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app.py

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+import os
+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image
+import torch
+import random
+from datetime import datetime
+# Set seed for reproducibility
+random.seed(42)
+np.random.seed(42)
+torch.manual_seed(42)
+# Data generation functions
+def generate_sample_data():
+    dates = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D')
+    values = np.random.normal(100, 15, len(dates)) + np.sin(np.arange(len(dates)) * 0.1) * 30
+    return pd.DataFrame({
+        'date': dates,
+        'value': values,
+        'category': np.random.choice(['A', 'B', 'C'], len(dates))
+    })
+def generate_line_chart(df, title="Time Series Data"):
+    fig, ax = plt.subplots(figsize=(12, 6))
+    ax.plot(df['date'], df['value'], marker='', linewidth=2, color='#3366CC')
+    ax.set_title(title, fontsize=16)
+    ax.set_xlabel('Date', fontsize=12)
+    ax.set_ylabel('Value', fontsize=12)
+    ax.grid(True, alpha=0.3)
+    fig.tight_layout()
+    return fig
+def generate_pie_chart(df, title="Category Distribution"):
+    category_counts = df['category'].value_counts()
+    fig, ax = plt.subplots(figsize=(8, 8))
+    ax.pie(category_counts, labels=category_counts.index, autopct='%1.1f%%', 
+           colors=['#3366CC', '#DC3912', '#FF9900'], startangle=90)
+    ax.set_title(title, fontsize=16)
+    fig.tight_layout()
+    return fig
+def generate_bar_chart(df, title="Monthly Averages"):
+    df['month'] = df['date'].dt.month_name()
+    monthly_avg = df.groupby('month')['value'].mean().reindex([
+        'January', 'February', 'March', 'April', 'May', 'June', 
+        'July', 'August', 'September', 'October', 'November', 'December'
+    ])
+    fig, ax = plt.subplots(figsize=(12, 6))
+    bars = ax.bar(monthly_avg.index, monthly_avg.values, color='#3366CC')
+    # Add value labels on top of each bar
+    for bar in bars:
+        height = bar.get_height()
+        ax.text(bar.get_x() + bar.get_width()/2., height + 1,
+                f'{height:.1f}', ha='center', va='bottom', fontsize=9)
+    ax.set_title(title, fontsize=16)
+    ax.set_xlabel('Month', fontsize=12)
+    ax.set_ylabel('Average Value', fontsize=12)
+    plt.xticks(rotation=45, ha='right')
+    fig.tight_layout()
+    return fig
+# Image processing functions
+def apply_filter(image, filter_type):
+    img = np.array(image)
+    if filter_type == "Grayscale":
+        # Convert to grayscale
+        if len(img.shape) == 3 and img.shape[2] == 3:  # Ensure it's an RGB image
+            return Image.fromarray(np.dot(img[...,:3], [0.2989, 0.5870, 0.1140]).astype(np.uint8))
+        return image
+    elif filter_type == "Sepia":
+        # Apply sepia filter
+        if len(img.shape) == 3 and img.shape[2] == 3:  # Ensure it's an RGB image
+            sepia_filter = np.array([
+                [0.393, 0.769, 0.189],
+                [0.349, 0.686, 0.168],
+                [0.272, 0.534, 0.131]
+            ])
+            sepia_img = np.clip(np.dot(img[...,:3], sepia_filter.T), 0, 255).astype(np.uint8)
+            return Image.fromarray(sepia_img)
+        return image
+    elif filter_type == "Negative":
+        # Create negative
+        return Image.fromarray(255 - img)
+    elif filter_type == "Blur":
+        # Simple blur using PIL
+        from PIL import ImageFilter
+        return image.filter(ImageFilter.GaussianBlur(radius=3))
+    else:
+        return image
+# Define theme
+theme = gr.themes.Soft(
+    primary_hue="indigo",
+    secondary_hue="blue",
+    neutral_hue="gray"
+)
+# Main application
+def create_dashboard():
+    sample_data = generate_sample_data()
+    with gr.Blocks(theme=theme) as app:
+        gr.Markdown("# Interactive Data Visualization & Image Processing Dashboard")
+        with gr.Tab("Data Visualization"):
+            gr.Markdown("### Explore sample time series data with various visualizations")
+            with gr.Row():
+                with gr.Column(scale=1):
+                    refresh_btn = gr.Button("Generate New Data")
+                    custom_title = gr.Textbox(label="Chart Title", value="Time Series Analysis")
+                with gr.Column(scale=3):
+                    with gr.Tabs():
+                        with gr.TabItem("Line Chart"):
+                            line_chart = gr.Plot(label="Time Series Line Chart")
+                        with gr.TabItem("Bar Chart"):
+                            bar_chart = gr.Plot(label="Monthly Average Bar Chart")
+                        with gr.TabItem("Pie Chart"):
+                            pie_chart = gr.Plot(label="Category Distribution")
+            def update_charts(title):
+                data = generate_sample_data()
+                return {
+                    line_chart: generate_line_chart(data, title if title else "Time Series Data"),
+                    bar_chart: generate_bar_chart(data, title if title else "Monthly Averages"),
+                    pie_chart: generate_pie_chart(data, title if title else "Category Distribution")
+                }
+            refresh_btn.click(fn=update_charts, inputs=[custom_title], outputs=[line_chart, bar_chart, pie_chart])
+            custom_title.change(fn=update_charts, inputs=[custom_title], outputs=[line_chart, bar_chart, pie_chart])
+        with gr.Tab("Image Processing"):
+            gr.Markdown("### Apply filters to your images")
+            with gr.Row():
+                with gr.Column():
+                    input_image = gr.Image(label="Upload Image", type="pil")
+                    filter_type = gr.Radio(
+                        ["Original", "Grayscale", "Sepia", "Negative", "Blur"],
+                        label="Select Filter",
+                        value="Original"
+                    )
+                    apply_btn = gr.Button("Apply Filter")
+                with gr.Column():
+                    output_image = gr.Image(label="Filtered Image", type="pil")
+            def process_image(image, filter_choice):
+                if image is None:
+                    return None
+                if filter_choice == "Original":
+                    return image
+                return apply_filter(image, filter_choice)
+            apply_btn.click(fn=process_image, inputs=[input_image, filter_type], outputs=output_image)
+            filter_type.change(fn=process_image, inputs=[input_image, filter_type], outputs=output_image)
+        with gr.Tab("About"):
+            gr.Markdown(f"""
+            ## About this Application
+            This is a demo dashboard showcasing Gradio's capabilities for creating interactive data visualization and image processing applications.
+            ### Features:
+            - **Data Visualization**: Generate and explore random time series data with line charts, bar charts, and pie charts
+            - **Image Processing**: Apply various filters to your images
+            ### Technologies:
+            - Built with Python and Gradio
+            - Uses Matplotlib for data visualization
+            - PIL for image processing
+            Created on: {datetime.now().strftime("%B %d, %Y")}
+            """)
+        # Initialize charts on page load
+        app.load(
+            fn=update_charts,
+            inputs=[custom_title],
+            outputs=[line_chart, bar_chart, pie_chart],
+        )
+    return app
+if __name__ == '__main__':
+    dashboard = create_dashboard()
+    dashboard.launch()
+
+if __name__ == '__main__':
+    demo.launch()