app.py

import streamlit as st
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline
import os

# Fix protobuf compatibility issue
os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"

# Load Dataset
df = pd.read_csv("spam.csv", encoding='latin-1')
df = df[['v1', 'v2']]
df.columns = ['label', 'message']

df['label'] = df['label'].map({'ham': 0, 'spam': 1})

# Tabs Navigation
tabs = st.tabs(["Overview", "Dataset & Training", "Spam Detection"])

with tabs[0]:  # Overview
    st.title("Spam Email Classifier")
    st.write("""
    This app classifies emails/messages as **Spam** or **Ham** using a **Naïve Bayes Classifier**.
    
    The dataset used for training consists of labeled SMS messages.
    """)

with tabs[1]:  # Dataset & Training
    st.title("Dataset & Training")
    st.write("### Sample Data")
    st.dataframe(df.head())
    
    st.write("### Dataset Statistics")
    st.write(df.describe())
    
    st.write("### Class Distribution")
    fig, ax = plt.subplots()
    sns.countplot(x='label', data=df, ax=ax)
    st.pyplot(fig)
    
    # Train Model
    X = df['message']
    y = df['label']
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    
    model = make_pipeline(TfidfVectorizer(), MultinomialNB())
    model.fit(X_train, y_train)

with tabs[2]:  # Spam Detection
    st.title("Spam Detection")
    
    st.sidebar.header("Enter your message:")
    user_input = st.sidebar.text_area("Type your email/message here:")
    
    if st.sidebar.button("Classify"):
        prediction = model.predict([user_input])[0]
        result = "Spam" if prediction == 1 else "Ham"
        st.write("### Classification Result")
        st.success(f"The message is classified as: {result}")