Update pages/intro.py

pages/intro.py

@@ -7,6 +7,18 @@ st.markdown("""
 </p>
 """, unsafe_allow_html=True)
 
+st.markdown("<h3 style='text-align:; color: #4CAF50;'>📊Example📈🔍 </h3>", unsafe_allow_html=True)
+
+
+st.markdown("""
+<p style="font-size:16px; color: black;">
+A farmer wanted to grow the best crops but didn’t know which seeds to plant. A scientist helped by collecting data on soil, temperature, and rainfall, and used that data to give the farmer advice on when to plant and water the crops.
+</p>
+<p style="font-size:16px; color: black;">
+Just like the scientist analyzed data to guide the farmer, data science uses data to find patterns and make decisions, helping solve problems and predict outcomes.
+</p>
+""", unsafe_allow_html=True)
+
 st.markdown("<h1>What is Artificial Intelligence? 🤖</h1>", unsafe_allow_html=True)
 
 st.markdown("""
@@ -65,6 +77,19 @@ Inputs are called <b>features</b>, and outputs are called <b>class labels</b>. M
 </p>
 """, unsafe_allow_html=True)
 
+st.markdown("<h3 style='text-align:; color: #4CAF50;'>🧠Example💡 </h3>", unsafe_allow_html=True)
+
+
+
+st.markdown("""
+<p style='text-align: justify; color: #333; font-size: 18px;'>
+Machine Learning (ML) is like a teacher who uses past exam results to find patterns and help students improve. By analyzing how study habits affect grades, 
+the teacher creates a system that suggests personalized study tips. Similarly, ML uses past data to identify patterns and make predictions, 
+like Netflix recommending movies based on your viewing history.
+</p>
+""", unsafe_allow_html=True)
+
+
 # DL Section
 st.markdown("<h1 >Deep Learning (DL)</h1>", unsafe_allow_html=True)
 st.markdown("""
@@ -74,7 +99,16 @@ It is a subfield of both <b>AI</b> and <b>ML</b>.
 </p>
 """, unsafe_allow_html=True)
 
-import streamlit as st
+st.markdown("<h3 style='text-align:; color: #4CAF50;'>🧠Example🤖💡 </h3>", unsafe_allow_html=True)
+
+
+st.markdown("""
+<p style='text-align: justify; color: #333; font-size: 18px;'>
+Deep Learning (DL) is like learning from experience instead of just following rules, unlike Machine Learning (ML), which follows fixed instructions. 
+DL uses systems that improve over time by processing data and recognizing patterns. 
+A real-life example is self-driving cars, which learn to navigate and make decisions by processing data and getting better with experience.
+</p>
+""", unsafe_allow_html=True)
 
 # App Title
 st.markdown("<h1>Generative AI: A Transformative Technology</h1>",unsafe_allow_html=True)
@@ -96,33 +130,19 @@ Generative AI is a <strong>transformative technology</strong> designed to mimic
 <strong>natural language processing</strong> and <strong>content generation</strong>.</p>
 """, unsafe_allow_html=True)
 
-# Key Features with examples
+# Key Feature with examples
 st.markdown("""
 <h2 style="color: #2196F3;">Key Features</h2>
 <ol style="font-size:16px;">
     <li>
         <strong>Content Creation</strong>: Generative AI tools like <strong>ChatGPT</strong> create human-like text for applications such as emails and stories.
     </li>
-    <li>
-        <strong>Visual Media Generation</strong>: Tools like <strong>DALL·E</strong> generate realistic and creative images.
-    </li>
-    <li>
-        <strong>Personalized Recommendations</strong>: Models like <strong>Gemini</strong> tailor suggestions based on user preferences.
-    </li>
 </ol>
 """, unsafe_allow_html=True)
 
-# Applications Section
-st.markdown("""
-<h2 style="color: #FF9800;">Applications</h2>
-<ul style="font-size:16px;">
-    <li><strong>Chatbots and Virtual Assistants</strong>: Enhancing customer interactions with tools like <strong>Meta AI</strong>.</li>
-    <li><strong>Text Summarization</strong>: Creating concise summaries from lengthy documents.</li>
-    <li><strong>Creative Tools</strong>: AI-driven design for artwork and music composition.</li>
-</ul>
-""", unsafe_allow_html=True)
 
-# Conclusion Section
+
+# Potential of Generative AI
 st.markdown("""
 <h2 style="color: #9C27B0;">Conclusion</h2>
 <p style="font-size:16px;">