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import duckdb
import pandas as pd
import gradio as gr
from datasets import load_dataset
import tempfile
from query import sql_query
max_rows = 20
max_cols = None
df_display_kwargs = dict(
wrap = True,
row_count = 3,
col_count = 4,
)
dataset_choices = [
"rotten_tomatoes",
"sciq",
]
def apply_sql(input_table, sql_query):
output_df = duckdb.query(sql_query).to_df()
return output_df
def display_dataset(dataset_id):
dataset = load_dataset(dataset_id, split="train")
df = dataset.to_pandas()
display_df = df.iloc[:max_rows, :max_cols]
return display_df, df
def upload_dataset(dataset_file):
if dataset_file is None:
return None, None
df = pd.read_csv(dataset_file.name).iloc[:max_rows, :max_cols]
display_df = df.iloc[:max_rows, :max_cols]
return display_df, df
def process_dataset(full_dataset, sql_query):
input_table = full_dataset
output_df = duckdb.query(sql_query).to_df()
with tempfile.NamedTemporaryFile(delete=False) as temp_file:
file_path = temp_file.name
output_df.to_csv(file_path)
return output_df, file_path
theme = gr.themes.Soft(
primary_hue="blue",
neutral_hue="slate",
)
with gr.Blocks(theme=theme) as demo:
full_dataset = gr.State()
with gr.Column():
with gr.Row().style(equal_height=True):
with gr.Column(variant="panel"):
with gr.Row():
dark_mode_btn = gr.Button("Dark Mode", variant="primary")
load_dataset_button = gr.Button("Load HF Dataset", variant="secondary")
dataset_selector = gr.Dropdown(label="HF Dataset", choices=dataset_choices, value=dataset_choices[0])
with gr.Column(variant="compact"):
with gr.Row():
sql_query_btn = gr.Button("Apply SQL Query", variant="secondary")
download_dataset_btn = gr.Button("Download Queried Dataset", variant="primary")
sql_query_comp = gr.Code(language=None, label="SQL Query", lines=3, value=sql_query)
with gr.Row().style(equal_height=True):
upload_dataset_comp = gr.File(label="Upload Dataset")
download_dataset_comp = gr.File(label="Download Dataset")
with gr.Column(variant="panel"):
input_df_display = gr.Dataframe(**df_display_kwargs, label=f"Input Dataframe (Truncated to first {max_rows} Rows)")
output_df_display = gr.Dataframe(**df_display_kwargs, label=f"Output Dataframe (Truncated to first {max_rows} Rows)")
load_dataset_button.click(fn=display_dataset, inputs=[dataset_selector], outputs=[input_df_display, full_dataset])
upload_dataset_comp.change(fn=upload_dataset, inputs=[upload_dataset_comp], outputs=[input_df_display, full_dataset])
sql_query_btn.click(fn=apply_sql, inputs=[input_df_display, sql_query_comp], outputs=[output_df_display])
download_dataset_btn.click(fn=process_dataset, inputs=[full_dataset, sql_query_comp], outputs=[output_df_display, download_dataset_comp])
toggle_dark_mode_args = dict(
fn=None,
inputs=None,
outputs=None,
_js="""() => {
if (document.querySelectorAll('.dark').length) {
document.querySelectorAll('.dark').forEach(el => el.classList.remove('dark'));
} else {
document.querySelector('body').classList.add('dark');
}
}""",
)
demo.load(**toggle_dark_mode_args)
dark_mode_btn.click(**toggle_dark_mode_args)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860) |