Update app.py

app.py

@@ -18,32 +18,23 @@ graph.attr(compound="true")
 graph.attr(nodesep="0.5")
 
 # Define the nodes
-graph.node("📊 Data Collection")
-graph.node("🧹 Data Cleaning")
-graph.node("🔧 Data Transformation")
-graph.node("🔎 Feature Engineering")
-graph.node("⚙️ Model Selection")
-graph.node("🎓 Model Training")
-graph.node("🚢 Model Deployment")
-graph.node("📡 Model Serving")
-graph.node("🔮 Predictions")
-graph.node("👍 Feedback Collection")
-graph.node("🤔 Feedback Processing")
-graph.node("✍️ Model Updating")
+nodes = [
+    "📊 Data Collection",
+    "🧹 Data Cleaning",
+    "🔧 Data Transformation",
+    "🔎 Feature Engineering",
+    "⚙️ Model Selection",
+    "🎓 Model Training",
+    "🚢 Model Deployment",
+    "📡 Model Serving",
+    "🔮 Predictions",
+    "👍 Feedback Collection",
+    "🤔 Feedback Processing",
+    "✍️ Model Updating"
+]
 
-# Add the edges
-graph.edge("📊 Data Collection", "🧹 Data Cleaning")
-graph.edge("🧹 Data Cleaning", "🔧 Data Transformation")
-graph.edge("🔧 Data Transformation", "🔎 Feature Engineering")
-graph.edge("🔎 Feature Engineering", "⚙️ Model Selection")
-graph.edge("⚙️ Model Selection", "🎓 Model Training")
-graph.edge("🎓 Model Training", "🚢 Model Deployment")
-graph.edge("🚢 Model Deployment", "📡 Model Serving")
-graph.edge("📡 Model Serving", "🔮 Predictions")
-graph.edge("🔮 Predictions", "👍 Feedback Collection")
-graph.edge("👍 Feedback Collection", "🤔 Feedback Processing")
-graph.edge("🤔 Feedback Processing", "✍️ Model Updating")
-graph.edge("✍️ Model Updating", "🎓 Model Training")
+for node in nodes:
+    graph.node(node)
 
 # Add the swim lanes
 with graph.subgraph(name="cluster_0") as c:
@@ -76,19 +67,9 @@ def render_graph():
 
 def update_graph():
     for i in range(10):
-        # Update the graph with new inputs randomly
-        graph.node("📊 Data Collection", label=f"📊 Data Collection\nData {random.randint(0,100)}")
-        graph.node("🧹 Data Cleaning", label=f"🧹 Data Cleaning\nCleaned Data {random.randint(0,100)}")
-        graph.node("🔧 Data Transformation", label=f"🔧 Data Transformation\nTransformed Data {random.randint(0,100)}")
-        graph.node("🔎 Feature Engineering", label=f"🔎 Feature Engineering\nFeatures {random.randint(0,100)}")
-        graph.node("⚙️ Model Selection", label=f"⚙️ Model Selection\nSelected Model {random.randint(0,100)}")
-        graph.node("🎓 Model Training", label=f"🎓 Model Training\nTrained Model {random.randint(0,100)}")
-        graph.node("🚢 Model Deployment", label=f"🚢 Model Deployment\nDeployed Model {random.randint(0,100)}")
-        graph.node("📡 Model Serving", label=f"📡 Model Serving\nServed Model {random.randint(0,100)}")
-        graph.node("🔮 Predictions", label=f"🔮 Predictions\nPredicted Results {random.randint(0,100)}")
-        graph.node("👍 Feedback Collection", label=f"👍 Feedback Collection\nFeedback {random.randint(0,100)}")
-        graph.node("🤔 Feedback Processing", label=f"🤔 Feedback Processing\nProcessed Feedback {random.randint(0,100)}")
-        graph.node("✍️ Model Updating", label=f"✍️ Model Updating\nUpdated Model {random.randint(0,100)}")
+        # Randomly select two nodes and add an edge between them
+        node1, node2 = random.sample(nodes, 2)
+        graph.edge(node1, node2)
         
         # Render the updated graph
         render_graph()
@@ -100,4 +81,4 @@ def update_graph():
 render_graph()
 
 # Update the graph every second for 60 seconds
-update_graph()
+update_graph()