add all files

app.py

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+import streamlit as st
+import pickle
+
+# EX :- win big now | win free urgent offer limited limited urgent urgent free beyond baby physical environmental none meeting foreign low | unknownmail.cc
+# EX :- project update | team sync president series today already involve lose control brother issue week blood firm personal let next | company.com
+
+
+# Page configuration
+st.set_page_config(page_title="Email Spam Detector", page_icon="📧")
+
+# Custom CSS for styling
+st.markdown("""
+    <style>
+    .main {
+        background-color: #f0f2f6;
+    }
+    .stButton>button {
+        width: 100%;
+        border-radius: 5px;
+        height: 3em;
+        background-color: #ff4b4b;
+        color: white;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Loading the model and vectorizer
+try:
+    model = pickle.load(open('spam_model.pkl', 'rb'))
+    vectorizer = pickle.load(open('vectorizer.pkl', 'rb'))
+except FileNotFoundError:
+    st.error("Error: 'spam_model.pkl' ya 'vectorizer.pkl' file nahi mili. Pehle model train karke save karein.")
+
+# UI Header
+st.title("📧 Email Spam Classifier")
+st.write("Apna email subject aur text niche enter karein check karne ke liye.")
+
+# Input Section
+with st.container():
+    domain = st.text_input("Email Domain", placeholder="Write your email domain...")
+    subject = st.text_input("Subject", placeholder="E.g. Congratulations! You won a prize")
+    message = st.text_area("Email Content", placeholder="Write your email body here...", height=150)
+
+# Prediction Logic
+if st.button("Predict Now"):
+    if message.strip() == "":
+        st.warning("Please enter the email text to analyze.")
+    else:
+        # Combine subject and message (Common practice in spam detection)
+        full_text = subject + " " + message + " " + domain
+        
+        # 1. Preprocess/Transform using vectorizer
+        data = vectorizer.transform([full_text])
+        
+        # 2. Prediction
+        prediction = model.predict(data)[0]
+        
+        # 3. Display Result
+        st.divider()
+        if prediction == 1: # Assuming 1 is Spam
+            st.error("🚨 This is a SPAM email!")
+        else:
+            st.success("✅ This is a HAM (Safe) email.")
+
+# Footer
+st.caption("Built with Python & Streamlit")

requirements.txt

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+streamlit
+numpy
+scikit-learn
+pandas
+
+
+
+
+# pickle.dump(model, open('spam_model.pkl', 'wb'))
+# pickle.dump(tfidf, open('vectorizer.pkl', 'wb'))
+# pickle.dump(scaler, open('scaler.pkl', 'wb'))

spam_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:83383f95a37119db5a3827448067bc091aa240f4752df799031b83d302bde1ad
+size 2397440

vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9582049e282ba16c83b7c967e8f9f4fc450f4555d613bd667811e4f2fa966188
+size 2016032