src/streamlit_app.py

import streamlit as st
import pandas as pd
import string
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix

# ----------------- STREAMLIT CONFIG -----------------
st.set_page_config(page_title="📧 Email Spam Detector", layout="centered")
st.title("📧 Email Spam Detector")
st.markdown("This app uses **Machine Learning** (Naive Bayes + TF-IDF) to classify emails as **Spam** or **Ham (Not Spam)**.")

# ----------------- DATA LOADING -----------------
@st.cache_data
def load_data(file):
    df = pd.read_csv(file, encoding='latin-1')[['v1', 'v2']]
    df.columns = ['label', 'message']
    df['label'] = df['label'].map({'ham': 0, 'spam': 1})
    return df

# File uploader for user dataset
st.subheader("📂 Upload Dataset")
uploaded_file = st.file_uploader("Upload your spam dataset (CSV format)", type=["csv"])

if uploaded_file is not None:
    df = load_data(uploaded_file)
    st.success("✅ Dataset loaded successfully from uploaded file.")
else:
    st.info("ℹ️ No file uploaded. Using default dataset (spam.csv).")
    df = load_data("spam.csv")

# ----------------- PREPROCESS FUNCTION -----------------
def clean_text(text):
    text = text.lower().strip()
    text = text.translate(str.maketrans("", "", string.punctuation))
    return text

df['message'] = df['message'].apply(clean_text)

# ----------------- TRAIN / TEST SPLIT -----------------
X_train, X_test, y_train, y_test = train_test_split(
    df['message'], df['label'], test_size=0.2, random_state=42
)

# ----------------- TF-IDF VECTORIZATION -----------------
vectorizer = TfidfVectorizer(stop_words='english')
X_train_tfidf = vectorizer.fit_transform(X_train)
X_test_tfidf = vectorizer.transform(X_test)

# ----------------- MODEL TRAINING -----------------
model = MultinomialNB()
model.fit(X_train_tfidf, y_train)

# ----------------- METRICS -----------------
y_pred = model.predict(X_test_tfidf)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)

# ----------------- SIDEBAR METRICS -----------------
st.sidebar.header("🔍 Model Performance")
st.sidebar.write(f"**Accuracy:** {accuracy:.2%}")
st.sidebar.write(f"**Precision:** {precision:.2%}")
st.sidebar.write(f"**Recall:** {recall:.2%}")
st.sidebar.write(f"**F1 Score:** {f1:.2%}")
st.sidebar.markdown("Model: `Multinomial Naive Bayes`  \nVectorizer: `TF-IDF`")

# Confusion Matrix
cm = confusion_matrix(y_test, y_pred)
fig, ax = plt.subplots()
sns.heatmap(cm, annot=True, fmt='d', cmap="Blues", xticklabels=["Ham", "Spam"], yticklabels=["Ham", "Spam"])
plt.ylabel('Actual')
plt.xlabel('Predicted')
st.sidebar.pyplot(fig)

# ----------------- PREDICT FUNCTION -----------------
def predict_message(msg):
    msg_clean = clean_text(msg)
    vect_msg = vectorizer.transform([msg_clean])
    pred = model.predict(vect_msg)[0]
    prob = model.predict_proba(vect_msg)[0][pred]
    return ("🚫 Spam", prob) if pred == 1 else ("✅ Ham (Not Spam)", prob)

# ----------------- USER INPUT -----------------
st.subheader("✉️ Test Your Message")
user_input = st.text_area("Enter your email message here:")

if st.button("Detect"):
    if user_input.strip() == "":
        st.warning("⚠️ Please enter a message to classify.")
    else:
        result, confidence = predict_message(user_input)
        st.success(f"Prediction: **{result}**  \nConfidence: **{confidence:.2%}**")

# ----------------- SAMPLE DATA -----------------
with st.expander("📂 View Sample Dataset"):
    st.dataframe(df.sample(10))