Update app.py

app.py

@@ -41,13 +41,24 @@ test_texts = [example['text'] for example in test_data]
 test_labels = [example['label'] for example in test_data]
 label_mapping = {0: 'sadness', 1: 'joy', 2: 'love', 3: 'anger', 4: 'fear', 5: 'surprise'}
 test_labels = [label_mapping[label].lower() for label in test_labels]
-'''
+
 # Evaluate DistilBERT
 distilbert_predictions = [distilbert_classifier(text)[0]['label'].lower() for text in test_texts]
 print("DistilBERT Model Evaluation Metrics:")
 print("Accuracy:", accuracy_score(test_labels, distilbert_predictions))
 print("F1 Score:", f1_score(test_labels, distilbert_predictions, average='weighted'))
-'''
+
+# Evaluate RoBERTa
+roberta_predictions = []
+for text in test_texts:
+    inputs = roberta_tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    outputs = roberta_model(**inputs)
+    prediction = torch.argmax(outputs.logits, dim=-1).item()
+    roberta_predictions.append(label_mapping[prediction].lower())
+    
+print("RoBERTa Model Evaluation Metrics:")
+print("Accuracy:", accuracy_score(test_labels, roberta_predictions))
+print("F1 Score:", f1_score(test_labels, roberta_predictions, average='weighted'))
 
 # Function to generate an image from a prompt using Stable Diffusion
 def generate_image(prompt):