{ "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", "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": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 5 }