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{ loadPageContent } from \"https://esm.town/v/charlypoly/browserbaseUtils\";\nimport { email } from \"https://esm.town/v/std/email?v=12\";\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\nimport { z } from \"npm:zod\";\nimport { zodToJsonSchema } from \"npm:zod-to-json-schema\";\n\nexport default async function(interval: Interval) {\n let html = await loadPageContent(\"https://news.ycombinator.com/\", { textContent: false });\n\n html = html.replace(/]*>[\\s\\S]*?<\\/style>/gi, \"\"); // remove useless context\n\n // Then, we define the shape of the data to extract\n // with a `zod` schema\n const schema = z.object({\n top: z\n .array(\n z.object({\n title: z.string(),\n points: z.number(),\n by: z.string(),\n url: z.string(),\n }),\n )\n .length(5)\n .describe(\"Top 5 stories on Hacker News\"),\n });\n\n // we create a OpenAI Tool that takes our schema as argument\n const extractContentTool: any = {\n type: \"function\",\n function: {\n name: \"extract_content\",\n description: \"Extracts the content from the given webpage(s)\",\n // we convert our zod Schema to a JSON Schema\n parameters: zodToJsonSchema(schema),\n },\n };\n\n const openai = new OpenAI();\n\n // We ask OpenAI to extract the content from the given web page.\n // The model will reach out to our `extract_content` tool and\n // by doing so, the model will extract the required data to satisfy\n // the requirement of `extract_content`s argument.\n const completion = await openai.chat.completions.create({\n model: \"gpt-4-turbo\",\n messages: [\n {\n role: \"system\",\n content: \"Extract the content from the given webpage(s)\",\n },\n { role: \"user\", content: html },\n ],\n tools: [extractContentTool],\n tool_choice: \"auto\",\n });\n\n // we retrieve the serialized arguments generated by OpenAI\n const result = completion.choices[0].message.tool_calls![0].function.arguments;\n\n console.log(result)\n \n // the serialized arguments are parsed into a valid JavaScript array of objects\n const parsed = schema.parse(JSON.parse(result));\n\n const completion2 = await openai.chat.completions.create({\n model: \"gpt-4-turbo\",\n messages: [\n { role: \"user\", content: `Transform the following JSON object into a nice HTML list: \\n \\n${JSON.stringify(result, null, 2)}\\n` },\n ],\n });\n\n console.log(completion2.choices[0].message.content)\n \n return\n await email({\n subject: \"Top 5 hacker news of the day\",\n html,\n });\n}","import { loadPageContent } from \"https://esm.town/v/charlypoly/browserbaseUtils\";\nimport { email } from \"https://esm.town/v/std/email?v=12\";\nimport { OpenAI } from \"https://esm.town/v/std/openai?v=4\";\nimport { z } from \"npm:zod\";\nimport { zodToJsonSchema } from \"npm:zod-to-json-schema\";\n\nexport default async function(interval: Interval) {\n let html = await loadPageContent(\"https://news.ycombinator.com/\", { textContent: false });\n\n html = html.replace(/]*>[\\s\\S]*?<\\/style>/gi, \"\"); // remove useless context\n\n // Then, we define the shape of the data to extract\n // with a `zod` schema\n const schema = z.object({\n top: z\n .array(\n z.object({\n title: z.string(),\n points: z.number(),\n by: z.string(),\n url: z.string(),\n }),\n )\n .length(5)\n .describe(\"Top 5 stories on Hacker News\"),\n });\n\n // we create a OpenAI Tool that takes our schema as argument\n const extractContentTool: any = {\n type: \"function\",\n function: {\n name: \"extract_content\",\n description: \"Extracts the content from the given webpage(s)\",\n // we convert our zod Schema to a JSON Schema\n parameters: zodToJsonSchema(schema),\n },\n };\n\n const openai = new OpenAI();\n\n // We ask OpenAI to extract the content from the given web page.\n // The model will reach out to our `extract_content` tool and\n // by doing so, the model will extract the required data to satisfy\n // the requirement of `extract_content`s argument.\n const completion = await openai.chat.completions.create({\n model: \"gpt-4-turbo\",\n messages: [\n {\n role: \"system\",\n content: \"Extract the content from the given webpage(s)\",\n },\n { role: \"user\", content: html },\n ],\n tools: [extractContentTool],\n tool_choice: \"auto\",\n });\n\n // we retrieve the serialized arguments generated by OpenAI\n const result = completion.choices[0].message.tool_calls![0].function.arguments;\n\n console.log(result);\n\n // the serialized arguments are parsed into a valid JavaScript array of objects\n const parsed = schema.parse(JSON.parse(result));\n\n const completion2 = await openai.chat.completions.create({\n model: \"gpt-4-turbo\",\n messages: [\n {\n role: \"system\",\n content: \"Generate HTML without the , and tags\",\n },\n {\n role: \"user\",\n content: `Transform the following JSON object into a nice HTML list: \\n \\n${\n JSON.stringify(result, null, 2)\n }\\n`,\n },\n ],\n });\n\n console.log(completion2.choices[0].message.content);\n\n return;\n await email({\n subject: \"Top 5 hacker news of the day\",\n html,\n 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Diff · v16