Update app.py

app.py

@@ -136,31 +136,14 @@ def evaluate_model_with_report(val_loader):
 
 # Performance metrics
 def generate_performance_metrics():
-    model.eval()  # Set model to evaluation mode
-
-    y_true = []  # True labels
-    y_pred = []  # Predicted labels
-
-    with torch.no_grad():
-        for batch in val_loader:
-            inputs = {key: val.to(device) for key, val in batch.items()}
-            labels = inputs.pop("labels").to(device)
-
-            outputs = model(**inputs)
-            prediction = torch.argmax(outputs.logits, dim=1).item()
-
-            y_true.append(label)
-            y_pred.append(prediction)
-
-    # Compute accuracy and classification report
-    accuracy = accuracy_score(y_true, y_pred)
-    report = classification_report(y_true, y_pred, output_dict=True)
-
+    y_pred = model.predict(X_test)
+    accuracy = evaluate_model_with_report(val_loader)
+    report = classification_report(y_true, y_pred, target_names=["Ham", "Spam"])
     return {
         "accuracy": f"{accuracy:.2%}",
         "precision": f"{report['1']['precision']:.2%}",
         "recall": f"{report['1']['recall']:.2%}",
-        "f1_score": f"{report['1']['f1-score']:.2%}",
+        "f1_score": f"{report['1']['f1-score']:.2%}"
     }
 
 # Gradio Interface