Duplicate from awacke1/Azure.Streamlit.Github.Actions.Azure.Container.Registry.Docker.AKS

.gitattributes

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README.md

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+---
+title: Azure.Streamlit.Github.Actions.Azure.Container.Registry.Docker.AKS
+emoji: 🌖
+colorFrom: yellow
+colorTo: pink
+sdk: streamlit
+sdk_version: 1.17.0
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: awacke1/Azure.Streamlit.Github.Actions.Azure.Container.Registry.Docker.AKS
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

Setup.md

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+Write python streamlit application to use Terraform, Github Actions, Azure Container Registry and Github to set up a new streamlit app on azure.
+To create a Streamlit application using Terraform, Github Actions, Azure Container Registry, and Github, you will need to follow these steps:
+
+Create a new Streamlit application.
+Push the application to a Github repository.
+Configure the Azure Container Registry.
+Set up a Github Actions workflow to build and push the Docker image to the Azure Container Registry.
+Use Terraform to create an Azure Kubernetes Service (AKS) cluster and deploy the Streamlit app on it.
+Here's an example implementation of these steps:
+
+Create a new Streamlit app:
+python
+Copy code
+# app.py
+import streamlit as st
+
+st.title("Hello, Streamlit!")
+st.write("This is a simple Streamlit app deployed on Azure.")
+Create a Dockerfile to containerize the app:
+docker
+Copy code
+# Dockerfile
+FROM python:3.8-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+COPY . .
+
+CMD ["streamlit", "run", "app.py"]
+Add a requirements.txt file with the necessary dependencies:
+makefile
+Copy code
+streamlit==1.1.0
+Push the application to a new Github repository.
+
+Configure the Azure Container Registry:
+
+Log in to the Azure Portal.
+Create a new Azure Container Registry.
+Copy the registry's login server name for later use.
+Set up a Github Actions workflow to build and push the Docker image to the Azure Container Registry:
+yaml
+Copy code
+# .github/workflows/main.yml
+name: CI/CD
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build-and-push:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v2
+
+    - name: Set up QEMU
+      uses: docker/setup-qemu-action@v1
+
+    - name: Set up Docker Buildx
+      uses: docker/setup-buildx-action@v1
+
+    - name: Login to Azure Container Registry
+      run: echo ${{ secrets.ACR_PASSWORD }} | docker login ${{ secrets.ACR_REGISTRY }} -u ${{ secrets.ACR_USERNAME }} --password-stdin
+
+    - name: Build and push
+      uses: docker/build-push-action@v2
+      with:
+        context: .
+        push: true
+        tags: ${{ secrets.ACR_REGISTRY }}/streamlit-app:latest
+Add the necessary secrets to your Github repository:
+ACR_REGISTRY: The login server name of your Azure Container Registry.
+ACR_USERNAME: The username for your Azure Container Registry.
+ACR_PASSWORD: The password for your Azure Container Registry.
+Create Terraform configuration files to set up the Azure Kubernetes Service (AKS) cluster and deploy the Streamlit app:
+main.tf: This file will contain the main Terraform configuration.
+variables.tf: This file will contain the Terraform variables.
+outputs.tf: This file will contain the Terraform outputs.
+hcl
+Copy code
+# main.tf
+provider "azurerm" {
+  features {}
+}
+
+resource "azurerm_resource_group" "aks" {
+  name     = "streamlit-aks-rg"
+  location = "East US"
+}
+
+resource "azurerm_kubernetes_cluster" "aks" {
+  name                = "streamlit-aks"
+  location            = azurerm_resource_group.aks.location
+  resource_group_name = azurerm_resource_group.aks.name
+  dns_prefix          = "streamlit-aks"
+
+  default_node_pool {
+    name       = "default"
+    node_count = 
+
+3
+vm_size = "Standard_DS2_v2"
+}
+
+identity {
+type = "SystemAssigned"
+}
+}
+
+resource "azurerm_kubernetes_cluster_node_pool" "streamlit" {
+name = "streamlit"
+kubernetes_cluster_id = azurerm_kubernetes_cluster.aks.id
+vm_size = "Standard_DS2_v2"
+node_count = 1
+}
+
+provider "kubernetes" {
+host = azurerm_kubernetes_cluster.aks.kube_config.0.host
+client_certificate = base64decode(azurerm_kubernetes_cluster.aks.kube_config.0.client_certificate)
+client_key = base64decode(azurerm_kubernetes_cluster.aks.kube_config.0.client_key)
+cluster_ca_certificate = base64decode(azurerm_kubernetes_cluster.aks.kube_config.0.cluster_ca_certificate)
+}
+
+resource "kubernetes_namespace" "streamlit" {
+metadata {
+name = "streamlit"
+}
+}
+
+resource "kubernetes_deployment" "streamlit" {
+metadata {
+name = "streamlit"
+namespace = kubernetes_namespace.streamlit.metadata.0.name
+}
+
+spec {
+replicas = 1
+
+selector {
+  match_labels = {
+    app = "streamlit"
+  }
+}
+
+template {
+  metadata {
+    labels = {
+      app = "streamlit"
+    }
+  }
+
+  spec {
+    container {
+      name  = "streamlit"
+      image = "${var.acr_login_server}/streamlit-app:latest"
+
+      resources {
+        limits {
+          cpu    = "1"
+          memory = "1Gi"
+        }
+
+        requests {
+          cpu    = "500m"
+          memory = "500Mi"
+        }
+      }
+
+      port {
+        container_port = 8501
+      }
+    }
+  }
+}
+}
+}
+
+resource "kubernetes_service" "streamlit" {
+metadata {
+name = "streamlit"
+namespace = kubernetes_namespace.streamlit.metadata.0.name
+}
+
+spec {
+selector = {
+app = "streamlit"
+}
+
+bash
+Copy code
+port {
+  port        = 80
+  target_port = 8501
+}
+
+type = "LoadBalancer"
+}
+}
+
+bash
+Copy code
+
+```hcl
+# variables.tf
+variable "acr_login_server" {
+  description = "The Azure Container Registry login server"
+  type        = string
+}
+hcl
+Copy code
+# outputs.tf
+output "streamlit_url" {
+  value       = "http://${kubernetes_service.streamlit.status.0.load_balancer.0.ingress.0.ip}"
+  description = "Streamlit app public URL"
+}
+Initialize Terraform and apply the configuration:
+sh
+Copy code
+$ terraform init
+$ terraform apply -var "acr_login_server=<your_acr_login_server>"
+Replace <your_acr_login_server> with the login server name of your Azure Container Registry.
+
+Access the Streamlit app using the public URL displayed in the Terraform output.
+Please note that this is an example implementation and you may need to adjust the configurations according to your specific requirements and environment.
+
+
+
+
+
+

app.py

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+import streamlit as st
+from collections import Counter
+import plotly.express as px
+import numpy as np
+
+def get_word_score(word):
+    # This function returns a score based on the length of the word
+    # Modify this function as per your requirements
+    score = len(word)**2
+    return score
+
+def get_word_frequency(text):
+    # This function returns the word frequency of the given text
+    words = text.split()
+    word_frequency = Counter(words)
+    return word_frequency
+
+# Load the markdown file
+with open('Setup.md', 'r') as file:
+    text = file.read()
+
+
+# Display the parsed markdown
+st.markdown(text, unsafe_allow_html=True)
+
+# Get the word frequency of the markdown text
+word_frequency = get_word_frequency(text)
+
+# Get the top words and their frequency
+top_words = word_frequency.most_common(10)
+top_words_dict = dict(top_words)
+
+# Create a Plotly bar chart to display the top words and their frequency
+fig = px.bar(x=list(top_words_dict.keys()), y=list(top_words_dict.values()), labels={'x':'Word', 'y':'Frequency'})
+st.plotly_chart(fig)
+
+# Calculate the scores for each word based on their length
+word_scores = {word:get_word_score(word) for word in word_frequency}
+top_word_scores = dict(sorted(word_scores.items(), key=lambda item: item[1], reverse=True)[:10])
+
+# Create a Plotly bar chart to display the top words and their scores
+fig = px.bar(x=list(top_word_scores.keys()), y=list(top_word_scores.values()), labels={'x':'Word', 'y':'Score'})
+st.plotly_chart(fig)

requirements.txt

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+plotly