Update app.py

app.py

@@ -13,7 +13,7 @@ def generate_ml_blog():
 def introduction_to_ml():
     introduction_blog = '''
     ## Introduction to Machine Learning (ML)
-    Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables computers to learn from data and make predictions or decisions without being explicitly programmed. It has revolutionized many industries and plays a crucial role in technologies such as self-driving cars, recommendation systems, and facial recognition.
+     🤖Machine Learning (ML) is a subset of Artificial Intelligence (AI) that enables computers to learn from data and make predictions or decisions without being explicitly programmed. It has revolutionized many industries and plays a crucial role in technologies such as self-driving cars, recommendation systems, and facial recognition.
 
     ### Types of Machine Learning
     There are three main types of machine learning:
@@ -226,64 +226,24 @@ def neural_networks():
 
 
 
-import streamlit as st
-
-# Define page functions (Make sure these functions are defined elsewhere in your code)
-def introduction_to_ml():
-    st.title("Introduction to Machine Learning (ML)")
-    # Add content for the Introduction page
-
-def supervised_learning():
-    st.title("Supervised Learning")
-    # Add content for Supervised Learning
-
-def unsupervised_learning():
-    st.title("Unsupervised Learning")
-    # Add content for Unsupervised Learning
-
-def reinforcement_learning():
-    st.title("Reinforcement Learning")
-    # Add content for Reinforcement Learning
-
-def linear_regression():
-    st.title("Linear Regression")
-    # Add content for Linear Regression
-
-def logistic_regression():
-    st.title("Logistic Regression")
-    # Add content for Logistic Regression
-
-def decision_trees():
-    st.title("Decision Trees")
-    # Add content for Decision Trees
-
-def knn():
-    st.title("K-Nearest Neighbors (KNN)")
-    # Add content for K-Nearest Neighbors
-
-def svm():
-    st.title("Support Vector Machines (SVM)")
-    # Add content for SVM
-
-def neural_networks():
-    st.title("Neural Networks")
-    # Add content for Neural Networks
 
 # Sidebar for content navigation
 st.sidebar.header("📚 Contents")
 
+# Show Introduction first in the sidebar
+intro_option = st.sidebar.radio(["Introduction"])
+
 # Grouping the types of machine learning into one section
 types_of_ml = st.sidebar.radio("📊 Types of Machine Learning", 
-                               ["Supervised Learning", "Unsupervised Learning", "Reinforcement Learning"])
+                               ["Select a Type", "Supervised Learning", "Unsupervised Learning", "Reinforcement Learning"])
 
 # Grouping other topics under a separate section
 popular_algorithms = st.sidebar.radio("🚀 Popular Algorithms", 
-                                      ["Linear Regression", "Logistic Regression", "Decision Trees", 
+                                      ["Select an Algorithm", "Linear Regression", "Logistic Regression", "Decision Trees", 
                                        "K-Nearest Neighbors (KNN)", "Support Vector Machines (SVM)", "Neural Networks"])
 
 # Mapping page functions to selection
 pages = {
-    "Introduction": introduction_to_ml,
     "Supervised Learning": supervised_learning,
     "Unsupervised Learning": unsupervised_learning,
     "Reinforcement Learning": reinforcement_learning,
@@ -297,26 +257,20 @@ pages = {
 
 # Main content area
 st.markdown("<h1 style='text-align: center; color: orange;'>Machine Learning (ML)</h1>", unsafe_allow_html=True)
-st.markdown(generate_ml_blog())
-
-# Use the selected type of machine learning or algorithm to show the content
-if types_of_ml in pages:
-    pages[types_of_ml]()
-
-if popular_algorithms in pages:
-    pages[popular_algorithms]()
-
-# Display the page content based on the selected page
-st.markdown(f"<h2 style='text-align: center; color: orange;'>{types_of_ml}</h2>", unsafe_allow_html=True)
-content_function = pages[types_of_ml]  # Get the corresponding function
-st.markdown(content_function())
-
-# Alternatively, display the popular algorithms' content as well
-st.markdown(f"<h2 style='text-align: center; color: orange;'>{popular_algorithms}</h2>", unsafe_allow_html=True)
-content_function = pages[popular_algorithms]  # Get the corresponding function
-st.markdown(content_function())
-
-
-
-
 
+# Show the Introduction content first
+if intro_option == "Introduction":
+    st.markdown(introduction_to_ml())
+
+# Show content based on the selected type of machine learning or algorithm from the sidebar
+if types_of_ml != "Select a Type":
+    st.markdown(f"<h2 style='text-align: center; color: orange;'>{types_of_ml}</h2>", unsafe_allow_html=True)
+    content_function = pages.get(types_of_ml, None)
+    if content_function:
+        st.markdown(content_function())
+
+if popular_algorithms != "Select an Algorithm":
+    st.markdown(f"<h2 style='text-align: center; color: orange;'>{popular_algorithms}</h2>", unsafe_allow_html=True)
+    content_function = pages.get(popular_algorithms, None)
+    if content_function:
+        st.markdown(content_function())