added code

.gitignore

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+.env
+venv/

app.py

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+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}")

requirements.txt

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+streamlit
+pandas
+joblib
+scikit-learn