Create app.py

app.py

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+import streamlit as st
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.optimizers import Adam
+
+# Set up Streamlit layout
+st.title("PyTorch vs Keras Comparison")
+
+# Define PyTorch model
+class PyTorchModel(nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(PyTorchModel, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+# Define Keras model
+def KerasModel(input_size, hidden_size, output_size):
+    model = Sequential()
+    model.add(Dense(hidden_size, activation='relu', input_shape=(input_size,)))
+    model.add(Dense(output_size))
+    return model
+
+# Define example NLP tasks
+nlp_tasks = {
+    'Task 1: Sentiment Analysis': {
+        'PyTorch': {
+            'model': PyTorchModel(100, 64, 2),
+            'optimizer': optim.Adam,
+        },
+        'Keras': {
+            'model': KerasModel(100, 64, 2),
+            'optimizer': Adam,
+        }
+    },
+    'Task 2: Text Classification': {
+        'PyTorch': {
+            'model': PyTorchModel(200, 128, 5),
+            'optimizer': optim.SGD,
+        },
+        'Keras': {
+            'model': KerasModel(200, 128, 5),
+            'optimizer': Adam,
+        }
+    }
+}
+
+# Select NLP task
+task = st.sidebar.selectbox("Select NLP Task", list(nlp_tasks.keys()))
+
+# Select framework
+framework = st.sidebar.selectbox("Select Framework", ['PyTorch', 'Keras'])
+
+# Get model and optimizer for selected task and framework
+model = nlp_tasks[task][framework]['model']
+optimizer = nlp_tasks[task][framework]['optimizer']
+
+# Display model summary
+st.subheader(f"{framework} Model Summary")
+st.text(model)
+
+# Display optimizer details
+st.subheader(f"{framework} Optimizer Details")
+st.text(optimizer)
+
+# Perform example computations
+if st.button("Perform Computation"):
+    # Perform forward pass
+    input_data = torch.randn(1, model.fc1.in_features)
+    output = model(input_data)
+    st.write(f"Output: {output}")
+
+    # Perform backward pass
+    loss = output.mean()
+    optimizer = optimizer(model.parameters(), lr=0.01)
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    st.write("Backward pass completed.")