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"
    # Columns to display
    columns_to_display = [
        "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
    display_csv(file_path, columns_to_display)

    # Custom CSS to make the app full screen
    st.markdown("""
    <style>
    .reportview-container {
        width: 100%;
        height: 100%;
    }
    </style>
    """, unsafe_allow_html=True)

if __name__ == "__main__":
    main()