Update app.py

app.py

@@ -1,31 +1,43 @@
 import streamlit as st
 import pandas as pd
 
-
 def display_csv(file_path, columns_to_display):
     # Load the CSV file using pandas
     df = pd.read_csv(file_path)
+
     # Select only the specified columns
     df_selected_columns = df[columns_to_display].sort_values(by=['avg_score'], ascending=False).reset_index(drop=True)
+
     # Display the selected columns as a table
     st.dataframe(df_selected_columns, height=500, width=1000)
 
 def main():
-    # Hardcoded file path
-    file_path = "merged-averaged-model_timings_2.1.0_12.1_NVIDIA_A10G_False.csv"
+    # Hardcoded file paths
+    file_path1 = "merged-averaged-model_timings_2.1.0_12.1_NVIDIA_A10G_False.csv"
+    file_path2 = "merged-averaged-model_timings_2.1.0_12.1_Tesla_T4_False.csv"  # Replace with the path to your second CSV file
+
     # Columns to display
     columns_to_display = [
-        "model_name", "pretrained", "avg_score", "image_time", "text_time", 
-        "image_shape", "text_shape", 
-        "output shape", 
+        "model_name", "pretrained", "avg_score", "image_time", "text_time",
+        "image_shape", "text_shape",
+        "output shape",
         "params (M)", "FLOPs (B)"
-    ]  # Specify the columns you want to display
-    
+    ]
+
     # Add header and description
     st.header("CLIP benchmarks - retrieval and inference")
-    st.write("CLIP benchmarks for inference and retrieval performance. Image size, context length and output dimensions are also included. Retrieval performance comes from https://github.com/mlfoundations/open_clip/blob/main/docs/openclip_retrieval_results.csv.Tested with A10G, CUDA 12.1, Torch 2.1.0")
-    
-    # Call the display_csv function with the hardcoded file path and selected columns
+    st.write("CLIP benchmarks for inference and retrieval performance. Image size, context length and output dimensions are also included. Retrieval performance comes from https://github.com/mlfoundations/open_clip/blob/main/docs/openclip_retrieval_results.csv. Tested with A10G, CUDA 12.1, Torch 2.1.0")
+
+    # Add radio button to select the CSV file
+    selected_file = st.radio("Select results for a specific GPU", ("GPU: A10g", "GPU: T4"))
+
+    # Determine the file path based on the selected file
+    if selected_file == "GPU: A10g":
+        file_path = file_path1
+    else:
+        file_path = file_path2
+
+    # Call the display_csv function with the selected file path and columns
     display_csv(file_path, columns_to_display)
 
     # Custom CSS to make the app full screen