Spaces:
Runtime error
Runtime error
| 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() | |