Update app.py

app.py

@@ -1,87 +1,102 @@
 import streamlit as st
-import pandas as pd
+from graphviz import Digraph
+import time
 import random
-import plotly.graph_objects as go
-import plotly.express as px
-
-def generate_health_conditions():
-    return [
-        {"condition": "💔 Heart disease", "emoji": "💗", "spending": {"Minnesota": 100, "Florida": 150, "California": 200, "New York": 120, "Texas": 180}, "treatment": "Regular checkups with a cardiologist", "savings": "$1000"},
-        {"condition": "🤕 Trauma-related disorders", "emoji": "🚑", "spending": {"Minnesota": 90, "Florida": 110, "California": 150, "New York": 100, "Texas": 130}, "treatment": "Counseling and physical therapy", "savings": "$500"},
-        {"condition": "🦀 Cancer", "emoji": "🎗️", "spending": {"Minnesota": 80, "Florida": 120, "California": 180, "New York": 100, "Texas": 150}, "treatment": "Early detection and treatment", "savings": "$2000"},
-        {"condition": "🧠 Mental disorders", "emoji": "🧘", "spending": {"Minnesota": 70, "Florida": 100, "California": 140, "New York": 90, "Texas": 120}, "treatment": "Therapy and medication", "savings": "$1500"},
-        {"condition": "🦴 Osteoarthritis and joint disorders", "emoji": "🏥", "spending": {"Minnesota": 60, "Florida": 90, "California": 120, "New York": 80, "Texas": 100}, "treatment": "Low-impact exercise and physical therapy", "savings": "$800"},
-        {"condition": "💉 Diabetes", "emoji": "🩸", "spending": {"Minnesota": 50, "Florida": 80, "California": 100, "New York": 60, "Texas": 90}, "treatment": "Regular checkups and medication", "savings": "$1200"},
-        {"condition": "🫁 Chronic obstructive pulmonary disease and asthma", "emoji": "🫀", "spending": {"Minnesota": 40, "Florida": 70, "California": 90, "New York": 50, "Texas": 80}, "treatment": "Inhalers and breathing exercises", "savings": "$600"},
-        {"condition": "🩺 Hypertension", "emoji": "💉", "spending": {"Minnesota": 30, "Florida": 60, "California": 80, "New York": 40, "Texas": 70}, "treatment": "Lifestyle changes and medication", "savings": "$900"},
-        {"condition": "🔬 Hyperlipidemia", "emoji": "🔬", "spending": {"Minnesota": 20, "Florida": 50, "California": 70, "New York": 30, "Texas": 60}, "treatment": "Lifestyle changes and medication", "savings": "$700"},
-        {"condition": "🦴 Back problems", "emoji": "🧍", "spending": {"Minnesota": 10, "Florida": 40,
-
-def calculate_total_spending(health_conditions):
-    total_spending = 0
-    for condition in health_conditions:
-        for state, spending in condition["spending"].items():
-            total_spending += spending
-    return round(total_spending, 1)
-
-def generate_sunburst_chart(health_conditions, total_spending):
-    fig_sunburst = go.Figure(go.Treemap(
-        labels=[f"{condition['emoji']} {condition['condition']} ({state})" for condition in health_conditions for state in condition['spending'].keys()],
-        parents=[f"{condition['condition']} ({state})" for condition in health_conditions for state in condition['spending'].keys()],
-        values=[spending for condition in health_conditions for spending in condition['spending'].values()],
-        branchvalues="total",
-    ))
-
-    fig_sunburst.update_layout(
-        title=f"Top Health Conditions in Different States by Spending (Total: ${total_spending}B)",
-        margin=dict(l=0, r=0, t=50, b=0),
-    )
-    return fig_sunburst
-
-def roll(state, condition):
-    frequencies = [random.randint(1, 10) for _ in range(1000)]
-    spending = condition["spending"][state]
-    frequencies = [round(frequency * spending / 100, 1) for frequency in frequencies]
-    return frequencies
-
-def generate_bar_chart(state, condition, frequencies):
-    fig_bar = px.bar(
-        x=[f"Variant {i}" for i in range(1, 11)],
-        y=frequencies[:10],
-        labels={'x': 'Variant', 'y': 'Cost'},
-        title=f"Variants of {condition['condition']} ({condition['emoji']}) in {state}",
-    )
-    return fig_bar
-
-def generate_recommendation(condition):
-    return f"Based on the severity of your {condition['condition']}, we recommend {condition['treatment']} for early treatment. This could save you up to {condition['savings']} in healthcare costs."
-
-def main():
-    states = ["Minnesota", "Florida", "California", "New York", "Texas"]
-    top_n = 10
-
-    health_conditions = generate_health_conditions()
-
-    total_spending = calculate_total_spending(health_conditions)
-
-    fig_sunburst = generate_sunburst_chart(health_conditions, total_spending)
-
-    st.plotly_chart(fig_sunburst)
-
-    condition_idx = st.selectbox("Select your current health condition", range(top_n))
-
-    condition = health_conditions[condition_idx]
-
-    st.write(generate_recommendation(condition))
-
-    state = st.selectbox("Select your state", states)
-
-    frequencies = roll(state, condition)
-
-    fig_bar = generate_bar_chart(state, condition, frequencies)
-
-    st.plotly_chart(fig_bar)
-
-if __name__ == "__main__":
-    main()
 
+# 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
+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")
+
+# 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")
+
+# 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(60):
+    # 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)}")
+
+# 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()