import streamlit as st
import pandas as pd
import time
import random

def calculate_cost(num_pairs, num_shirts, num_pants, gpu_type):
    if gpu_type == "Nvidia A100":
        daily_rate = 28
        time_per_pair = 1  # minute
    elif gpu_type == "H100 80GB PCIe":
        daily_rate = 78.96
        time_per_pair = 0.5  # assuming it's twice as fast
    else:  # AWS p4d.24xlarge
        daily_rate = 786.48
        time_per_pair = 0.25  # assuming it's four times as fast due to 8 GPUs

    total_items = num_pairs + num_shirts + num_pants
    total_time_minutes = total_items * (time_per_pair / 2)  # Divide by 2 as per the new logic
    total_time_hours = total_time_minutes / 60
    hourly_rate = daily_rate / 24
    total_cost = total_time_hours * hourly_rate
    
    return total_cost

def generate_random_case(gpu_type):
    new_case = {
        'pairs': random.randint(0, 9),
        'shirts': random.randint(0, 19),
        'pants': random.randint(0, 19)
    }
    new_case['price'] = calculate_cost(new_case['pairs'], new_case['shirts'], new_case['pants'], gpu_type)
    return new_case

def main():
    st.set_page_config(page_title="Automated GPU Cost Calculator", page_icon="🧮", layout="wide")

    st.title("Automated GPU Cost Calculator")

    col1, col2 = st.columns(2)

    with col1:
        is_automated = st.toggle("Automate case generation")
        gpu_type = st.selectbox(
            "Select GPU type:",
            ("Nvidia A100", "H100 80GB PCIe", "AWS p4d.24xlarge (8x A100)")
        )

    with col2:
        if not is_automated:
            num_pairs = st.number_input("Number of pairs:", min_value=0, value=0)
            num_shirts = st.number_input("Number of shirts:", min_value=0, value=0)
            num_pants = st.number_input("Number of pants:", min_value=0, value=0)
            if st.button("Calculate Cost"):
                cost = calculate_cost(num_pairs, num_shirts, num_pants, gpu_type)
                st.write(f"Estimated cost: ${cost:.4f}")
        else:
            num_pairs = num_shirts = num_pants = 0
            cases = []
            cost_placeholder = st.empty()
            cases_placeholder = st.empty()

            while is_automated:
                new_case = generate_random_case(gpu_type)
                cases.append(new_case)
                num_pairs += new_case['pairs']
                num_shirts += new_case['shirts']
                num_pants += new_case['pants']
                
                total_cost = calculate_cost(num_pairs, num_shirts, num_pants, gpu_type)
                cost_placeholder.write(f"Total cost: ${total_cost:.4f}")
                
                cases_text = "**Generated Cases**\n"
                for i, case in enumerate(cases[-10:], 1):  # Show only the last 10 cases
                    cases_text += f"* Case {i}: {case['pairs']} pairs, {case['shirts']} shirts, {case['pants']} pants = ${case['price']:.4f}\n"
                cases_placeholder.markdown(cases_text)
                
                time.sleep(5)  # Generate a new case every 5 seconds

    st.subheader("GPU Information")
    gpu_data = {
        "Provider": ["H100 80GB PCIe", "AWS (p4d.24xlarge)", "GPU Mart"],
        "GPU": ["Nvidia H100", "Nvidia A100 (8 GPUs)", "Nvidia A100"],
        "vCPUs": [16, 96, "Dual 18-Core E5-2697v4"],
        "RAM": ["125 GB", "1152 GiB", "256 GB"],
        "GPU Memory": ["80 GB", "320 GB (8 x 40 GB)", "40 GB HBM2e"],
        "Instance Storage": ["Network Storage: 10Pb+", "8 x 1000 GB NVMe SSD", "240 GB SSD + 2TB NVMe + 8TB SATA"],
        "Network Bandwidth": ["Not Specified", "400 Gbps", "100Mbps - 1Gbps"],
        "On-Demand Price/hr": ["$3.29", "$32.77", "N/A"],
        "Daily Price": ["$78.96", "$786.48", "$28.00"],
        "Monthly Price": ["$2,368.80", "$23,594.40", "$799.00"],
        "1-Year Reserved (Hourly)": ["N/A", "$19.22", "N/A"],
        "3-Year Reserved (Hourly)": ["N/A", "$11.57", "N/A"]
    }

    df = pd.DataFrame(gpu_data)
    st.table(df)

if __name__ == "__main__":
    main()