Update app.py

app.py

@@ -1,27 +1,53 @@
 from flask import Flask, render_template, request
 import joblib
+import os
 
 app = Flask(__name__)
 
-# Load trained model
-model = joblib.load("EmailSpamdetection.joblib")
-
+# -------------------------------
+# Paths
+# -------------------------------
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+MODEL_PATH = os.path.join(BASE_DIR, "EmailSpamdetection.joblib")
+
+# -------------------------------
+# Load the model
+# -------------------------------
+# This model MUST be a pipeline:
+# (vectorizer + MultinomialNB)
+try:
+    model = joblib.load(MODEL_PATH)
+except Exception as e:
+    print("Error loading model:", e)
+    model = None
+
+# -------------------------------
+# Flask Routes
+# -------------------------------
 @app.route("/", methods=["GET", "POST"])
 def index():
     prediction = None
+    email_text = ""
 
     if request.method == "POST":
-        email_text = request.form["email"]
-
-        # Model expects list-like input
-        result = model.predict([email_text])[0]
+        email_text = request.form.get("email")
 
-        if result == 1:
-            prediction = "🚫 Spam Email"
+        if not model:
+            prediction = "❌ Model not loaded"
+        elif not email_text.strip():
+            prediction = "❌ Please enter email text"
         else:
-            prediction = "✅ Not Spam Email"
-
-    return render_template("index.html", prediction=prediction)
-
+            # Transform & predict using pipeline
+            try:
+                result = model.predict([email_text])[0]
+                prediction = "🚫 Spam Email" if result == 1 else "✅ Not Spam Email"
+            except Exception as e:
+                prediction = f"❌ Prediction Error: {e}"
+
+    return render_template("index.html", prediction=prediction, email_text=email_text)
+
+# -------------------------------
+# Run App
+# -------------------------------
 if __name__ == "__main__":
     app.run(debug=True)