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| import streamlit as st | |
| import time | |
| # Streamlit App | |
| st.title("AI Model Deployment 🚀") | |
| # Intro | |
| st.write(""" | |
| Welcome to the AI model deployment flow! Here, we'll follow the process of deploying | |
| your fine-tuned AI model to one of the cloud instances. Let's begin! | |
| """) | |
| # Select cloud provider | |
| cloud_provider = st.selectbox("Choose a cloud provider:", ["AWS EC2", "Google Cloud VM", "Azure VM"]) | |
| st.write(f"You've selected {cloud_provider}!") | |
| # Specify model details | |
| model_name = st.text_input("Enter your AI model name:", "MySpecialModel") | |
| if model_name: | |
| st.write(f"We'll deploy the model named: {model_name}") | |
| # Button to start the deployment | |
| if st.button("Start Deployment"): | |
| st.write("Deployment started... Please wait!") | |
| # Simulate progress bar for deployment | |
| latest_iteration = st.empty() | |
| bar = st.progress(0) | |
| for i in range(100): | |
| # Update the progress bar with each iteration. | |
| latest_iteration.text(f"Deployment progress: {i+1}%") | |
| bar.progress(i + 1) | |
| time.sleep(0.05) | |
| st.write(f"Deployment completed! Your model {model_name} is now live on {cloud_provider} 🌐") | |
| # Sidebar for additional settings (pretend configurations) | |
| st.sidebar.title("Deployment Settings") | |
| instance_type = st.sidebar.selectbox("Instance Type:", ["Standard", "High Memory", "High CPU", "GPU"]) | |
| storage_option = st.sidebar.slider("Storage Size (in GB):", 10, 500, 50) | |
| st.sidebar.write(f"Instance Type: {instance_type}") | |
| st.sidebar.write(f"Storage Size: {storage_option} GB") |