Update app.py

app.py

@@ -1,5 +1,62 @@
 import streamlit as st
 import main
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+#x = st.slider('Select a value')
+#st.write(x, 'squared is', x * x)
+import torch
+import torch.nn as nn
+from torch import optim
+from torch.utils.data import DataLoader
+from NN import OffensiveLanguageClassifier, OffensiveLanguageDataset
+
+# Set the device to use for training
+from process_data import train
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+
+batch_size = 2
+vocab_size = 23885
+hidden_size = 128
+output_size = 3
+num_layers = 2
+num_epochs = 2
+
+# Create the model and move it to the device
+model = OffensiveLanguageClassifier(vocab_size, hidden_size, output_size, num_layers, dropout = 0.3)
+model.to(device)
+
+# Define the loss function and the optimizer
+loss_fn = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters())
+
+# Create the DataLoader
+
+train_dataset = OffensiveLanguageDataset(train[0], train["class"])
+#print(train_dataset.shape)
+#print(train_dataset.head(10))
+
+dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+print(type(dataloader))
+# Train the model
+for epoch in range(num_epochs):
+    #print(dataloader)
+    #train_features, train_labels = next(iter(dataloader)
+    for data , labels in dataloader:
+        #print(data)
+        #print(labels)
+        #data, labels = data.to(device), labels.to(device)
+
+        # Forward pass
+        #print(type(data[0])) 
+        data = torch.stack(data)
+        logits = model(data)
+        loss = loss_fn(logits, labels)
+
+        # Backward pass and optimization
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+    # Print the loss and accuracy at the end of each epoch
+    st.write(f'Epoch {epoch+1}: loss = {loss:.4f}')