Update app.py

app.py

@@ -47,11 +47,12 @@ emotions_target = pd.Categorical(df['emotion']).codes
 emotion_classes = pd.Categorical(df['emotion']).categories
 
 # Memory-efficient Neural Network with PyTorch
+
 class MemoryEfficientNN(nn.Module):
     def __init__(self, input_size, hidden_size, num_classes):
         super(MemoryEfficientNN, self).__init__()
         self.layers = nn.Sequential(
-            nn.Linear(input_size, hidden_size),
+            nn.Embedding(input_size, hidden_size),
             nn.ReLU(),
             nn.Dropout(0.2),
             nn.Linear(hidden_size, hidden_size),
@@ -61,7 +62,7 @@ class MemoryEfficientNN(nn.Module):
         )
         
     def forward(self, x):
-        return self.layers(x)
+        return self.layers(x.long())
 
 # Memory-efficient dataset
 class MemoryEfficientDataset(IterableDataset):
@@ -282,7 +283,7 @@ def process_input(text):
         
         rf_prediction = rf_model.predict(encoded_text)[0]
         isolation_score = isolation_forest.decision_function(encoded_text)[0]
-        nn_prediction = model(torch.FloatTensor(encoded_text.toarray()).to(device)).argmax(dim=1).item()
+        nn_prediction = model(torch.LongTensor(encoded_text.todense()).to(device)).argmax(dim=1).item()
         
         predicted_emotion = emotion_classes[rf_prediction]
         sentiment_score = isolation_score