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