Create app.py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+
+# Define some basic convolution kernels
+KERNELS = {
+    "Edge Detection": np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]),
+    "Sharpen": np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]]),
+    "Blur": np.ones((3, 3)) / 9,
+    "Emboss": np.array([[-2, -1, 0], [-1, 1, 1], [0, 1, 2]]),
+}
+
+def apply_convolution(image, kernel_name):
+    # Convert image to grayscale for simplicity
+    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    kernel = KERNELS[kernel_name]
+    convolved = cv2.filter2D(gray, -1, kernel)
+    return convolved
+
+# Define Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Interactive Convolution Visualizer")
+    with gr.Row():
+        image_input = gr.Image(type="numpy", label="Upload Image")
+        kernel_dropdown = gr.Dropdown(choices=list(KERNELS.keys()), label="Select Kernel")
+    output_image = gr.Image(type="numpy", label="Processed Image")
+    process_button = gr.Button("Apply Convolution")
+
+    process_button.click(apply_convolution, inputs=[image_input, kernel_dropdown], outputs=output_image)
+
+# Launch the app
+demo.launch()