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import streamlit as st
from graphviz import Digraph
import time
import random
# 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="TB") # Top to Bottom or LR Left to Right
graph.attr(fontsize="20")
graph.attr(compound="true")
graph.attr(nodesep="0.5")
# Define the nodes
nodes = [
"📊 Data Collection",
"🧹 Data Cleaning",
"🔧 Data Transformation",
"🔎 Feature Engineering",
"⚙️ Model Selection",
"🎓 Model Training",
"🚢 Model Deployment",
"📡 Model Serving",
"🔮 Predictions",
"👍 Feedback Collection",
"🤔 Feedback Processing",
"✍️ Model Updating"
]
for node in nodes:
graph.node(node)
# Add the swim lanes
with graph.subgraph(name="cluster_0") as c:
c.attr(rank="1")
c.attr(label=SWIM_LANES["Data Pipelines"])
c.edge("📊 Data Collection", "🧹 Data Cleaning", style="invis")
c.edge("🧹 Data Cleaning", "🔧 Data Transformation", style="invis")
with graph.subgraph(name="cluster_1") as c:
c.attr(rank="2")
c.attr(label=SWIM_LANES["Build and Train Models"])
c.edge("🔎 Feature Engineering", "⚙️ Model Selection", style="invis")
c.edge("⚙️ Model Selection", "🎓 Model Training", style="invis")
with graph.subgraph(name="cluster_2") as c:
c.attr(rank="3")
c.attr(label=SWIM_LANES["Deploy and Predict"])
c.edge("🚢 Model Deployment", "📡 Model Serving", style="invis")
c.edge("📡 Model Serving", "🔮 Predictions", style="invis")
with graph.subgraph(name="cluster_3") as c:
c.attr(rank="4")
c.attr(label="Reinforcement Learning Human Feedback")
c.edge("🔮 Predictions", "👍 Feedback Collection", style="invis")
c.edge("👍 Feedback Collection", "🤔 Feedback Processing", style="invis")
c.edge("🤔 Feedback Processing", "✍️ Model Updating", style="invis")
def render_graph():
st.graphviz_chart(graph.source)
def update_graph():
for i in range(10):
# 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()
# Wait for 1 second
time.sleep(1)
# Render the initial graph
render_graph()
# Update the graph every second for 60 seconds
update_graph()
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