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")