Delete main.py

main.py

@@ -1,54 +0,0 @@
-import pandas as pd
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.model_selection import train_test_split
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.metrics import accuracy_score
-import nltk
-from nltk.corpus import stopwords
-import re
-
-# Download NLTK stopwords
-nltk.download('stopwords')
-
-# Load dataset
-# Assuming you have a CSV file with 'url' and 'label' columns
-data = pd.read_csv('malicious_phish.csv')
-
-# Preprocess URLs
-def preprocess_url(url):
-    url = re.sub(r"http\S+", "", url)  # Remove http links
-    url = re.sub(r"\d+", "", url)      # Remove digits
-    url = re.sub(r"\W", " ", url)      # Remove non-word characters
-    url = url.lower()                  # Convert to lowercase
-    return url
-
-data['url'] = data['url'].apply(preprocess_url)
-
-# Split data into training and testing sets
-X = data['url']
-y = data['type']
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-# Vectorize URLs using TF-IDF
-vectorizer = TfidfVectorizer(stop_words=stopwords.words('english'))
-X_train_tfidf = vectorizer.fit_transform(X_train)
-X_test_tfidf = vectorizer.transform(X_test)
-
-# Train a Naive Bayes classifier
-model = MultinomialNB()
-model.fit(X_train_tfidf, y_train)
-
-# Predict and evaluate
-y_pred = model.predict(X_test_tfidf)
-accuracy = accuracy_score(y_test, y_pred)
-print(f"Accuracy: {accuracy * 100:.2f}%")
-
-# Function to predict if a URL is malicious
-def predict_url(url):
-    processed_url = preprocess_url(url)
-    vectorized_url = vectorizer.transform([processed_url])
-    prediction = model.predict(vectorized_url)
-    return prediction[0]
-
-# Example usage
-print(predict_url("br-icloud.com.br"))