app.py

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)