Update app.py

app.py

@@ -1,46 +1,27 @@
-import pandas as pd
-import numpy as np
-from sklearn.naive_bayes import MultinomialNB
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import classification_report
-import gradio as gr
-
-def load_data():
-    df = pd.read_csv("spam.csv", encoding="latin-1")
-    df = df[['v1', 'v2']]
-    df.columns = ['label', 'message']
-    df['spam'] = df['label'].apply(lambda x: 1 if x == 'spam' else 0)
-    return df
-
-def train_model(df):
-    X_train, X_test, y_train, y_test = train_test_split(df.message, df.spam, test_size=0.2, random_state=42)
-
-    vectorizer = CountVectorizer()
-    X_train_cv = vectorizer.fit_transform(X_train)
-    X_test_cv = vectorizer.transform(X_test)
-
-    model = MultinomialNB()
-    model.fit(X_train_cv, y_train)
-
-    y_pred = model.predict(X_test_cv)
-    print("Model performance on test set:")
-    print(classification_report(y_test, y_pred))
-
-    return model, vectorizer
-
-def predict_spam(email_text):
-    email_count = vectorizer.transform([email_text])
-    prediction = model.predict(email_count)[0]
-    return "Spam ❌" if prediction == 1 else "Not Spam"
-df = load_data()
-model, vectorizer = train_model(df)
-
-interface = gr.Interface(
-    fn=predict_spam,
-    inputs=gr.Textbox(lines=5, placeholder="Paste your email text here..."),
-    outputs="text",
-    title="Email Spam Detector",
-    description="A machine learning model using Naive Bayes to detect whether an email is spam or not."
-)
-interface.launch()
+from flask import Flask, render_template, request
+import joblib
+
+app = Flask(__name__)
+
+# Load trained model
+model = joblib.load("EmailSpamdetection.joblib")
+
+@app.route("/", methods=["GET", "POST"])
+def index():
+    prediction = None
+
+    if request.method == "POST":
+        email_text = request.form["email"]
+
+        # Model expects list-like input
+        result = model.predict([email_text])[0]
+
+        if result == 1:
+            prediction = "🚫 Spam Email"
+        else:
+            prediction = "✅ Not Spam Email"
+
+    return render_template("index.html", prediction=prediction)
+
+if __name__ == "__main__":
+    app.run(debug=True)