Update app.py

app.py

@@ -7,7 +7,7 @@ import torch.nn.functional as F
 from torch.utils.data import Dataset, DataLoader
 from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
 from sklearn.model_selection import train_test_split
-from sklearn.metrics import classification_report
+from sklearn.metrics import classification_report, accuracy_score
 import gradio as gr
 
 # Define device
@@ -93,8 +93,18 @@ 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)
+    model.eval()  # Set model to evaluation mode
+    
+    y_pred = []
+    
+    with torch.no_grad():
+        for email in X_test:
+            inputs = tokenizer(email, padding=True, truncation=True, max_length=128, return_tensors="pt")
+            outputs = model(**inputs)
+            prediction = torch.argmax(outputs.logits, dim=1).item()
+            y_pred.append(prediction)
+    
+    accuracy = accuracy_score(y_test, y_pred)
     report = classification_report(y_test, y_pred, output_dict=True)
     
     return {
@@ -104,6 +114,7 @@ def generate_performance_metrics():
         "f1_score": f"{report['1']['f1-score']:.2%}",
     }
 
+
 # Gradio Interface
 
 def create_interface():
@@ -127,16 +138,13 @@ def create_interface():
             results = classify_email(email_text)
             return (
                 results["result"],
-                results["confidence"],
-                results["highlighted"],
-                results["spammy_keywords"],
-                results["advice"]
+                results["confidence"]
             )
 
         analyze_button.click(
             fn=classify_email,
             inputs=email_input,
-            outputs=[result_output, confidence_output, accuracy_output]
+            outputs=[result_output, confidence_output]
 )
 
         gr.Markdown("## 📊 Model Performance Analytics")
@@ -146,7 +154,7 @@ def create_interface():
             gr.Textbox(value=performance_metrics["recall"], label="Recall", interactive=False)
             gr.Textbox(value=performance_metrics["f1_score"], label="F1 Score", interactive=False)
         
-            return interface
+    return interface
 
 # Launch the interface
 interface = create_interface()