Update app.py

app.py

@@ -1,50 +1,29 @@
-import matplotlib.pyplot as plt
-import numpy as np
+# Import necessary libraries
 import gradio as gr
+from data_loader import read_dask_data, read_polars_data, read_another_dask_data
 
-def plot_forecast(final_year, cryptocurrencies, noise, show_legend, point_style, line_colors):
-    start_year = 2020
-    x = np.arange(start_year, final_year + 1)
-    year_count = x.shape[0]
-    plt_format = ({"cross": "X", "line": "-", "circle": "o--"})[point_style]
-    
-    fig = plt.figure()
-    ax = fig.add_subplot(111)
-    
-    for i, crypto in enumerate(cryptocurrencies):
-        series = np.arange(0, year_count, dtype=float)
-        series = series**2 * (i + 1)
-        series += np.random.rand(year_count) * noise
-        ax.plot(x, series, plt_format, label=crypto, color=line_colors[i % len(line_colors)])
-    
-    if show_legend:
-        plt.legend()
-    
-    ax.set_xlabel("Year")
-    ax.set_ylabel("Value")
-    ax.set_title(f"Cryptocurrency Forecast from {start_year} to {final_year}")
-    
-    return fig
+# Function to handle the data loading logic
+def load_and_display_data():
+    # Load datasets
+    dask_data = read_dask_data()
+    polars_data = read_polars_data()
+    another_dask_data = read_another_dask_data()
 
-# Create a color palette for the plot
-def generate_color_palette():
-    return ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd"]
+    # Optionally process data (e.g., filter, summarize)
+    # Display the first few rows of the datasets
+    return {
+        "Dask Data from Codeforces Python Submissions": dask_data.head(),
+        "Polars Data from Python Reasoning Dataset": polars_data.head(),
+        "Another Dask Data from Python Github Code": another_dask_data.head()
+    }
 
+# Create a Gradio interface to display the data
 demo = gr.Interface(
-    plot_forecast,
-    [
-        gr.Radio([2025, 2030, 2035, 2040], label="Project to:", info="Select the forecast year."),
-        gr.CheckboxGroup(["Bitcoin", "Ethereum", "Litecoin"], label="Cryptocurrency Selection", info="Choose which cryptocurrencies to forecast."),
-        gr.Slider(1, 100, label="Noise Level", info="Adjust the level of random noise added to the data."),
-        gr.Checkbox(label="Show Legend", info="Toggle the display of the legend."),
-        gr.Dropdown(["cross", "line", "circle"], label="Point Style", info="Choose the marker style for the plot."),
-        gr.ColorPicker(label="Line Colors", value="#1f77b4", info="Choose line colors for the plot.", show_alpha=False),  # Color picker for lines
-    ],
-    outputs=gr.Plot(label="Forecast Plot", format="png"),
-    layout="vertical",  # Improved layout for better organization
-    title="Cryptocurrency Forecasting Tool",  # Added title
-    theme="huggingface",  # Applying a predefined theme for better look and feel
+    load_and_display_data,
+    inputs=[],  # No inputs for this simple demo
+    outputs="json",  # Display output as a JSON to show data head
+    title="Dataset Loader Demo",
 )
 
 if __name__ == "__main__":
-    demo.launch(show_error=True)
+    demo.launch()