Create app.py

app.py

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+import streamlit as st
+from graphviz import Digraph
+
+# Define the emoji to use for the swim lanes
+SWIM_LANES = {
+    "Data Pipelines": "🔍",
+    "Build and Train Models": "🧪",
+    "Deploy and Predict": "🚀"
+}
+
+# Define the graph structure
+graph = Digraph()
+graph.attr(rankdir="LR")
+graph.attr(fontsize="20")
+graph.attr(compound="true")
+
+# Add the swim lanes
+with graph.subgraph(name="cluster_0") as c:
+    c.attr(color="gray")
+    c.attr(label=SWIM_LANES["Data Pipelines"])
+    c.node_attr.update(style="filled", color="white")
+    c.node("📊 Data Collection")
+    c.node("🧹 Data Cleaning")
+    c.node("🔧 Data Transformation")
+    
+with graph.subgraph(name="cluster_1") as c:
+    c.attr(color="gray")
+    c.attr(label=SWIM_LANES["Build and Train Models"])
+    c.node_attr.update(style="filled", color="white")
+    c.node("🔎 Feature Engineering")
+    c.node("⚙️ Model Selection")
+    c.node("🎓 Model Training")
+    
+with graph.subgraph(name="cluster_2") as c:
+    c.attr(color="gray")
+    c.attr(label=SWIM_LANES["Deploy and Predict"])
+    c.node_attr.update(style="filled", color="white")
+    c.node("🚢 Model Deployment")
+    c.node("📡 Model Serving")
+    c.node("🔮 Predictions")
+    
+# Add the RLHF step
+with graph.subgraph(name="cluster_3") as c:
+    c.attr(color="lightblue")
+    c.attr(label="Reinforcement Learning Human Feedback")
+    c.node_attr.update(style="filled", color="white")
+    c.node("👍 Feedback Collection")
+    c.node("🤔 Feedback Processing")
+    c.node("✍️ Model Updating")
+    
+# Define 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")
+
+# Render the graph in Streamlit
+st.graphviz_chart(graph.source)