Update pages/Life_cycle_of_ML.py

pages/Life_cycle_of_ML.py

@@ -84,38 +84,113 @@ The life cycle of Machine Learning (ML) involves several stages, from defining t
     </marker>
   </defs>
 </svg>
-
-<!-- Buttons below shapes -->
-
-<!-- Problem Statement Button (Rectangle) -->
-<button style="width: 150px; height: 50px; background-color: #FFB6C1; border: 1px solid #000; text-align: center;">Problem Statement</button>
-
-<!-- Data Collection Button (Ellipse) -->
-<button style="width: 160px; height: 80px; background-color: #ADD8E6; border-radius: 40px; border: 1px solid #000; text-align: center;">Data Collection</button>
-
-<!-- Simple EDA Button (Ellipse) -->
-<button style="width: 160px; height: 80px; background-color: #90EE90; border-radius: 40px; border: 1px solid #000; text-align: center;">Simple EDA</button>
-
-<!-- Data Preprocessing Button (Rectangle) -->
-<button style="width: 150px; height: 50px; background-color: #FFD700; border: 1px solid #000; text-align: center;">Data Preprocessing</button>
-
-<!-- EDA Button (Circle) -->
-<button style="width: 80px; height: 80px; background-color: #FF7F50; border-radius: 50%; border: 1px solid #000; text-align: center;">EDA</button>
-
-<!-- Feature Engineering Button (Ellipse) -->
-<button style="width: 160px; height: 80px; background-color: #9370DB; border-radius: 40px; border: 1px solid #000; text-align: center;">Feature Engineering</button>
-
-<!-- Training Button (Rectangle) -->
-<button style="width: 150px; height: 50px; background-color: #FF6347; border: 1px solid #000; text-align: center;">Training</button>
-
-<!-- Testing Button (Circle) -->
-<button style="width: 80px; height: 80px; background-color: #98FB98; border-radius: 50%; border: 1px solid #000; text-align: center;">Testing</button>
-
-<!-- Deploying Button (Ellipse) -->
-<button style="width: 160px; height: 80px; background-color: #F0E68C; border-radius: 40px; border: 1px solid #000; text-align: center;">Deploying</button>
-
-<!-- Monitoring Button (Rectangle) -->
-<button style="width: 150px; height: 50px; background-color: #B0E0E6; border: 1px solid #000; text-align: center;">Monitoring</button>
+# Initialize session state for tracking the current lifecycle step
+if "step" not in st.session_state:
+    st.session_state.step = 0
+
+# Define ML lifecycle steps with detailed descriptions
+ml_lifecycle = [
+    {
+        "title": "1️⃣ **Problem Statement**",
+        "description": """
+        **Understanding the problem and setting objectives for the ML model.
+        """
+    },
+    {
+        "title": "2️⃣ **Data Collection**",
+        "description": """
+        **Gathering relevant data for model training.
+        """
+    },
+    {
+        "title": "3️⃣ **Data Preparation**",
+        "description": """
+        ** Initial analysis to understand the dataset's basic properties.  
+        - Steps:  
+            - Handle missing values and outliers.  
+            - Normalize or scale numerical features.  
+            - Encode categorical data (e.g., one-hot encoding).  
+        - Tools: Python libraries like Pandas, NumPy, or OpenCV for images.  
+        - Example: Removing null values from a customer dataset.
+        """
+    },
+    {
+        "title": "4️⃣ **Feature Engineering**",
+        "description": """
+        **Select or create the most relevant features for the model.  
+        - Techniques:  
+            - Feature Selection: Choose the most important columns (e.g., using correlation).  
+            - Feature Creation: Combine or transform existing features.  
+        """
+    },
+    {
+        "title": "5️⃣ **Model Selection**",
+        "description": """
+        ** Choose the right ML algorithm for your problem.  
+        - Factors to consider:
+            - Problem type: Classification, Regression, Clustering, etc.  
+            - Data size and structure.  
+        """
+    },
+    {
+        "title": "6️⃣ **Training**",
+        "description": """
+        ** Train the ML model using training data.  
+        - Process:  
+            - Split data into training and validation sets.  
+            - Use the training data to fit the model.  
+        """
+    },
+    {
+        "title": "7️⃣ **Evaluation**",
+        "description": """
+        ** Assess the model's performance using metrics.  
+        - Metrics:
+            - Classification: Accuracy, Precision, Recall, F1-Score.  
+            - Regression: Mean Squared Error (MSE), R² Score.  
+        """
+    },
+    {
+        "title": "8️⃣ **Deployment**",
+        "description": """
+        ** Integrate the trained model into a production environment.  
+        - Steps:
+            - Create an API for model predictions.  
+            - Monitor performance on real-world data.  
+        """
+    },
+    {
+        "title": "9️⃣ **Monitoring & Maintenance**",
+        "description": """
+        **Ensure the model continues to perform well over time.  
+        - Monitor:
+            - Data drift: Changes in data distribution.  
+            - Model decay: Performance deterioration.
+        """
+    },
+]
+
+# Display title and description
+st.title("📊 Machine Learning Life Cycle")
+st.write("Explore the detailed view of life cycle of a Machine Learning project by clicking  the steps below:")
+
+# Buttons for navigation
+col1, col2, col3 = st.columns([1, 1, 1])
+with col1:
+    if st.button("⬅️ Previous", disabled=st.session_state.step == 0):
+        st.session_state.step -= 1
+with col3:
+    if st.button("➡️ Next", disabled=st.session_state.step == len(ml_lifecycle) - 1):
+        st.session_state.step += 1
+
+# Display the current lifecycle step with details
+current_step = ml_lifecycle[st.session_state.step]
+st.markdown(f"### {current_step['title']}")
+st.markdown(current_step['description'])
+
+# Reset button
+if st.button("🔄 Restart"):
+    st.session_state.step = 0
 """
 
 # Render HTML content in Streamlit