update the repository

app.py

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+import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.naive_bayes import GaussianNB
+from sklearn.svm import SVC
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+import seaborn as sns
+
+# Load MNIST dataset
+digits = datasets.load_digits()
+X, y = digits.data, digits.target
+
+# Split into train and test sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Initialize classifiers
+nb_classifier = GaussianNB()
+svm_classifier = SVC()
+knn_classifier = KNeighborsClassifier(n_neighbors=3)
+
+# Train classifiers
+nb_classifier.fit(X_train, y_train)
+svm_classifier.fit(X_train, y_train)
+knn_classifier.fit(X_train, y_train)
+
+# Predict
+nb_predictions = nb_classifier.predict(X_test)
+svm_predictions = svm_classifier.predict(X_test)
+knn_predictions = knn_classifier.predict(X_test)
+
+# Compute accuracy
+nb_accuracy = accuracy_score(y_test, nb_predictions)
+svm_accuracy = accuracy_score(y_test, svm_predictions)
+knn_accuracy = accuracy_score(y_test, knn_predictions)
+
+# Compute classification reports and confusion matrices
+nb_report = classification_report(y_test, nb_predictions)
+svm_report = classification_report(y_test, svm_predictions)
+knn_report = classification_report(y_test, knn_predictions)
+
+nb_cm = confusion_matrix(y_test, nb_predictions)
+svm_cm = confusion_matrix(y_test, svm_predictions)
+knn_cm = confusion_matrix(y_test, knn_predictions)
+
+def main():
+    # Streamlit App
+    st.title("MNIST Classifier Performance")
+    st.write("### Sample Images from MNIST Dataset")
+
+    about = """# 🖥️ MNIST Classifier Performance App 🚀
+This Streamlit app demonstrates the performance of three different machine learning classifiers on the **MNIST handwritten digits dataset**. 📊 The classifiers compared are:
+
+✅ **Naïve Bayes**  
+✅ **Support Vector Machine (SVM)**  
+✅ **K-Nearest Neighbors (KNN)**  
+
+## 🔍 Features:
+- 📸 **Displays 5 sample images** from the MNIST dataset.
+- 📊 **Trains and evaluates** Naïve Bayes, SVM, and KNN classifiers.
+- 🏆 **Compares classifier accuracy** on the test dataset.
+- 📄 **Shows classification reports** with precision, recall, and F1-score.
+- 🔥 **Visualizes confusion matrices** using heatmaps for better understanding.
+
+## 📌 How to Use:
+1. Run the app using Streamlit.  
+2. Navigate through the **three tabs** to check the performance of each classifier.  
+3. Analyze the **classification report and confusion matrix** for deeper insights.  
+4. Read the **comparison section** to understand the strengths and weaknesses of each model.  
+
+## 🎯 Insights:
+- **Naïve Bayes**: Fast but may struggle with complex patterns.  
+- **SVM**: Balanced performance with good accuracy.  
+- **KNN**: Effective but can be slow with large datasets.  
+
+🚀 Explore and experiment with different models to enhance classification performance!  
+
+### 📌 About the Creator  
+**Created by:** *Louie F. Cervantes, M.Eng. (Information Engineering)*  
+**(c) 2025 West Visayas State University**
+"""
+    with st.expander("About the App"):
+        st.markdown(about)
+
+    # Display 5 sample images
+    fig, axes = plt.subplots(1, 5, figsize=(10, 3))
+    for i, ax in enumerate(axes):
+        ax.imshow(digits.images[i], cmap='gray')
+        ax.set_title(f"Label: {digits.target[i]}")
+        ax.axis('off')
+    st.pyplot(fig)
+
+    # Create tabs
+    tab1, tab2, tab3 = st.tabs(["Naïve Bayes", "SVM", "KNN"])
+
+    with tab1:
+        st.subheader("Naïve Bayes Classifier")
+        st.write(f"Accuracy: {nb_accuracy:.4f}")
+        st.write("Classification Report:")
+        st.write(nb_report)
+        st.write("Confusion Matrix:")
+        fig, ax = plt.subplots()
+        sns.heatmap(nb_cm, annot=True, fmt='d', cmap='Blues', ax=ax)
+        st.pyplot(fig)
+
+    with tab2:
+        st.subheader("Support Vector Machine (SVM)")
+        st.write(f"Accuracy: {svm_accuracy:.4f}")
+        st.write("Classification Report:")
+        st.write(svm_report)
+        st.write("Confusion Matrix:")
+        fig, ax = plt.subplots()
+        sns.heatmap(svm_cm, annot=True, fmt='d', cmap='Blues', ax=ax)
+        st.pyplot(fig)
+
+    with tab3:
+        st.subheader("K-Nearest Neighbors (KNN)")
+        st.write(f"Accuracy: {knn_accuracy:.4f}")
+        st.write("Classification Report:")
+        st.write(knn_report)
+        st.write("Confusion Matrix:")
+        fig, ax = plt.subplots()
+        sns.heatmap(knn_cm, annot=True, fmt='d', cmap='Blues', ax=ax)
+        st.pyplot(fig)
+
+    # Comparison
+    st.write("## Classifier Comparison")
+    st.write("### Observations:")
+    st.write("- **Naïve Bayes** is fast but may struggle with complex patterns.")
+    st.write("- **SVM** performs well with a balance of accuracy and speed.")
+    st.write("- **KNN** can be effective but may be slower with large datasets.")
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+numpy
+matplotlib
+scikit-learn
+seaborn