[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_164":165,"_166":167},"routes/_app.x.$handle.$name",{"_95":94,"_32":45,"_103":85,"_27":104,"_13":14,"_105":-5,"_11":106,"_107":108,"_109":110,"_111":112,"_113":114,"_20":-5,"_115":-5,"_116":-5,"_117":25,"_118":119,"_120":49,"_121":-5,"_122":-5,"_123":124},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_43":44,"_32":45,"_38":46,"_47":-5,"_48":49,"_50":51,"_52":51,"_53":-5,"_54":-5,"_18":55,"_56":31,"_57":49,"_58":-5,"_59":-5,"_60":61,"_62":63,"_64":-5,"_65":-5,"_66":-5,"_67":-5,"_68":69,"_70":69,"_71":72,"_73":72,"_74":72,"_75":69,"_76":69,"_77":78,"_79":80,"_81":82,"_83":25},"version",11,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":37,"_38":39,"_40":14,"_41":42,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.tsx","latestPath","fileId","cb801564-26db-11f0-a5b3-569c3dd06744","name","fileType","http","content","import { marked } from \"https://cdn.jsdelivr.net/npm/marked/lib/marked.esm.js\";\nimport { generateMerchantTransactionData } from \"https://esm.town/v/mjweaver01/generateMerchantTransactionData\";\n\nimport { Hono } from \"npm:hono\";\nimport { OpenAI } from \"npm:openai\";\n\nconst trendGPT = async (data, onData) => {\n const openai = new OpenAI();\n\n // Start the OpenAI stream\n const chatStream = await openai.chat.completions.create({\n messages: [\n {\n role: \"system\",\n content: `\n You are a helpful assistant for a data analysis...\n `.replaceAll(\"\\n\", \"\"), // shortened for brevity\n },\n {\n role: \"user\",\n content: JSON.stringify(data),\n },\n ],\n model: \"chatgpt-4o-latest\",\n max_tokens: 4000,\n temperature: 0,\n stream: true, // Enable streaming\n });\n\n // The OpenAI SDK provides an async iterator for streaming\n let result = \"\";\n try {\n for await (const chunk of chatStream) {\n const content = chunk.choices?.[0]?.delta?.content || \"\";\n result += content;\n if (onData) onData(result); // Stream the result to the client\n }\n } catch (err) {\n console.error(\"Error during stream processing:\", err);\n throw new Error(\"An error occurred while streaming the response.\");\n }\n\n return result;\n};\n\nconst trendsData = generateMerchantTransactionData();\nconst app = new Hono();\nconst styles = \"\";\nconst mermaidClient = `\n \n `;\nconst renderer = new marked.Renderer();\nrenderer.code = (code) => {\n if (code.lang === \"mermaid\") return `

${code.text}

`;\n return `

${code.text}

${code.text}

`;\n return `

${code.text}

`;\n};\n\napp.get(\"/\", async (c) => {\n let htmlContent = styles + mermaidClient;\n try {\n await trendGPT(JSON.stringify(trendsData), (streamedData) => {\n htmlContent = marked.parse(streamedData, {\n breaks: true,\n renderer,\n });\n c.html(htmlContent); // Continuously update the HTML content as data streams in\n });\n } catch (error) {\n console.error(\"Error while rendering:\", error);\n return c.text(\"An error occurred while rendering the response.\", 500);\n }\n\n return c.html(htmlContent);\n});\n\napp.post(\"/ask\", async (c) => {\n const body = await c.req.parseBody();\n let responseText = \"\";\n try {\n await trendGPT(JSON.stringify(body), (streamedData) => {\n responseText += streamedData;\n c.text(responseText); // Continuously update the response as data streams in\n });\n } catch (error) {\n console.error(\"Error while processing request:\", error);\n return c.text(\"An error occurred while processing your request.\", 500);\n }\n\n return 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FraudTrendsGPT\n\n> Generate real-time Fraud Trend Reports using GPT-4o\n\n## Goal\n\nThis agent is designed to find trends in merchant transaction reports, and produce a real time report.\n\nReports are complete with relevant data tables and Mermaid charts.\n\n## Semi-Data-Agnostic\n\nThis agent is semi-agnostic to the data provided. \n\nMeaning, it will provide a report so long as the provided data is shaped similarly, or the prompt is updated to support the new data shape.\n\n## Agent Reusability\n\nMoreover, this agent can be rewritten for any number of use cases. \n\nWith some variable renaming, and rewriting of the prompt, this should produce very accurate data analytic reports for any data provided.\n\n![Screenshot 2024-08-22 at 7.32.16 PM.png](https://imagedelivery.net/iHX6Ovru0O7AjmyT5yZRoA/fb6a3ff0-59ff-441d-9c36-3db88cdce200/public)\n![Screenshot 2024-08-22 at 7.33.22 PM.png](https://imagedelivery.net/iHX6Ovru0O7AjmyT5yZRoA/94532123-291c-4208-0482-c28c64f51400/public)\n\n\nMigrated from folder: 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Diff · v11