in range(2)}
+
+# Gradio UI
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=4, placeholder="Enter a suspicious message or account description..."),
+    outputs=gr.Label(num_top_classes=2),
+    title="🛡️ Phishing Account Detector",
+    description="Detects whether an account or message is likely phishing or legitimate using a custom DistilBERT model."
+)
+
+if __name__ == "__main__":
+    interface.launch()