Update app.py

app.py

@@ -1,6 +1,7 @@
+Create an UI for this calculator: 
+
 import streamlit as st
 import pandas as pd
-
 def calculate_cost(num_pairs, gpu_type):
     if gpu_type == "Nvidia A100":
         daily_rate = 28
@@ -11,48 +12,45 @@ def calculate_cost(num_pairs, gpu_type):
     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_time_minutes = num_pairs * time_per_pair
     total_time_hours = total_time_minutes / 60
     hourly_rate = daily_rate / 24
     total_cost = total_time_hours * hourly_rate
-    
-    return total_cost
-
-st.set_page_config(page_title="GPU Cost Calculator", page_icon="🧮", layout="wide")
 
-st.title("GPU Cost Calculator")
-
-# Input for number of pairs
-num_pairs = st.number_input("Enter the number of pairs to process:", min_value=1, value=5)
-
-# Select GPU type
-gpu_type = st.selectbox(
-    "Select GPU type:",
-    ("Nvidia A100", "H100 80GB PCIe", "AWS p4d.24xlarge (8x A100)")
-)
-
-# Calculate button
-if st.button("Calculate Cost"):
-    cost = calculate_cost(num_pairs, gpu_type)
-    st.write(f"Estimated cost for processing {num_pairs} pairs on {gpu_type}: ${cost:.4f}")
+    return total_cost
+def main():
+    st.title("GPU Cost Calculator")
+    # Input for number of pairs
+    num_pairs = st.number_input("Enter the number of pairs to process:", min_value=1, value=5)
+    # Select GPU type
+    gpu_type = st.selectbox(
+        "Select GPU type:",
+        ("Nvidia A100", "H100 80GB PCIe", "AWS p4d.24xlarge (8x A100)")
+    )
+    # Calculate button
+    if st.button("Calculate Cost"):
+        cost = calculate_cost(num_pairs, gpu_type)
+        st.write(f"Estimated cost for processing {num_pairs} pairs on {gpu_type}: ${cost:.4f}")
+    # Display GPU information
+    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()
 
-# Display GPU information
-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)