Rename app.py to gpu_calculator.py

app.py

@@ -1,64 +0,0 @@
-import streamlit as st
-import pandas as pd
-
-def calculate_cost(num_pairs, 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_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)
-
-# Input for shirt size
-shirt_size = st.text_input("Enter shirt size (e.g., S, M, L, XL):")
-
-# Input for bottom wear size
-bottom_wear_size = st.text_input("Enter bottom wear size (e.g., S, M, L, XL):")
-
-# 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)

gpu_calculator.py

@@ -0,0 +1,99 @@
+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()