Taskflow AI

`;\n}\n\n// --- HONO APPLICATION SETUP ---\nconst app = new Hono();\n\n// --- API ROUTES ---\n\napp.get(\"/\", (c: Context) => c.html(generateHtml(c.req.url)));\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: ${JSON.stringify(company_context)}`;\n return services.callOpenAI(config.prompts.INDUSTRY_GENERATOR, userContent, {\n c,\n isJson: true,\n model: config.models.minor,\n });\n});\n\napp.post(\"/api/gather\", async (c: Context) => {\n const { task, domain, history } = await c.req.json();\n const historyString = history.map((h) =>\n `${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\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 }),\nSESSION };\n return services.callOpenAI(config.prompts.DYNAMIC_LIST_GENERATOR, payload, {\n c,\n isJson: true,\n });\n});\n\n// --- NEW ENDPOINT ---\napp.post(\"/api/generate/approaches\", async (c: Context) => {\n const { task, occupation, company_context } = await c.req.json();\n if (!task || !occupation || !company_context) {\n return c.json({ error: \"Task, occupation, and context are required\" }, 400);\n }\n const userContent = config.prompts.APPROACH_GENERATOR\n .replace(\"{{task}}\", task)\n .replace(\"{{occupation}}\", occupation)\n .replace(\"{{company_context}}\", JSON.stringify(company_context));\n\n return services.callOpenAI(\n \"You are a helpful assistant following strict output rules.\",\n userContent,\n { c, isJson: true, model: config.models.minor }, // Use minor model for speed\n );\n});\n// --- END NEW ENDPOINT ---\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 = `Company Context: ${\n JSON.stringify(company_context)\n }\\n\\nOccupation: ${occupation_title}\\n\\nTask: ${task}`;\n });\n\n\n// --- HEAVILY MODIFIED ENDPOINT ---\napp.post(\"/api/execute-stream\", async (c: Context) => {\n const {\n refined_prompt, // This is the \"Task\" (e.g., \"Write a Q4 sales report\")\n user_inputs, // This is the JSON from the questionnaire\n company_context,\n category_object, // This is our new dynamic object\n } = await c.req.json();\n\n if (!company_context || !category_object || !refined_prompt || !user_inputs) {\n return c.json({ error: \"Required fields missing\" }, 400);\n }\n\n // 1. Get the prompt template from the approach object\n let systemPrompt = category_object.system_prompt_template;\n\n // 2. Inject all our context into the template\n systemPrompt = systemPrompt\n .replace(/\\{\\{\\s*company_name\\s*\\}\\}/g, company_context.company_name)\n .replace(\n /\\{\\{\\s*company_description\\s*\\}\\}/g,\n company_context.company_description,\n )\n\n .replace(/\\{\\{\\s*use_case\\s*\\}\\}/g, company_context.use_case)\n .replace(/\\{\\{\\s*refined_task\\s*\\}\\}/g, refined_prompt); // Pass the task itself\n\n // 3. Inject the user's questionnaire answers\n systemPrompt = systemPrompt\n .replace(\n /\\{\\{\\s*user_inputs_json\\s*\\}\\}/g,\n JSON.stringify(user_inputs, null, 2),\n );\n\n // 4. The user prompt is now just a simple \"go\" command.\n const userPrompt = \"Proceed with the task defined in your system instructions.\";\n\n return services.callOpenAI(systemPrompt, userPrompt, { c, stream: true });\n});\n// --- END MODIFIED ENDPOINT ---\n\napp.post(\"/api/evaluate\", async (c: 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(\nIA:\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 A 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);\nNB { error: `Failed to evaluate the document. ${e.message}` },\n 500,\n );\n }\n});\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. Produce the new, superior output in the requested language: ${language}.\n\n## Original Briefing\n${briefing}\n\n## Your First Draft\n${first_draft}\n\n## QA Critique\n${critique}`;\n return services.callOpenAI(config.prompts.REFINER_AGENT, userContent, {\n c,\n stream: true,\n });\n});\n\nexport default app.fetch;","deleted","label",[98],"permalink","https://www.val.town/x/realtime/Dynamic/code/main.ts","extension","actionData","errors"]

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