Update pages/6_Logistic_Regression.py

pages/6_Logistic_Regression.py

@@ -8,6 +8,9 @@ from sklearn.preprocessing import StandardScaler
 from sklearn.metrics import accuracy_score, confusion_matrix
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense
+from tensorflow.keras.utils import plot_model
+import io
+from PIL import Image
 
 # Load Iris dataset
 iris = load_iris()
@@ -26,13 +29,61 @@ scaler = StandardScaler()
 X_train = scaler.fit_transform(X_train)
 X_test = scaler.transform(X_test)
 
+# Streamlit interface
+st.title('Logistic Regression with Keras on Iris Dataset')
+st.write("""
+## Introduction
+Logistic Regression is a statistical model used for binary classification tasks. 
+In this tutorial, we will use the Iris dataset to classify whether a flower is 
+**Setosa** or **Versicolor** based on its features.
+""")
+
+# Display Iris dataset information
+st.write("### Iris Dataset")
+st.write("""
+The Iris dataset contains 150 samples of iris flowers, each described by four features: 
+sepal length, sepal width, petal length, and petal width. There are three classes: Setosa, Versicolor, and Virginica.
+For this example, we'll only use the Setosa and Versicolor classes.
+""")
+
+# Display flower images
+st.write("### Flower Images")
+col1, col2 = st.columns(2)
+with col1:
+    st.image("https://upload.wikimedia.org/wikipedia/commons/5/56/Iris_setosa_2.jpg", caption="Iris Setosa", use_column_width=True)
+with col2:
+    st.image("https://upload.wikimedia.org/wikipedia/commons/4/41/Iris_versicolor_3.jpg", caption="Iris Versicolor", use_column_width=True)
+
+# Plotting sample data
+st.write("### Sample Data Distribution")
+fig, ax = plt.subplots()
+for i, color in zip([0, 1], ['blue', 'orange']):
+    idx = np.where(y == i)
+    ax.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i], edgecolor='k')
+ax.set_xlabel(iris.feature_names[0])
+ax.set_ylabel(iris.feature_names[1])
+ax.legend()
+st.pyplot(fig)
+
+# User input for number of epochs
+epochs = st.slider('Select number of epochs for training:', min_value=10, max_value=200, value=100, step=10)
+
 # Build the logistic regression model using Keras
 model = Sequential()
 model.add(Dense(1, input_dim=4, activation='sigmoid'))
 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
 
+# Display the model architecture
+st.write("### Model Architecture")
+st.write(model.summary())
+fig, ax = plt.subplots()
+buf = io.BytesIO()
+plot_model(model, to_file=buf, show_shapes=True, show_layer_names=True)
+buf.seek(0)
+st.image(buf, caption='Logistic Regression Model Architecture', use_column_width=True)
+
 # Train the model
-model.fit(X_train, y_train, epochs=100, verbose=0)
+model.fit(X_train, y_train, epochs=epochs, verbose=0)
 
 # Predict and evaluate the model
 y_pred_train = (model.predict(X_train) > 0.5).astype("int32")
@@ -43,9 +94,6 @@ test_accuracy = accuracy_score(y_test, y_pred_test)
 
 conf_matrix = confusion_matrix(y_test, y_pred_test)
 
-# Streamlit interface
-st.title('Logistic Regression with Keras on Iris Dataset')
-
 st.write('## Model Performance')
 st.write(f'Training Accuracy: {train_accuracy:.2f}')
 st.write(f'Testing Accuracy: {test_accuracy:.2f}')
@@ -73,3 +121,18 @@ if st.button('Predict'):
     prediction = (model.predict(input_data_scaled) > 0.5).astype("int32")
     st.write(f'Prediction: {"Setosa" if prediction[0][0] == 0 else "Versicolor"}')
 
+# Examples of different parameters for each flower type
+st.write('## Examples of Parameters')
+st.write("""
+### Iris Setosa:
+- Sepal Length: 5.1 cm
+- Sepal Width: 3.5 cm
+- Petal Length: 1.4 cm
+- Petal Width: 0.2 cm
+
+### Iris Versicolor:
+- Sepal Length: 7.0 cm
+- Sepal Width: 3.2 cm
+- Petal Length: 4.7 cm
+- Petal Width: 1.4 cm
+""")