from datasets import load_dataset
import streamlit as st
import pandas as pd


# Load your dataset (replace 'your-dataset' with the actual dataset name)
csv_url = 'https://huggingface.co/datasets/NENS/wim_data/resolve/main/input_employees/boran.csv'

#df = pd.read_csv('esther.csv')

#st.write(df)

#df.loc[df['week'] == 42, 'druk'] = 'heel druk'


#hoi= fs.ls("datasets/NENS/wim_data/input_employees/", detail=False)
#st.write(hoi)

#button = st.button('do it')

#if button:
    
    #st.write(df)

    #with fs.open("datasets/NENS/wim_data/input_employees/esther.csv", "w") as f:
    #    f.write("text,label")
    #    f.write("Fantastic movie!,good")

        #https://huggingface.co/datasets/NENS/wim_data/resolve/main/input_employees/esther.csv
    
    #df.to_csv("hf://spaces/NENS/test/test.csv")
    #print('het werkt!')


from huggingface_hub import HfFileSystem
import pandas as pd

# Initialize HfFileSystem
fs = HfFileSystem()

# Define the path to your file in the Hugging Face Space
remote_csv_path = "esther.csv"  # Path to the file in your repo

# Step 1: Read the CSV file from your Hugging Face Space
with fs.open(remote_csv_path, 'rb') as f:
    df = pd.read_csv(f)

print("Original DataFrame:")
print(df)

# Step 2: Make some changes to the DataFrame
df.loc[df['week'] == 42, 'druk'] = 'heel druk'

print("Modified DataFrame:")
print(df)

button = st.button('do it')

if button:

    # Step 3: Save the modified DataFrame back to a CSV file
    with fs.open(remote_csv_path, 'wb') as f:
        df.to_csv(f, index=False)
    
    print(f"Saved changes back to {remote_csv_path}")