Update app.py

app.py

@@ -47,7 +47,6 @@ 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__()
@@ -65,21 +64,19 @@ class MemoryEfficientNN(nn.Module):
         return self.layers(x.long())
 
 # Memory-efficient dataset
-class MemoryEfficientNN(nn.Module):
-    def __init__(self, input_size, hidden_size, num_classes):
-        super(MemoryEfficientNN, self).__init__()
-        self.layers = nn.Sequential(
-            nn.Embedding(input_size, hidden_size),
-            nn.ReLU(),
-            nn.Dropout(0.2),
-            nn.Linear(hidden_size, hidden_size),
-            nn.ReLU(),
-            nn.Dropout(0.2),
-            nn.Linear(hidden_size, num_classes)
-        )
-        
-    def forward(self, x):
-        return self.layers(x.long())
+class MemoryEfficientDataset(IterableDataset):
+    def __init__(self, X, y, batch_size):
+        self.X = X
+        self.y = torch.LongTensor(y)  # 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), 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]
 hidden_size = 64
@@ -133,14 +130,8 @@ emotions = {
     'optimism': {'percentage': 10, 'motivation': 'hopeful', 'intensity': 0},
     'pessimism': {'percentage': 10, 'motivation': 'doubtful', 'intensity': 0},
     'boredom': {'percentage': 10, 'motivation': 'indifferent', 'intensity': 0},
-    'envy': {'percentage': 10, 'motivation': 'jealous', 'intensity': 0}
-}
-total_percentage = 200
-default_percentage = total_percentage / len(emotions)
-for emotion in emotions:
-    emotions[emotion]['percentage'] = default_percentage
-
-emotion_history_file = 'emotion_history.json'
+    'envy': {'percentage': 10, 'motivation': 'jealous', 'intensity': 0},
+    emotion_history_file = 'emotion_history.json'
 
 def load_historical_data(file_path=emotion_history_file):
     if os.path.exists(file_path):
@@ -272,7 +263,6 @@ 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)
@@ -304,6 +294,7 @@ 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",
@@ -314,4 +305,6 @@ iface = gr.Interface(
         gr.Textbox(label="GPT Generated Text")
     ],
     live=True
-)
+)
+
+iface.launch(share=True)