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email_spam_calsifier.py

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+# -*- coding: utf-8 -*-
+"""Email_spam_calsifier
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1obsGCm8CluG_jKB59HlJAlyO5movzKD_
+
+# IMPORT LIBRARIES
+"""
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import pickle
+
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay
+
+"""#**LOAD DATA**"""
+
+df = pd.read_csv('/content/mail_data.csv')
+
+# Replace null values
+data = df.where((pd.notnull(df)), '')
+
+# Convert labels
+data.loc[data['Category'] == 'spam', 'Category'] = 0
+data.loc[data['Category'] == 'ham', 'Category'] = 1
+
+# Separate features and labels
+x = data['Message']
+y = data['Category'].astype('int')
+
+"""#**TRAIN TEST SPLIT**
+
+"""
+
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=3)
+
+"""#**TF-IDF**"""
+
+tfidf = TfidfVectorizer(min_df=1, stop_words='english', lowercase=True)
+
+x_train_features = tfidf.fit_transform(x_train)
+x_test_features = tfidf.transform(x_test)
+
+"""#**SVM MODEL**"""
+
+model = SVC()
+
+model.fit(x_train_features, y_train)
+
+"""#**PREDICTIONS**"""
+
+train_pred = model.predict(x_train_features)
+test_pred = model.predict(x_test_features)
+
+"""#**EVALUATION**"""
+
+train_acc = accuracy_score(y_train, train_pred)
+test_acc = accuracy_score(y_test, test_pred)
+
+precision = precision_score(y_test, test_pred)
+recall = recall_score(y_test, test_pred)
+f1 = f1_score(y_test, test_pred)
+
+print("Training Accuracy:", train_acc)
+print("Testing Accuracy:", test_acc)
+print("Precision:", precision)
+print("Recall:", recall)
+print("F1 Score:", f1)
+
+"""#**SAMPLE TEST**"""
+
+sample = ["claim your free gift card today"]
+
+sample_vec = tfidf.transform(sample)
+result = model.predict(sample_vec)
+
+print("\nPrediction:", "Ham" if result[0] == 1 else "Spam")
+
+"""#**SAVE MODEL**"""
+
+pickle.dump(model, open("email_model.pkl", "wb"))
+pickle.dump(tfidf, open("email_vectorizer.pkl", "wb"))
+
+from google.colab import files
+files.download("email_model.pkl")
+files.download("email_vectorizer.pkl")
+
+"""#**BAR CHART**"""
+
+plt.figure()
+data['Category'].value_counts().plot(kind='bar')
+plt.title("Spam vs Ham Emails")
+plt.xlabel("Category (0=Spam, 1=Ham)")
+plt.ylabel("Count")
+plt.savefig("bar_chart.png")
+plt.show()
+
+"""#**CONFUSION MATRIX**"""
+
+cm = confusion_matrix(y_test, test_pred)
+disp = ConfusionMatrixDisplay(confusion_matrix=cm)
+disp.plot()
+plt.savefig("confusion_matrix.png")
+plt.show()
+
+"""#**ACCURACY COMPARISON**"""
+
+plt.figure()
+plt.bar(['Train Accuracy', 'Test Accuracy'], [train_acc, test_acc])
+plt.title("Train vs Test Accuracy")
+plt.savefig("accuracy_graph.png")
+plt.show()
+
+"""#**DOWNLOAD GRAPHS**"""
+
+files.download("bar_chart.png")
+files.download("confusion_matrix.png")
+files.download("accuracy_graph.png")
+
+!pip install streamlit pyngrok
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%writefile app.py
+# import streamlit as st
+# import pickle
+# 
+# model = pickle.load(open("email_model.pkl", "rb"))
+# tfidf = pickle.load(open("email_vectorizer.pkl", "rb"))
+# 
+# st.title("Email Spam Detection App")
+# 
+# user_input = st.text_area("Type your email here:")
+# 
+# if st.button("Predict"):
+#     if user_input.strip() == "":
+#         st.warning("Please enter some text!")
+#     else:
+#         input_features = tfidf.transform([user_input])
+#         prediction = model.predict(input_features)
+#         st.success("Ham Email " if prediction[0]==1 else "Spam Email ")
+
+!npm install -g localtunnel
+
+!streamlit run app.py & npx localtunnel --port 8501