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import gradio as gr
from typing import Dict, Any
from data_loader import read_dask_data, read_polars_data, read_another_dask_data
def load_and_process_data(dataset_choice: str, num_rows: int) -> Dict[str, Any]:
"""
Load and process data based on the user's choice and specified number of rows.
Args:
dataset_choice (str): The dataset to load.
num_rows (int): The number of rows to display.
Returns:
Dict[str, Any]: A dictionary containing the processed data or error message.
"""
dataset_mapping = {
"Dask Data": read_dask_data,
"Polars Data": read_polars_data,
"Another Dask Data": read_another_dask_data
}
data_loader = dataset_mapping.get(dataset_choice)
if not data_loader:
return {"error": "Invalid dataset choice."}
try:
data = data_loader()
processed_data = data.head(num_rows)
return {"processed_data": processed_data.to_dict()}
except Exception as e:
return {"error": f"Data processing failed: {str(e)}"}
def create_interface():
"""
Create and launch the Gradio interface for data selection and display.
"""
with gr.Blocks() as demo:
gr.Markdown("# Enhanced Dataset Loader Demo")
gr.Markdown("Interact with various datasets and select the amount of data to display.")
with gr.Row():
dataset_choice = gr.Dropdown(
choices=["Dask Data", "Polars Data", "Another Dask Data"],
label="Select Dataset"
)
num_rows = gr.Slider(
minimum=1, maximum=100, value=5, label="Number of Rows to Display"
)
processed_data_output = gr.JSON(label="Processed Data")
dataset_choice.render()
num_rows.render()
processed_data_output.render()
demo.add(dataset_choice, num_rows, processed_data_output, load_and_process_data)
demo.launch()
if __name__ == "__main__":
create_interface()
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