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{
"cells": [
{
"cell_type": "markdown",
"id": "a71ed017-e1b0-4299-88b3-f0eb05adc4df",
"metadata": {},
"source": [
"# The Price is Right\n",
"\n",
"The final step is to build a User Interface\n",
"\n",
"We will use more advanced aspects of Gradio - building piece by piece."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "614c6202-4575-448d-98ee-78b735775d2b",
"metadata": {},
"outputs": [],
"source": [
"import gradio as gr\n",
"from deal_agent_framework import DealAgentFramework\n",
"from agents.deals import Opportunity, Deal"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0534e714-5a9c-45c6-998c-3472ac0bb8b5",
"metadata": {},
"outputs": [],
"source": [
"with gr.Blocks(title=\"The Price is Right\", fill_width=True) as ui:\n",
"\n",
" with gr.Row():\n",
" gr.Markdown('<div style=\"text-align: center;font-size:24px\">The Price is Right - Deal Hunting Agentic AI</div>')\n",
" with gr.Row():\n",
" gr.Markdown('<div style=\"text-align: center;font-size:14px\">Autonomous agent framework that finds online deals, collaborating with a proprietary fine-tuned LLM deployed on Modal, and a RAG pipeline with a frontier model and Chroma.</div>')\n",
" \n",
"\n",
"ui.launch(inbrowser=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "18c12c10-750c-4da3-8df5-f2bc3393f9e0",
"metadata": {},
"outputs": [],
"source": [
"# Updated to change from height to max_height due to change in Gradio v5\n",
"# With much thanks to student Ed B. for raising this\n",
"\n",
"with gr.Blocks(title=\"The Price is Right\", fill_width=True) as ui:\n",
"\n",
" initial_deal = Deal(product_description=\"Example description\", price=100.0, url=\"https://cnn.com\")\n",
" initial_opportunity = Opportunity(deal=initial_deal, estimate=200.0, discount=100.0)\n",
" opportunities = gr.State([initial_opportunity])\n",
"\n",
" def get_table(opps):\n",
" return [[opp.deal.product_description, opp.deal.price, opp.estimate, opp.discount, opp.deal.url] for opp in opps]\n",
"\n",
" with gr.Row():\n",
" gr.Markdown('<div style=\"text-align: center;font-size:24px\">\"The Price is Right\" - Deal Hunting Agentic AI</div>')\n",
" with gr.Row():\n",
" gr.Markdown('<div style=\"text-align: center;font-size:14px\">Deals surfaced so far:</div>')\n",
" with gr.Row():\n",
" opportunities_dataframe = gr.Dataframe(\n",
" headers=[\"Description\", \"Price\", \"Estimate\", \"Discount\", \"URL\"],\n",
" wrap=True,\n",
" column_widths=[4, 1, 1, 1, 2],\n",
" row_count=10,\n",
" col_count=5,\n",
" max_height=400,\n",
" )\n",
"\n",
" ui.load(get_table, inputs=[opportunities], outputs=[opportunities_dataframe])\n",
"\n",
"ui.launch(inbrowser=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "87106328-a17a-447e-90b9-c547613468da",
"metadata": {},
"outputs": [],
"source": [
"agent_framework = DealAgentFramework()\n",
"\n",
"with gr.Blocks(title=\"The Price is Right\", fill_width=True) as ui:\n",
"\n",
" initial_deal = Deal(product_description=\"Example description\", price=100.0, url=\"https://cnn.com\")\n",
" initial_opportunity = Opportunity(deal=initial_deal, estimate=200.0, discount=100.0)\n",
" opportunities = gr.State([initial_opportunity])\n",
"\n",
" def get_table(opps):\n",
" return [[opp.deal.product_description, opp.deal.price, opp.estimate, opp.discount, opp.deal.url] for opp in opps]\n",
"\n",
" def do_select(opportunities, selected_index: gr.SelectData):\n",
" row = selected_index.index[0]\n",
" opportunity = opportunities[row]\n",
" agent_framework.planner.messenger.alert(opportunity)\n",
"\n",
" with gr.Row():\n",
" gr.Markdown('<div style=\"text-align: center;font-size:24px\">\"The Price is Right\" - Deal Hunting Agentic AI</div>')\n",
" with gr.Row():\n",
" gr.Markdown('<div style=\"text-align: center;font-size:14px\">Deals surfaced so far:</div>')\n",
" with gr.Row():\n",
" opportunities_dataframe = gr.Dataframe(\n",
" headers=[\"Description\", \"Price\", \"Estimate\", \"Discount\", \"URL\"],\n",
" wrap=True,\n",
" column_widths=[4, 1, 1, 1, 2],\n",
" row_count=10,\n",
" col_count=5,\n",
" max_height=400,\n",
" )\n",
"\n",
" ui.load(get_table, inputs=[opportunities], outputs=[opportunities_dataframe])\n",
" opportunities_dataframe.select(do_select, inputs=[opportunities], outputs=[])\n",
"\n",
"ui.launch(inbrowser=True)"
]
},
{
"cell_type": "markdown",
"id": "ecfed67b-ebcb-4e17-ad15-a7151f940119",
"metadata": {},
"source": [
"# Time for the code\n",
"\n",
"And now we'll move to the price_is_right.py code, followed by price_is_right_final.py"
]
},
{
"cell_type": "markdown",
"id": "d783af8a-08a8-4e59-886a-7ca32f16bcf5",
"metadata": {},
"source": [
"# Running the final product\n",
"\n",
"## Just hit shift + enter in the next cell, and let the deals flow in!!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "48506465-1c7a-433f-a665-b277a8b4665c",
"metadata": {},
"outputs": [],
"source": [
"!python price_is_right_final.py"
]
},
{
"cell_type": "markdown",
"id": "331a2044-566f-4866-be4d-7542b7dfdf3f",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../thankyou.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#090;\">CONGRATULATIONS AND THANK YOU!!!</h2>\n",
" <span style=\"color:#090;\">\n",
" It's so fabulous that you've made it to the end! My heartiest congratulations. Please stay in touch! I'm <a href=\"https://www.linkedin.com/in/eddonner/\">here on LinkedIn</a> if we're not already connected. And my editor would be cross with me if I didn't mention one more time: it makes a HUGE difference when students rate this course on Udemy - it's one of the main ways that Udemy decides whether to show it to others. <br/><br/>Thanks once again for working all the way through the course, and I'm excited to hear all about your career as an LLM Engineer.\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "096397f9-1215-4814-ab4b-e32002ff4ceb",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
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},
"language_info": {
"codemirror_mode": {
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"file_extension": ".py",
"mimetype": "text/x-python",
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"nbconvert_exporter": "python",
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"nbformat": 4,
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