\n",
+ "
\n",
+ " \"\"\"\n",
+ " )\n",
+ "\n",
+ " \n",
+ " with gr.Row():\n",
+ " \n",
+ " \n",
+ " with gr.Column(variant='compact'):\n",
+ " # gr.Markdown(\"# Inputs: \")\n",
+ " \n",
+ " with gr.Row():\n",
+ " gr.Markdown(\"## Select a problem (objective): \")\n",
+ " img_key = gr.Markdown(value=\"\", visible=False)\n",
+ " \n",
+ " gallery = gr.Gallery(value=image_paths, label=\"Objective Functions\", \n",
+ " # height = 450, \n",
+ " object_fit='contain',\n",
+ " columns=3, rows=3, elem_id=\"gallery\")\n",
+ " \n",
+ " gr.Markdown(\"## Enter iteration Number: \")\n",
+ " iteration_input = gr.Slider(label=\"Iterations:\", minimum=15, maximum=50, step=1, value=15)\n",
+ " \n",
+ "\n",
+ " # Row for the Clear and Submit buttons\n",
+ " with gr.Row():\n",
+ " clear_button = gr.Button(\"Clear\")\n",
+ " submit_button = gr.Button(\"Submit\", variant=\"primary\")\n",
+ "\n",
+ " with gr.Column():\n",
+ " # gr.Markdown(\"# Outputs: \")\n",
+ " gr.Markdown(\"## Problem Formulation: \")\n",
+ " formulation = gr.Image(value='Formulation_default.png', height=150)\n",
+ " gr.Markdown(\"## Results: \")\n",
+ " gr.Markdown(\"The graph will plot the best observed data v.s. the time for the algorithm to run up until the iteration. The PFN-CBO shows the result of the realtime optimization running in the backend while the GP-CBO shows the stored data from our previous experiments since running GP-CBO will take longer time.\")\n",
+ " convergence_plot = gr.Plot(label=\"Convergence Plot\")\n",
+ "\n",
+ "\n",
+ "\n",
+ " def handle_select(evt: gr.SelectData):\n",
+ " selected_image = evt.value\n",
+ " key = evt.value['image']['orig_name']\n",
+ " formulation = 'Test_formulation.png'\n",
+ " print('here')\n",
+ " print(key)\n",
+ "\n",
+ " return key, formulation\n",
+ " \n",
+ " gallery.select(fn=handle_select, inputs=None, outputs=[img_key, formulation])\n",
+ "\n",
+ "\n",
+ " \n",
+ " submit_button.click(\n",
+ " submit_action,\n",
+ " inputs=[img_key, iteration_input],\n",
+ " # outputs= [radar_plot, convergence_plot],\n",
+ " outputs= convergence_plot,\n",
+ " \n",
+ " # progress=True # Enable progress tracking\n",
+ " \n",
+ " )\n",
+ "\n",
+ " clear_button.click(\n",
+ " clear_output,\n",
+ " inputs=None,\n",
+ " outputs=[gallery, convergence_plot, iteration_input, img_key, formulation]\n",
+ " ).then(\n",
+ " # Step 2: Reset the gallery to the original list\n",
+ " reset_gallery,\n",
+ " inputs=None,\n",
+ " outputs=gallery\n",
+ " )\n",
+ "\n",
+ " \n",
+ "\n",
+ "demo.launch()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "776c7ab2-96a1-4e22-9b4b-daf69960e3c4",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9d2c7c58-43b1-4e5b-9135-17683dac1788",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5d33a24c-818c-4023-bbbd-495f992a9d1a",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "764d0258-ec88-41d5-b5b5-e0bcb39ff313",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d2c35245-543c-4b82-8d12-04f3dda1468b",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "e3663adc-3e95-418b-bf50-0a372615cdd6",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "30886262-bd87-4760-a585-7872e071663f",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Gradio_test.ipynb b/Gradio_test.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..74e386929798d14fd4ba31a2105e2b9336de3138
--- /dev/null
+++ b/Gradio_test.ipynb
@@ -0,0 +1,569 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "4453c5ad-ec87-42e0-a6d5-e3fd3593aec2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Running on local URL: http://127.0.0.1:7891\n",
+ "Running on public URL: https://f714b6f956fb581264.gradio.live\n",
+ "\n",
+ "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ ""
+ ],
+ "text/plain": [
+ "Pre-trained Transformer for Constrained Bayesian Optimization
\n", + "Paper: \n", + " Fast and Accurate Bayesian Optimization with Pre-trained Transformers for Constrained Engineering Problems \n", + "
\n", + "\n", + "This is a demo for Bayesian Optimization using PFN (Prior-Data Fitted Networks). \n", + " Select your objective function by clicking on one of the check boxes below, then enter the iteration number to run the optimization process. \n", + " The results will be visualized in the radar chart and convergence plot.
\n", + " \n", + " \n", + " \n", + "\n", + "\n",
+ " ![\"Example](\"https://github.com/rosenyu304/BOEngineeringBenchmark/blob/main/Icons.png?raw=true\")
\n",
+ "\n",
+ "
\n",
+ " \"\"\"\n",
+ " )\n",
+ "\n",
+ " selected_objective = gr.State(None) # To store the selected objective function\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " with gr.Row():\n",
+ " \n",
+ " objective_checkbox_group = gr.CheckboxGroup(\n",
+ " choices=[\"JLH1\", \"JLH2\", \"GKXWC1\", \"GKXWC2\", \"Ackley2D\", \"Ackley6D\", \"Ackley10D\", \"Keane Bump\", \"Three Truss\", \"Reinforced Concrete Beam\", \"Pressure Vessel\", \"Welded Beam\", \"Speed Reducer\", \"Car\"],\n",
+ " label=\"Select the design problem:\"\n",
+ " )\n",
+ " with gr.Row():\n",
+ " iteration_input = gr.Number(label=\"Enter Iteration Number:\", value=10)\n",
+ " \n",
+ "\n",
+ " # Row for the Clear and Submit buttons\n",
+ " with gr.Row():\n",
+ " clear_button = gr.Button(\"Clear\")\n",
+ " submit_button = gr.Button(\"Submit\", variant=\"primary\")\n",
+ " \n",
+ " \n",
+ " with gr.Row():\n",
+ " with gr.Column():\n",
+ " radar_plot = gr.Plot(label=\"Resulting Design\")\n",
+ " with gr.Column():\n",
+ " convergence_plot = gr.Plot(label=\"Convergence Plot\")\n",
+ "\n",
+ "\n",
+ "\n",
+ " # Define actions for buttons\n",
+ " def clear_action():\n",
+ " return None, None, None\n",
+ "\n",
+ " def submit_action(objective_function_choices, iteration_input):\n",
+ " # Handle the case where multiple choices are selected\n",
+ " if len(objective_function_choices) > 0:\n",
+ " selected_function = objective_function_choices[0] # Assuming using the first selected function\n",
+ " return optimize(selected_function, iteration_input)\n",
+ " return None, None\n",
+ "\n",
+ " # Button click actions\n",
+ " clear_button.click(clear_action, outputs=[objective_checkbox_group, radar_plot, convergence_plot])\n",
+ " submit_button.click(\n",
+ " submit_action, \n",
+ " inputs=[objective_checkbox_group, iteration_input], \n",
+ " outputs=[radar_plot, convergence_plot]\n",
+ " )\n",
+ "\n",
+ "demo.launch(share=True)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "352d0291-93b4-43eb-b683-3d48776dc670",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "92ecbbe6-dea6-4e7f-aae1-f0d442dbda3b",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ba69b5f9-c52c-4c23-8645-c81c27f7a815",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "05789fba-2099-46b7-8675-64b7969427a1",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Running on local URL: http://127.0.0.1:7899\n",
+ "\n",
+ "To create a public link, set `share=True` in `launch()`.\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ ""
+ ],
+ "text/plain": [
+ "Pre-trained Transformer for Constrained Bayesian Optimization
\n", + "This is a demo for Bayesian Optimization using PFN (Prior-Data Fitted Networks). \n", + " Select your objective function by clicking on one of the check boxes below, then enter the iteration number to run the optimization process. \n", + " The results will be visualized in the radar chart and convergence plot.
\n", + "\n",
+ "\n",
+ "