Update app.py

app.py

@@ -68,21 +68,14 @@ class MemoryEfficientNN(nn.Module):
 class MemoryEfficientDataset(IterableDataset):
     def __init__(self, X, y, batch_size):
         self.X = X
-        self.y = y.astype(int)  # Convert labels to integers
+        self.y = torch.LongTensor(y.astype(int))  # Convert labels to long tensors
         self.batch_size = batch_size
 
     def __iter__(self):
         for i in range(0, len(self.y), self.batch_size):
             X_batch = self.X[i:i+self.batch_size].toarray()
-            y_batch = self.y[i:i+self.batch_size]
-            yield torch.FloatTensor(X_batch), torch.LongTensor(y_batch)
-
-    def __iter__(self):
-        for i in range(0, len(self.y), self.batch_size):
-            X_batch = self.X[i:i+self.batch_size].toarray()
-            y_batch = self.y[i:i+self.batch_size]
-            yield torch.FloatTensor(X_batch), torch.LongTensor(y_batch)
-
+            y_batch = self.y[i:i+self.batch_size].unsqueeze(1)  # Add a new dimension to the labels
+            yield torch.FloatTensor(X_batch), y_batch
 # Train Memory-Efficient Neural Network
 X_train, X_test, y_train, y_test = train_test_split(contexts_encoded, emotions_target, test_size=0.2, random_state=42)
 input_size = X_train.shape[1]
@@ -276,6 +269,7 @@ def get_sentiment(text):
     result = sentiment_pipeline(text)[0]
     return f"Sentiment: {result['label']}, Score: {result['score']:.4f}"
 
+
 def process_input(text):
     try:
         normalized_text = normalize_context(text)
@@ -306,7 +300,6 @@ def process_input(text):
         error_message = f"An error occurred: {str(e)}"
         print(error_message)  # Logging the error
         return error_message, error_message, error_message, error_message
-
 iface = gr.Interface(
     fn=process_input,
     inputs="text",