Update app.py

app.py

@@ -93,18 +93,20 @@ def evaluate_model_with_report(val_loader):
 
 # Performance metrics
 def generate_performance_metrics():
+    y_pred = model.predict(X_test)
+    accuracy = accuracy_score(y_test,y_pred)
+    report = classification_report(y_test, y_pred, output_dict=True))
     return {
-        "accuracy": "N/A",
-        "precision": "N/A",
-        "recall": "N/A",
-        "f1_score": "N/A",
-        "confusion_matrix_plot": "",
+        "accuracy": f"{accuracy:.2%}",
+        "precision": f"{report['1']['precision']:.2%}",
+        "recall": f"{report['1']['recall']:.2%}",
+        "f1_score": f"{report['1']['f1-score']:.2%}",
     }
 
-performance_metrics = generate_performance_metrics()
-
 # Gradio Interface
+
 def create_interface():
+    performance_metrics = generate_performance_metrics()
     with gr.Blocks() as interface:
         gr.Markdown("Spam Email Classification")
 
@@ -120,6 +122,16 @@ def create_interface():
 
         analyze_button = gr.Button("Analyze Email 🕵️‍♂️")
 
+        def email_analysis_pipeline(email_text):
+            results = classify_email(email_text)
+            return (
+                results["result"],
+                results["confidence"],
+                results["highlighted"],
+                results["spammy_keywords"],
+                results["advice"]
+            )
+
         analyze_button.click(
             fn=classify_email,
             inputs=email_input,