[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8,"_9":10},"actionData","errors","root",{"_165":166,"_167":168},"routes/_app.x.$handle.$name",{"_97":96,"_32":44,"_105":83,"_27":106,"_13":14,"_107":-5,"_11":108,"_109":110,"_111":112,"_113":114,"_115":116,"_20":-5,"_117":118,"_119":120,"_121":25,"_122":123,"_124":56,"_125":-5,"_126":-5,"_127":128},"routes/_app.x.$handle.$name.branch.$branch.version.$version.diff",{"_11":12,"_13":14,"_15":16,"_17":-5,"_18":-5},"project",{"_42":43,"_32":44,"_37":45,"_46":-5,"_47":25,"_48":49,"_50":49,"_51":-5,"_52":-5,"_18":53,"_54":31,"_55":56,"_57":56,"_58":-5,"_59":60,"_61":62,"_63":-5,"_64":-5,"_65":-5,"_66":-5,"_67":68,"_69":68,"_70":71,"_72":71,"_73":71,"_74":68,"_75":68,"_76":77,"_78":79,"_80":81,"_82":25},"version",4,"changes",[19],"aiChatInfo","userId",{"_20":21,"_22":23,"_24":25,"_26":25,"_27":28,"_29":28},"file",{"_30":31,"_32":28,"_33":34,"_35":36,"_37":38,"_39":14,"_40":41,"_24":25},"previous",{"_30":31,"_32":28,"_33":34,"_35":36},"deleted",false,"deletedHere","path","main.ts","latestPath","fileId","019a0b64-21e6-738e-a03d-9a46214a8dac","name","fileType","http","content","// @ts-nocheck\n// Taskflow AI - Professional Autonomous Agent Factory\n// A modern, user-friendly platform for AI-powered task execution\n// STYLING APPLIED FROM: TaskflowEnterprise React Component\n// FINAL INTEGRATED VERSION (Parts 1-6)\nimport { Hono } from \"npm:hono@4.4.12\";\nimport { OpenAI } from \"https://esm.town/v/std/openai\";\nimport type { Context } from \"npm:hono@4.4.12\";\nimport { streamText } from \"npm:hono@4.4.12/streaming\";\n\n// --- TYPE DEFINITIONS ---\ninterface CompanyContext {\n company_name: string;\n company_description: string;\n use_case: string;\n language: string;\n industry?: string;\n}\ninterface ConversationHistory {\n role: \"assistant\" | \"user\";\n content: string;\n}\ninterface GatherBody {\n task: string;\n domain: string;\n history: ConversationHistory[];\n}\ninterface DynamicPromptBody {\n occupation_title: string;\n task: string;\n company_context: CompanyContext;\n}\ninterface ExecuteBody {\n refined_prompt: string;\n user_inputs: Record;\n company_context: CompanyContext;\n category: string;\n}\n\n// --- CENTRALIZED CONFIGURATION ---\nconst config = {\n models: {\n major: \"gpt-4o\",\n minor: \"gpt-4o-mini\",\n },\n prompts: {\n INFORMATION_GATHERER:\n `You are a native JSON speaking expert information gatherer designed to acquire all necessary details for a given task and domain. Your goal is to ask the fewest, most concise, and natural questions possible, one at a time. If information is missing or refused, make a reasonable assumption or broaden your scope. Iteratively ask questions and integrate responses until all required information for the {{task}} within the {{domain}} is gathered.\nRULES\nPrimary Goal: Ask single, concise questions to gather the minimum required information for the task, aiming for completion efficiently.\nDecision Point: After analyzing the conversation history, decide if you have enough information.If YES: Your immediate response MUST be the token \"COMPLETE\" followed by a single, minified JSON object of all gathered data. Do not ask for confirmation. Example: \\`COMPLETE\\`{\"party_date\":\"next Saturday\",\"guests\":50,\"location\":\"rented hall\"}\nIf NO: You are missing critical information. Proceed to the next rule.\nQuestion Formulation: If more info is needed, formulate the single most impactful and concise question.\nOutput Format (Questions): The question must be a short, direct interrogative. DO NOT add conversational filler, commentary, apologies, or instructions.\nCONTEXT\nTask: {{task}}\nDomain: {{domain}}\nConversation History:\n{{history}}`,\n PROMPT_REFINER:\n `You are a native JSON speaking prompt engineer. Your mission is to transmute occupational tasks into direct, end-to-end deliverables that align with *industry-standard formats* for that type of output within the defined industry.\n# CORE RULE:\nReturn a prompt with {{variables}} placeholders, but ensure that the output is a polished, fully formed artifact recognized in this field as a professional deliverable.\n# INSTRUCTIONS\n1. Persona: Start with \"You are a top 0.1% [Occupation]...\"\n2. Objective: Translate the Task into a directive that *produces the actual output* (e.g., for accountants → \"Audit Report\", for UX designers → \"Wireframe Specification\", for lawyers → \"Legal Agreement\").\n3. Context: Incorporate the Company Context to ground the deliverable in the business setting.\n4. Placeholders: Convert user-supplied inputs into descriptive {{snake_case_placeholders}}. Include at least 2.\n5. Delivery Rules:\n - Output must follow the **standard deliverable structure** in that domain (document sections, layout, terminology).\n - Exclude plans, commentary, or explanations.\n - If uncertain, assume the most common professional output format for that industry and task type.\n6. Output Format: The return prompt must define a clear final output type (e.g., “Return a structured PDF-ready report”).\n7. Mandate: End with a directive like “Produce the deliverable now.”\nReturn a JSON object:{\"refined_prompt\": \"...\"}`,\n MASTER_PROMPT_ENGINEER:\n `You are a native JSON speaking master prompt architect. Construct a comprehensive, self-contained blueprint for another AI to execute a task flawlessly, conforming to **industry-standard deliverable formats**.\n# INSTRUCTIONS\n1. Deconstruct Task\n2. Define Expert Persona\n3. Contextualize Industry-Specific Deliverable Expectations\n4. Establish Logic and Negative Constraints\n5. Define Output Schema and Examples\n6. Conclude with Execution Command\nReturn the blueprint as markdown, no JSON wrapper.`,\n INDUSTRY_GENERATOR:\n `You are a native JSON speaking expert business analyst. Generate a list of professional industries most relevant to the given company.\nRULES\nAnalyze Context: Deeply analyze the provided company context (name, description, use case) to understand their core business.\nGenerate Industries: Produce a list of 10-12 diverse yet highly relevant industries.\nOutput Format: Your entire response MUST be a single, minified JSON object with one key: \"industries\". The value must be an array of strings.\nCOMPANY CONTEXT\n{{company_context}}`,\n DYNAMIC_LIST_GENERATOR:\n `You are a native JSON speaking expert AI that generates context-aware lists for professional applications. Each generated entity (occupation or task) must map directly to a tangible, industry-standard deliverable format used in that field.\n# RULES\n1. **Analyze Context:** Deeply analyze 'company_context' and, if available, 'industry' or 'occupation' to determine what outputs are considered professional and standard for that domain (e.g., for Marketing → \"Campaign Brief\", for Software → \"Technical Specification\", for Legal → \"Contract Draft\").\n2. **Generate List:**\n - If 'generation_type' is \"occupations\", list specific professional roles relevant to that industry.\n - If 'generation_type' is \"tasks\", ONLY generate tasks that culminate in a clear, recognized output (e.g., \"Create a press release\", \"Develop a system architecture diagram\", \"Draft a privacy policy\").\n3. **Output Standardization:** Each task must inherently correspond to a recognized document, presentation, report, or artifact with a conventional structure in that field.\n4. **Output Format:** Strict JSON — one key \"generated_list\", value = array of objects with:\n - \"name\" (string): concise, professional task or role title\n - \"description\" (string): one line describing the standard deliverable expected\n5. **Relevance and Specificity:** Avoid abstract or open-ended tasks (e.g., \"Think about\", \"Plan for\", \"Brainstorm\"). Use only tasks where a complete deliverable output exists.\n6. **Input is JSON:** You must parse the JSON input before generating.`,\n EVALUATOR_AGENT:\n `You are a native JSON speaking, meticulous, and impartial AI QA agent. Given a task briefing, success criteria, and AI output, you will:\nScore: Score the output 1-10 on each criterion.\nCritique: Provide a concise, professional critique of its strengths and weaknesses.\nSCORING RULES\nGut Check: Before all else, if the output has obvious placeholders ('example.com', '[Insert Name Here]') or is extremely generic, penalize heavily on \"Plausibility and Specificity.\"\nPenalize Non-Deliverables: If the output is a plan or commentary instead of the final deliverable, penalize heavily (scores <= 3) on the relevant criterion.\nOUTPUT FORMAT\nYour entire response MUST be a single, minified JSON object: {\"scores\": [{\"criterion\": \"...\", \"score\": ...}], \"critique\": \"...\"}. You MUST write the critique in the requested language. Add no other text or markdown.`,\n REFINER_AGENT:\n `You are a top-tier \"Refiner\" agent. You have your original task briefing, your first draft, and a critique from a QA agent. Your SOLE mission is to rewrite the first draft to fully address every point in the critique, elevating it to a 10/10 standard.\nRULES\nAnalyze and Integrate: Apply all suggested changes from the critique to the first draft.\nNo Commentary: Do not apologize, explain changes, or include meta commentary.\nFinal Deliverable Only: Your output must be only the new, improved, final version.\nLanguage: Write the final output in the requested language.`,\n SUCCESS_CRITERIA_GENERATOR:\n `You are a native JSON speaking expert QA analyst. Your task is to generate a concise list of success criteria for evaluating an AI-generated document.\nRULES\nAnalyze Task: Deeply analyze the task briefing to understand the desired output's purpose and requirements.\nGenerate Criteria: Create 4-6 specific, measurable, relevant success criteria as short strings.\nOutput Format: Your response MUST be a single, minified JSON object: {\"criteria\": [\"...\", \"...\", ...]}.\nTASK BRIEFING\n{{task_briefing}}`,\n },\n seedTemplates: {\n \"default\":\n `You are a top-tier professional expert. Your task is to produce a complete, industry-standard deliverable.`,\n \"marketing\":\n `You are an expert marketing strategist. Produce a professional deliverable ready for a client presentation. The output must adhere to standard marketing document formats (e.g., brief, proposal, copy deck).`,\n \"development\":\n `You are a senior software architect. Produce the technical deliverable requested. The output must be a complete, well-structured technical artifact (e.g., specification, API documentation, architecture plan) using industry-standard formatting (like Markdown).`,\n \"operations\":\n `You are a director of operations. Produce a formal business document for the task. This must be a structured report, plan, or policy, ready for internal distribution and executive review.`,\n \"general\":\n `You are a versatile business professional. Produce a polished, well-structured document that directly fulfills the task requirements, formatted for immediate professional use.`,\n \"prompt_building\":\n `You are a native JSON speaking master prompt architect. Construct a comprehensive, self-contained blueprint for another AI to execute a task flawlessly, conforming to industry-standard deliverable formats.`,\n },\n};\n\n// --- STATIC DATA ---\nconst taskCategories = [\n {\n id: \"thinking\",\n name: \"Strategic Planning\",\n description: \"Generate strategies, outlines, or comprehensive analyses.\",\n icon: \"🧠\",\n },\n {\n id: \"prompt_building\",\n name: \"Prompt Engineering\",\n description:\n \"Create detailed, expert-level prompts for AI systems that yield production-grade outputs.\",\n icon: \"🛠️\",\n },\n {\n id: \"document_creation\",\n name: \"Professional Documents\",\n description:\n \"Produce structured, industry-standard documents or artifacts ready for immediate professional use.\",\n icon: \"📄\",\n },\n];\n\n// --- BACKEND SERVICES ---\nconst services = {\n async callOpenAI(\n systemPrompt: string,\n userContent: string | object,\n options: {\n model?: string;\n isJson?: boolean;\n stream?: boolean;\n c?: Context;\n } = {},\n ) {\n const {\n model = config.models.major,\n isJson = false,\n stream = false,\n c = null,\n } = options;\n const openai = new OpenAI();\n const messages: any[] = [{ role: \"system\", content: systemPrompt }, {\n role: \"user\",\n content: typeof userContent === \"string\"\n ? userContent\n : JSON.stringify(userContent),\n }];\n const requestPayload: any = { model, messages, stream };\n if (isJson) requestPayload.response_format = { type: \"json_object\" };\n try {\n const completion = await openai.chat.completions.create(requestPayload);\n if (stream && c) {\n return streamText(c, async (s) => {\n for await (const part of completion) {\n await s.write(part.choices[0]?.delta?.content || \"\");\n }\n });\n }\n if (isJson && c) {\n return c.json(JSON.parse(completion.choices[0].message.content));\n }\n return completion;\n } catch (e) {\n console.error(`Error calling OpenAI: ${e.message}`);\n throw new Error(\"AI service failed.\");\n }\n },\n};\n\n// --- FRONTEND GENERATION ---\nfunction generateHtml(sourceUrl: string): string {\n const languages = {\n \"English\": \"en-US\",\n \"Spanish\": \"es-ES\",\n \"French\": \"fr-FR\",\n \"German\": \"de-DE\",\n \"Japanese\": \"ja-JP\",\n \"Chinese (Simplified)\": \"zh-CN\",\n };\n const languageOptions = Object.entries(languages).map(([name, code]) =>\n ``\n ).join(\"\");\n const taskCategoriesJson = JSON.stringify(taskCategories);\n\n return `Taskflow AI | Intelligent Task Automation

Great job!

You're making excellent progress.

Taskflow AI

`;\n}\n\n// --- HONO APPLICATION SETUP ---\nconst app = new Hono();\n\n// --- API ROUTES ---\n\n/* ----------------------------------------\n Base Route\n----------------------------------------- */\napp.get(\"/\", (c: Context) => c.html(generateHtml(c.req.url)));\n\n/* ----------------------------------------\n Industry Generation\n----------------------------------------- */\napp.post(\"/api/industries\", async (c: Context) => {\n const { company_context } = await c.req.json();\n if (!company_context) {\n return c.json({ error: \"Company context is required\" }, 400);\n }\n const userContent = `Company Context: ${JSON.stringify(company_context)}`;\n return services.callOpenAI(config.prompts.INDUSTRY_GENERATOR, userContent, {\n c,\n isJson: true,\n model: config.models.minor,\n });\n});\n\n/* ----------------------------------------\n Information Gatherer\n----------------------------------------- */\napp.post(\"/api/gather\", async (c: Context) => {\n const { task, domain, history } = await c.req.json();\n const historyString = history.map((h) =>\n `${h.role === \"assistant\" ? \"AI\" : \"User\"}: ${h.content}`\n ).join(\"\\n\");\n const prompt = config.prompts.INFORMATION_GATHERER.replace(\"{{task}}\", task)\n .replace(\"{{domain}}\", domain).replace(\n \"{{history}}\",\n historyString ||\n \"No history yet. Start with your most important question.\",\n );\n try {\n const completion = await services.callOpenAI(\n \"You are a helpful assistant following strict output rules.\",\n prompt,\n { model: config.models.minor },\n );\n const responseText = completion.choices[0].message.content.trim();\n if (responseText.startsWith(\"COMPLETE\")) {\n const jsonPart = responseText.replace(\"COMPLETE\", \"\");\n return c.json({ status: \"complete\", data: JSON.parse(jsonPart) });\n } else {\n return c.json({ status: \"in-progress\", question: responseText });\n }\n } catch (e) {\n console.error(\"Gathering agent failed:\", e);\n return c.json({\n status: \"in-progress\",\n question:\n \"I'm having a little trouble with that. Could you please rephrase your last answer?\",\n }, 500);\n }\n});\n\n/* ----------------------------------------\n Dynamic Generation (Occupations / Tasks)\n----------------------------------------- */\napp.post(\"/api/generate/:type\", async (c: Context) => {\n const { type } = c.req.param();\n if (type !== \"occupations\" && type !== \"tasks\") {\n return c.json({ error: \"Invalid generation type\" }, 400);\n }\n const body = await c.req.json();\n const payload = {\n generation_type: type,\n company_context: body.company_context,\n ...(type === \"occupations\" && { industry: body.industry }),\n ...(type === \"tasks\" && { occupation: body.occupation }),\n constraints: {\n deliverable_focus:\n \"Only generate tasks that culminate in recognized, professional deliverables or artifacts for that industry (reports, specifications, proposals, contracts, policies, etc.)\",\n reject_types: [\n \"brainstorming\",\n \"ideation\",\n \"planning-only\",\n \"summarization\",\n \"discussion\",\n ],\n },\n };\n return services.callOpenAI(config.prompts.DYNAMIC_LIST_GENERATOR, payload, {\n c,\n isJson: true,\n });\n});\n\n/* ----------------------------------------\n Dynamic Prompt Creation\n----------------------------------------- */\napp.post(\"/api/prompt/dynamic\", async (c: Context) => {\n const { occupation_title, task, company_context } = await c.req.json<\n DynamicPromptBody\n >();\n const userContent =\n `Company Context: ${JSON.stringify(company_context)}\\n\\n` +\n `Occupation: ${occupation_title}\\n\\n` +\n `Task: ${task}\\n\\n` +\n `MANDATE: The refined prompt MUST yield a tangible, industry-standard deliverable typical for this role and domain — e.g., structured reports, specifications, legal documents, marketing assets, or similar professional artifacts. Exclude anything conceptual or partial.`;\n return services.callOpenAI(config.prompts.PROMPT_REFINER, userContent, {\n c,\n isJson: true,\n });\n});\n\n/* ----------------------------------------\n Execute Stream (Task Execution)\n----------------------------------------- */\napp.post(\"/api/execute-stream\", async (c: Context) => {\n const { refined_prompt, user_inputs, company_context, category } = await c.req\n .json();\n\n if (!company_context || !category || !refined_prompt || !user_inputs) {\n return c.json({ error: \"Required fields missing\" }, 400);\n }\n\n let systemPrompt = \"\";\n let userPrompt = \"\";\n\n // Inject user inputs into the refined task briefing\n let finalTaskBriefing = refined_prompt;\n for (const [key, value] of Object.entries(user_inputs)) {\n finalTaskBriefing = finalTaskBriefing.replace(\n new RegExp(`\\\\{\\\\{\\\\s*${key}\\\\s*\\\\}\\\\}`, \"g\"),\n String(value),\n );\n }\n\n // --- TEMPLATE SELECTION LOGIC ---\n let baseTemplate = \"\";\n if (category === \"prompt_building\") {\n baseTemplate = config.seedTemplates.prompt_building;\n // For prompt building, the user prompt IS the task\n userPrompt =\n `Create an expert execution prompt for the task below, fully aligned with standard deliverable formats of this industry.\\n\\n` +\n `Task: \"${finalTaskBriefing}\"\\n\\n` +\n `Company Context: ${JSON.stringify(company_context)}`;\n } else {\n // For all other categories, select template based on company's use case\n baseTemplate = config.seedTemplates[company_context.use_case] ||\n config.seedTemplates.default;\n\n userPrompt = \"Proceed with task execution now.\";\n }\n\n // Construct the final System Prompt\n systemPrompt = `# BASE DIRECTIVE\\n${baseTemplate}\\n\\n` +\n `# COMPANY CONTEXT\\n` +\n `- Company: ${company_context.company_name}\\n` +\n `- Description: ${company_context.company_description}\\n` +\n `- Use Case: ${company_context.use_case}\\n` +\n `- Language for Output: ${company_context.language}\\n` +\n `---\\n` +\n `# TASK BRIEFING\\n${finalTaskBriefing}\\n\\n` +\n `# FINAL INSTRUCTIONS\\n` +\n `1. Deliver a complete, well-structured, **industry-standard artifact** based *only* on the TASK BRIEFING.\\n` +\n `2. Do not include conversation, apologies, or explanations.\\n` +\n `3. Write the entire response in the requested language: **${company_context.language}**.`;\n\n // Use 'prompt_building' logic for its specific user prompt, otherwise use the default.\n // Note: Both branches call the same service, but this structure is kept\n // in case the userPrompt logic diverges more significantly later.\n if (category === \"prompt_building\") {\n return services.callOpenAI(systemPrompt, userPrompt, { c, stream: true });\n } else {\n return services.callOpenAI(systemPrompt, userPrompt, { c, stream: true });\n }\n});\n\n/* ----------------------------------------\n Evaluation\n----------------------------------------- */\napp.post(\"/api/evaluate\", async (c: Context) => {\n const { briefing, first_draft, company_context } = await c.req.json();\n if (!briefing || !first_draft || !company_context) {\n return c.json({\n error: \"Briefing, first_draft, and company_context are required\",\n }, 400);\n }\n try {\n const criteriaPrompt = config.prompts.SUCCESS_CRITERIA_GENERATOR.replace(\n \"{{task_briefing}}\",\n briefing,\n );\n const criteriaCompletion = await services.callOpenAI(\n \"You are a helpful assistant following strict output rules.\",\n criteriaPrompt,\n { model: config.models.minor, isJson: true },\n );\n const success_criteria =\n JSON.parse(criteriaCompletion.choices[0].message.content).criteria;\n const userContent = {\n task_briefing: briefing,\n success_criteria: success_criteria,\n raw_output: first_draft,\n };\n return services.callOpenAI(config.prompts.EVALUATOR_AGENT, userContent, {\n c,\n isJson: true,\n model: config.models.minor,\n });\n } catch (e) {\n console.error(\"Evaluation failed:\", e);\n return c.json(\n { error: `Failed to evaluate the document. ${e.message}` },\n 500,\n );\n }\n});\n\n/* ----------------------------------------\n Refinement\n----------------------------------------- */\napp.post(\"/api/refine\", async (c: Context) => {\n const { briefing, first_draft, critique, language } = await c.req.json();\n const userContent = `# YOUR TASK\nRewrite your first draft to fully address every point in the critique, elevating the work to a 10/10 standard. Return only the improved final deliverable. Do not write an apology, explanation, or any meta-commentary. 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Diff · v4