199 lines
7.3 KiB
Plaintext
199 lines
7.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a71ed017-e1b0-4299-88b3-f0eb05adc4df",
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"metadata": {},
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"source": [
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"# The Price is Right\n",
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"\n",
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"The final step is to build a User Interface\n",
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"\n",
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"We will use more advanced aspects of Gradio - building piece by piece."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "614c6202-4575-448d-98ee-78b735775d2b",
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"metadata": {},
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"outputs": [],
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"source": [
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"import gradio as gr\n",
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"from deal_agent_framework import DealAgentFramework\n",
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"from agents.deals import Opportunity, Deal"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0534e714-5a9c-45c6-998c-3472ac0bb8b5",
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"metadata": {},
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"outputs": [],
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"source": [
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"with gr.Blocks(title=\"The Price is Right\", fill_width=True) as ui:\n",
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"\n",
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" with gr.Row():\n",
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" gr.Markdown('<div style=\"text-align: center;font-size:24px\">The Price is Right - Deal Hunting Agentic AI</div>')\n",
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" with gr.Row():\n",
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" 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",
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" \n",
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"\n",
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"ui.launch(inbrowser=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "18c12c10-750c-4da3-8df5-f2bc3393f9e0",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Updated to change from height to max_height due to change in Gradio v5\n",
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"# With much thanks to student Ed B. for raising this\n",
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"\n",
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"with gr.Blocks(title=\"The Price is Right\", fill_width=True) as ui:\n",
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"\n",
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" initial_deal = Deal(product_description=\"Example description\", price=100.0, url=\"https://cnn.com\")\n",
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" initial_opportunity = Opportunity(deal=initial_deal, estimate=200.0, discount=100.0)\n",
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" opportunities = gr.State([initial_opportunity])\n",
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"\n",
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" def get_table(opps):\n",
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" return [[opp.deal.product_description, opp.deal.price, opp.estimate, opp.discount, opp.deal.url] for opp in opps]\n",
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"\n",
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" with gr.Row():\n",
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" gr.Markdown('<div style=\"text-align: center;font-size:24px\">\"The Price is Right\" - Deal Hunting Agentic AI</div>')\n",
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" with gr.Row():\n",
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" gr.Markdown('<div style=\"text-align: center;font-size:14px\">Deals surfaced so far:</div>')\n",
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" with gr.Row():\n",
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" opportunities_dataframe = gr.Dataframe(\n",
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" headers=[\"Description\", \"Price\", \"Estimate\", \"Discount\", \"URL\"],\n",
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" wrap=True,\n",
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" column_widths=[4, 1, 1, 1, 2],\n",
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" row_count=10,\n",
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" col_count=5,\n",
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" max_height=400,\n",
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" )\n",
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"\n",
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" ui.load(get_table, inputs=[opportunities], outputs=[opportunities_dataframe])\n",
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"\n",
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"ui.launch(inbrowser=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "87106328-a17a-447e-90b9-c547613468da",
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"metadata": {},
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"outputs": [],
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"source": [
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"agent_framework = DealAgentFramework()\n",
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"\n",
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"with gr.Blocks(title=\"The Price is Right\", fill_width=True) as ui:\n",
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"\n",
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" initial_deal = Deal(product_description=\"Example description\", price=100.0, url=\"https://cnn.com\")\n",
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" initial_opportunity = Opportunity(deal=initial_deal, estimate=200.0, discount=100.0)\n",
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" opportunities = gr.State([initial_opportunity])\n",
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"\n",
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" def get_table(opps):\n",
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" return [[opp.deal.product_description, opp.deal.price, opp.estimate, opp.discount, opp.deal.url] for opp in opps]\n",
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"\n",
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" def do_select(opportunities, selected_index: gr.SelectData):\n",
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" row = selected_index.index[0]\n",
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" opportunity = opportunities[row]\n",
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" agent_framework.planner.messenger.alert(opportunity)\n",
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"\n",
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" with gr.Row():\n",
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" gr.Markdown('<div style=\"text-align: center;font-size:24px\">\"The Price is Right\" - Deal Hunting Agentic AI</div>')\n",
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" with gr.Row():\n",
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" gr.Markdown('<div style=\"text-align: center;font-size:14px\">Deals surfaced so far:</div>')\n",
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" with gr.Row():\n",
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" opportunities_dataframe = gr.Dataframe(\n",
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" headers=[\"Description\", \"Price\", \"Estimate\", \"Discount\", \"URL\"],\n",
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" wrap=True,\n",
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" column_widths=[4, 1, 1, 1, 2],\n",
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" row_count=10,\n",
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" col_count=5,\n",
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" max_height=400,\n",
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" )\n",
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"\n",
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" ui.load(get_table, inputs=[opportunities], outputs=[opportunities_dataframe])\n",
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" opportunities_dataframe.select(do_select, inputs=[opportunities], outputs=[])\n",
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"\n",
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"ui.launch(inbrowser=True)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ecfed67b-ebcb-4e17-ad15-a7151f940119",
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"metadata": {},
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"source": [
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"# Time for the code\n",
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"\n",
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"And now we'll move to the price_is_right.py code, followed by price_is_right_final.py"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d783af8a-08a8-4e59-886a-7ca32f16bcf5",
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"metadata": {},
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"source": [
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"# Running the final product\n",
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"\n",
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"## Just hit shift + enter in the next cell, and let the deals flow in!!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "48506465-1c7a-433f-a665-b277a8b4665c",
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"metadata": {},
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"outputs": [],
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"source": [
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"!python price_is_right_final.py"
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]
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},
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{
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"cell_type": "markdown",
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"id": "331a2044-566f-4866-be4d-7542b7dfdf3f",
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"metadata": {},
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"source": [
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"<table style=\"margin: 0; text-align: left;\">\n",
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" <tr>\n",
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" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
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" <img src=\"../thankyou.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
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" </td>\n",
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" <td>\n",
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" <h2 style=\"color:#090;\">CONGRATULATIONS AND THANK YOU!!!</h2>\n",
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" <span style=\"color:#090;\">\n",
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" 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",
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" </span>\n",
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" </td>\n",
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" </tr>\n",
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"</table>"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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