Deploy Gradio app with multiple files

app.py

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+import gradio as gr
+import pandas as pd
+from data_utils import generate_initial_data, process_data, handle_data_selection
+
+# --- Configuration ---
+initial_df = generate_initial_data()
+initial_regions = ["All"] + sorted(initial_df['Region'].unique().tolist())
+MAX_PROFIT_SLIDER = initial_df['Profit'].max()
+MIN_PROFIT_SLIDER = initial_df['Profit'].min()
+
+def plot_data_updates(df, region, profit_limit):
+    """Processes filtered data and returns updated plot objects."""
+    if df is None or (isinstance(df, pd.DataFrame) and df.empty):
+        gr.Warning("No data available to display.")
+        return gr.Plot(None), gr.Plot(None)
+
+    try:
+        daily_summary, product_summary = process_data(df, region, profit_limit)
+
+        if daily_summary.empty and product_summary.empty:
+            gr.Warning("No data matches the current filter criteria.")
+            return gr.Plot(None), gr.Plot(None)
+
+        # Line Plot: Daily Sales Trend
+        line_plot_update = gr.LinePlot(
+            daily_summary,
+            x="Day",
+            y="Total Sales",
+            title=f"Daily Sales Trend (Region: {region})",
+            tooltip=["Day", "Total Sales"],
+            height=300
+        )
+
+        # Bar Plot: Product Quantity Breakdown
+        bar_plot_update = gr.BarPlot(
+            product_summary,
+            x="Product",
+            y="Quantity",
+            title="Total Quantity by Product",
+            color="Product",
+            tooltip=["Product", "Quantity"],
+            height=300
+        )
+
+        return line_plot_update, bar_plot_update
+
+    except Exception as e:
+        gr.Error(f"An error occurred during data processing: {e}")
+        return gr.Plot(None), gr.Plot(None)
+
+def respond(message, history):
+    """Simple chat response function."""
+    if message.strip():
+        history = history + [[message, "You said: " + message]]
+    return "", history
+
+# --- Gradio Blocks Definition ---
+with gr.Blocks(theme=gr.themes.Soft(), title="Interactive Data Reporter", fill_height=True) as demo:
+    gr.HTML(
+        "<div style='text-align: center; padding: 10px; border-bottom: 1px solid #f0f0f0;'>"
+        "<h1>Interactive Sales Data Dashboard</h1>"
+        "<p>Analyze synthetic sales data using linked controls and dynamic charts.</p>"
+        "<p>Built with <a href='https://huggingface.co/spaces/akhaliq/anycoder' target='_blank' style='color: blue; text-decoration: none;'>anycoder</a></p>"
+        "</div>"
+    )
+
+    # State to hold the original, potentially edited dataset
+    raw_data_state = gr.State(value=initial_df)
+    
+    with gr.Row(equal_height=False):
+        
+        # --- Column 1: Controls and Raw Data Display ---
+        with gr.Column(scale=2, min_width=400):
+            with gr.Accordion("Filters and Controls", open=True):
+                region_dropdown = gr.Dropdown(
+                    choices=initial_regions,
+                    value="All",
+                    label="Select Region",
+                )
+                
+                profit_slider = gr.Slider(
+                    minimum=MIN_PROFIT_SLIDER,
+                    maximum=MAX_PROFIT_SLIDER,
+                    value=MIN_PROFIT_SLIDER,
+                    step=50,
+                    label="Minimum Profit Filter",
+                    interactive=True
+                )
+                
+                refresh_btn = gr.Button("Apply Filters & Update Plots", variant="primary")
+
+            with gr.Group():
+                # Dataframe for raw data display and editing
+                raw_data_display = gr.Dataframe(
+                    value=initial_df,
+                    headers=initial_df.columns.tolist(),
+                    datatype=['date', 'str', 'str', 'number', 'number', 'number', 'number'],
+                    interactive=True, # Allow user edits
+                    label="Raw Sales Data (Click a row to see details)",
+                    height=500,
+                    show_row_numbers=True
+                )
+                
+                selection_output = gr.Textbox(
+                    label="Selected Row Details",
+                    lines=4,
+                    interactive=False,
+                    placeholder="Click on a row in the table above to see details here."
+                )
+
+        # --- Column 2: Visualizations ---
+        with gr.Column(scale=3, min_width=500):
+            with gr.Tabs():
+                with gr.TabItem("Graphical Report"):
+                    with gr.Row():
+                        line_plot_out = gr.LinePlot(label="Sales Trend Over Time")
+                    with gr.Row():
+                        bar_plot_out = gr.BarPlot(label="Product Quantity Breakdown")
+                with gr.TabItem("Chat"):
+                    chatbot = gr.Chatbot()
+                    msg = gr.Textbox(placeholder="Type your message here...", show_label=False)
+                    msg.submit(respond, inputs=[msg, chatbot], outputs=[msg, chatbot])
+    
+    # --- Event Listeners and Interactions ---
+    
+    # Define the inputs and outputs for the main plotting function
+    inputs_for_plot = [raw_data_state, region_dropdown, profit_slider]
+    outputs_for_plot = [line_plot_out, bar_plot_out]
+    
+    # 1. Update plots when the "Apply" button is clicked
+    refresh_btn.click(plot_data_updates, inputs=inputs_for_plot, outputs=outputs_for_plot)
+    
+    # 2. Update plots when filter controls are changed (live update)
+    region_dropdown.change(plot_data_updates, inputs=inputs_for_plot, outputs=outputs_for_plot)
+    profit_slider.release(plot_data_updates, inputs=inputs_for_plot, outputs=outputs_for_plot)
+    
+    # 3. Update the shared `raw_data_state` if a user edits the DataFrame component.
+    # This ensures that plot updates use the latest user-modified data.
+    def update_state(df):
+        return df
+
+    raw_data_display.edit(
+        update_state, 
+        inputs=[raw_data_display], 
+        outputs=[raw_data_state],
+        queue=False # Use a fast, non-blocking update for the state
+    )
+    
+    # 4. Handle DataFrame row selection to show details
+    raw_data_display.select(
+        handle_data_selection, 
+        inputs=[raw_data_state], 
+        outputs=[selection_output]
+    )
+
+    # 5. Initial plot generation on application load
+    demo.load(plot_data_updates, inputs=inputs_for_plot, outputs=outputs_for_plot)
+
+
+demo.queue().launch()

data_utils.py

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+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import gradio as gr
+
+def generate_initial_data(n_rows=50):
+    """Generates a synthetic sales DataFrame for simulation."""
+    dates = [datetime(2023, 1, 1) + timedelta(days=i) for i in range(n_rows)]
+    np.random.seed(42)
+    
+    data = {
+        'Date': dates,
+        'Region': np.random.choice(['North', 'South', 'East', 'West'], n_rows),
+        'Product': np.random.choice(['A', 'B', 'C', 'D'], n_rows),
+        'Sales': np.random.randint(100, 500, n_rows),
+        'Quantity': np.random.randint(5, 50, n_rows),
+        'Cost': np.random.uniform(10, 50, n_rows).round(2)
+    }
+    df = pd.DataFrame(data)
+    # Calculate Profit
+    df['Profit'] = (df['Sales'] - df['Cost']) * df['Quantity']
+    return df
+
+def process_data(df, selected_region, min_profit):
+    """Filters and aggregates data based on user controls."""
+    if df is None:
+        return pd.DataFrame(), pd.DataFrame()
+
+    # Ensure data from Gradio component is handled as a DataFrame
+    # This is important as gr.State might pass the raw object
+    if not isinstance(df, pd.DataFrame):
+        df = pd.DataFrame(df)
+    
+    # Filtering
+    if selected_region != "All":
+        df = df[df['Region'] == selected_region]
+        
+    df = df[df['Profit'] >= min_profit]
+    
+    # Aggregation for Line Plot (Daily Sales Trend)
+    if not df.empty:
+        df['Date'] = pd.to_datetime(df['Date'])
+        daily_summary = df.groupby(df['Date'].dt.date)['Sales'].sum().reset_index()
+        daily_summary.rename(columns={'Date': 'Day', 'Sales': 'Total Sales'}, inplace=True)
+    else:
+        daily_summary = pd.DataFrame(columns=['Day', 'Total Sales'])
+
+    # Aggregation for Bar Plot (Product breakdown)
+    product_summary = df.groupby('Product')['Quantity'].sum().reset_index()
+
+    return daily_summary, product_summary
+
+def handle_data_selection(df, evt: gr.SelectData):
+    """Handles the selection event on the DataFrame component."""
+    if df is None or not evt.index:
+        return "No data selected."
+        
+    if not isinstance(df, pd.DataFrame):
+        df = pd.DataFrame(df)
+
+    row_index = evt.index[0]
+    
+    if row_index >= len(df):
+        return "Invalid row selected."
+        
+    row_data = df.iloc[row_index].to_dict()
+    
+    output_text = f"Selected Row {row_index} details:\n"
+    for key, value in row_data.items():
+        # Handle datetime objects for display
+        if isinstance(value, (datetime, pd.Timestamp)):
+            value = value.strftime('%Y-%m-%d')
+        elif isinstance(value, np.generic):
+            value = value.item() # Convert numpy types to native Python types
+            
+        output_text += f"  {key}: {value}\n"
+        
+    return output_text

requirements.txt

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+pandas
+numpy
+openpyxl
+gradio
+requests
+Pillow