Create app.py

app.py

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+import streamlit as st
+import numpy as np
+from sklearn.decomposition import DictionaryLearning
+from sklearn.datasets import load_digits
+import matplotlib.pyplot as plt
+
+# Title of the app
+st.title('Dictionary Learning Demo with Streamlit')
+
+# Description
+st.write('''
+    This application demonstrates the concept of Dictionary Learning using the scikit-learn library.
+    Dictionary learning aims to find a sparse representation of the data in the form of a dictionary and a sparse matrix.
+''')
+
+# Load dataset
+digits = load_digits()
+data = digits.data
+
+# Display a sample image from the dataset
+st.write("Sample image from the dataset:")
+sample_index = 0
+sample_image = data[sample_index].reshape(8, 8)
+plt.imshow(sample_image, cmap='gray')
+st.pyplot(plt)
+
+# Get user input for the number of dictionary components
+n_components = st.slider('Number of dictionary components', 1, 64, 32)
+
+# Perform dictionary learning
+dl = DictionaryLearning(n_components=n_components, transform_algorithm='lasso_lars', random_state=0)
+X_transformed = dl.fit_transform(data)
+dictionary = dl.components_
+
+# Display the learned dictionary components
+st.write("Learned dictionary components:")
+fig, axes = plt.subplots(4, 8, figsize=(8, 4))
+for i, ax in enumerate(axes.ravel()):
+    if i < n_components:
+        ax.imshow(dictionary[i].reshape(8, 8), cmap='gray')
+        ax.axis('off')
+st.pyplot(fig)
+
+# Display sparsity of the transformed data
+sparsity = np.mean(X_transformed == 0)
+st.write(f'Sparsity of the transformed data: {sparsity:.2f}')