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import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification, make_circles, make_blobs, make_moons
from sklearn.model_selection import train_test_split, learning_curve
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, f1_score
from mlxtend.plotting import plot_decision_regions
# Display image
# st.markdown("<div style='text-align: center;'>"
# "<img src='inno.jpg' width='700' height='400'>"
# "</div>",
# unsafe_allow_html=True)
from PIL import Image
# image = Image.open("Inno.jpg")
# image = image.resize((800, 500)) # Resize image before displaying
st.image("innomatics-footer-logo.webp", use_container_width=True, width=300)
# Streamlit app title with color
st.markdown("<h1 style='text-align: center; color: #FF5733;'>Boundary Surfaces Visualization</h1>", unsafe_allow_html=True)
# Select dataset
data = st.sidebar.selectbox('Select Dataset', ('Classification', 'Circles', 'Blobs', 'Moons'))
if data == 'Classification':
X, y = make_classification(n_samples=100, n_features=2, n_redundant=0, random_state=27)
elif data == 'Circles':
X, y = make_circles(n_samples=100, factor=0.5, noise=0.05)
elif data == 'Blobs':
X, y = make_blobs(n_samples=250, centers=2, n_features=2, cluster_std=1.0, random_state=27)
elif data == 'Moons':
X, y = make_moons(n_samples=250, noise=0.1, random_state=27)
# Split dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=27)
def plot_decision_surface(X, y, model, title):
plt.figure(figsize=(6,4))
plot_decision_regions(X, y, clf=model, colors="#7f7f7f,#bcbd22,#17becf")
plt.title(title)
st.pyplot(plt.gcf(), clear_figure=True)
# Select classifier
classifier_name = st.sidebar.selectbox('Select Classifier',
('KNN', 'Naive Bayes', 'Logistic Regression', 'Decision Tree', 'Random Forest', 'SVM'))
# Initialize model based on user selection
if classifier_name == 'KNN':
n_neighbors = st.sidebar.slider('Number of Neighbors (k)', 1, 15, 3)
weights = st.sidebar.radio('Weight Function', ('uniform', 'distance'))
algorithm = st.sidebar.selectbox('Algorithm', ('auto', 'ball_tree', 'kd_tree', 'brute'))
p = st.sidebar.slider("Distance Parameter (p)", 1, 5, 2)
model = KNeighborsClassifier(n_neighbors=n_neighbors, weights=weights, algorithm=algorithm,p=p)
elif classifier_name == 'Naive Bayes':
model = GaussianNB()
elif classifier_name == 'Logistic Regression':
model = LogisticRegression()
elif classifier_name == 'Decision Tree':
model = DecisionTreeClassifier()
elif classifier_name == 'Random Forest':
n_estimators = st.sidebar.slider('Number of Trees', 10, 200, 100)
model = RandomForestClassifier(n_estimators=n_estimators)
elif classifier_name == 'SVM':
kernel = st.sidebar.selectbox('Kernel Type', ('linear', 'poly', 'rbf', 'sigmoid'))
C = st.sidebar.slider('Regularization (C)', 0.01, 10.0, 1.0)
model = SVC(kernel=kernel, C=C)
# Train model
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
# Compute accuracy & F1-score
accuracy = accuracy_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)
# Display metrics in Streamlit
st.markdown("<h3 style='color: #4CAF50;'>📊 Model Performance</h3>", unsafe_allow_html=True)
st.write(f"✅ **Accuracy:** {accuracy:.2f}")
st.write(f"📏 **F1-score:** {f1:.2f}")
# Plot decision boundary
st.subheader("📍 Decision Boundary")
plot_decision_surface(X, y, model, f'{classifier_name} Decision Surface')
# Plot Learning Curve
def plot_learning_curve(model, X, y):
train_sizes, train_scores, test_scores = learning_curve(model, X, y, cv=5, scoring='accuracy', train_sizes=np.linspace(0.1, 1.0, 10))
train_mean = np.mean(train_scores, axis=1)
test_mean = np.mean(test_scores, axis=1)
plt.figure(figsize=(6,4))
plt.plot(train_sizes, train_mean, 'o-', label="Training Accuracy", color="blue")
plt.plot(train_sizes, test_mean, 'o-', label="Validation Accuracy", color="red")
plt.xlabel("Training Samples")
plt.ylabel("Accuracy")
plt.title(f"Learning Curve: {classifier_name}")
plt.legend()
st.pyplot(plt.gcf(), clear_figure=True)
# Display Learning Curve
st.subheader("📈 Learning Curve")
plot_learning_curve(model, X, y)
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