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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") |