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import streamlit as st
from graphviz import Digraph
st.markdown("""
Prompt:
Create an interactive streamlit graph builder using the graphviz diagram model language and the streamlit feature: st.graphviz_chart(figure_or_dot, use_container_width=False) to show an azure cloud architecture model including the top ten architecture components for python full stack development for web, api, ml, models, datasets torch, transformers, streamlit, azure docker and kubernetes pods for scaling
""")
# Dot demo:
import streamlit as st
# Define the default graphviz DOT string
default_dot = """
digraph G {
rankdir=LR
node [shape=box]
WebApp -> API
API -> Models
API -> Datasets
Models -> Torch
Models -> Transformers
WebApp -> Streamlit
Streamlit -> Azure
Azure -> Docker
Azure -> Kubernetes
}
"""
# Define the list of top 10 components
components = [
"WebApp",
"API",
"Models",
"Datasets",
"Torch",
"Transformers",
"Streamlit",
"Azure",
"Docker",
"Kubernetes",
]
# Define a dictionary to map component names to DOT node IDs
node_ids = {
component: component.lower()
for component in components
}
def build_dot_string(selected_components):
"""Builds a DOT string representing the selected components"""
selected_nodes = [node_ids[component] for component in selected_components]
dot = """
digraph G {
rankdir=LR
node [shape=box]
"""
for node in selected_nodes:
dot += f"{node} [color=blue]\n"
for i in range(len(selected_nodes)):
for j in range(i+1, len(selected_nodes)):
dot += f"{selected_nodes[i]} -> {selected_nodes[j]}\n"
dot += "}"
return dot
def main():
st.title("Azure Cloud Architecture Builder")
# Select the components
st.sidebar.title("Select components")
selected_components = st.sidebar.multiselect(
"Select the top 10 components",
components,
default=components[:3]
)
# Build the DOT string
dot = build_dot_string(selected_components)
# Render the graphviz chart
st.graphviz_chart(dot, use_container_width=True)
if __name__ == "__main__":
main()
# Initialize the graph
graph = Digraph(comment='Architectural Model')
# Add nodes to the graph
graph.node('data_layer', 'Data Layer')
graph.node('acr', 'Azure Container Registry')
graph.node('aks', 'Azure Kubernetes\n& Docker Container Pod\nwith Scalability')
graph.node('snowflake', 'Snowflake Instance')
graph.node('cosmos', 'Azure Cosmos\nDatabase')
graph.node('api', 'API Standard\n(using Uvicorn)')
graph.node('soar', 'SOAR Component\n(on Linux Python\nSlimbuster Docker)')
# Add edges to the graph
graph.edge('data_layer', 'acr')
graph.edge('acr', 'aks')
graph.edge('aks', 'snowflake')
graph.edge('aks', 'cosmos')
graph.edge('aks', 'api')
graph.edge('aks', 'soar')
# Define the Streamlit app
def app():
st.title('Architectural Model')
# Draw the graph
st.graphviz_chart(graph.source)
# Add buttons to customize the graph
if st.button('Hide Data Layer'):
graph.node('data_layer', style='invisible')
if st.button('Hide Snowflake Instance'):
graph.node('snowflake', style='invisible')
if st.button('Hide SOAR Component'):
graph.node('soar', style='invisible')
st.markdown("""
# QA Model Spaces:
QA use cases include QA, Semantic Document and FAQ Search.
1. Streamlit Question Answering w Hugging Face: https://huggingface.co/spaces/awacke1/Question-answering
2. Seq2Seq:
- https://huggingface.co/spaces/awacke1/4-Seq2SeqQAT5
- https://huggingface.co/spaces/awacke1/AW-04-GR-Seq-2-Seq-QA-Auto-Gen
3. BioGPT: https://huggingface.co/spaces/awacke1/microsoft-BioGPT-Large-PubMedQA
4. NLP QA Context: https://huggingface.co/spaces/awacke1/NLPContextQATransformersRobertaBaseSquad2
- https://huggingface.co/spaces/awacke1/SOTA-Plan
5. https://huggingface.co/spaces/awacke1/Question-answering
6. QA MLM: https://huggingface.co/spaces/awacke1/SOTA-MedEntity
""")
# Run the Streamlit app
if __name__ == '__main__':
app()
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