\n Campy, Your A.I. Campaign Advisor\n

\n\n

\n \n

\n\n\n\n`;\n}\n\n// --- BACKEND LOGIC ---\nexport default async function (req: Request) {\n const url = new URL(req.url);\n const action = url.searchParams.get(\"action\");\n\n if (req.method === \"POST\" && action === \"chat\") {\n const { messages } = await req.json();\n\n // 1. Send the conversation to OpenAI\n const response = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [{ role: \"system\", content: CAMPY_SYSTEM_PROMPT }, ...messages],\n tools: tools,\n tool_choice: \"auto\",\n });\n\n const responseMessage = response.choices[0].message;\n const toolCalls = responseMessage.tool_calls;\n\n let clientResponses = [];\n\n // 2. If there's a simple text response, add it to the queue.\n if (responseMessage.content) {\n clientResponses.push({\n type: \"message\",\n payload: responseMessage.content,\n });\n }\n\n // 3. If the model wants to call tools, execute them.\n if (toolCalls) {\n // We'll send the tool call message back to the history for context\n messages.push(responseMessage);\n\n for (const toolCall of toolCalls) {\n const functionName = toolCall.function.name;\n const functionArgs = JSON.parse(toolCall.function.arguments);\n let functionResponse;\n\n if (functionName === \"render_ui_component\") {\n const { task_description, data } = functionArgs;\n clientResponses.push({\n type: \"message\",\n payload:\n `OK, I'm building a UI component to show you: ${task_description}`,\n });\n const code = await generate_ui_code(task_description, data);\n // This tool's result is sent directly to the client to execute\n clientResponses.push({ type: \"code\", payload: { ...code, data } });\n functionResponse = {\n success: true,\n message:\n \"UI component code was generated and sent to the client for rendering.\",\n };\n } else if (functionName === \"get_current_events\") {\n clientResponses.push({\n type: \"message\",\n payload: `Getting the latest news on \"${functionArgs.topic}\"...`,\n });\n functionResponse = get_current_events(functionArgs.topic);\n } else if (functionName === \"generate_talking_points\") {\n clientResponses.push({\n type: \"message\",\n payload:\n `Drafting some talking points about \"${functionArgs.subject}\"...`,\n });\n functionResponse = await generate_talking_points(\n functionArgs.subject,\n functionArgs.context,\n );\n }\n\n // Add the function's result back to the conversation history for the *next* turn\n messages.push({\n tool_call_id: toolCall.id,\n role: \"tool\" as const,\n name: functionName,\n content: JSON.stringify(functionResponse),\n });\n }\n\n // 4. After executing tools, let the model process the results\n const secondResponse = await openai.chat.completions.create({\n model: \"gpt-4o\",\n messages: [\n { role: \"system\", content: CAMPY_SYSTEM_PROMPT },\n ...messages,\n ],\n });\n\n const finalMessage = secondResponse.choices[0].message.content;\n if (finalMessage) {\n clientResponses.push({ type: \"message\", payload: finalMessage });\n }\n }\n\n return new Response(\n JSON.stringify({\n responses: clientResponses,\n // The final state of the assistant's turn to be added to history\n assistant_response: response.choices[0].message,\n }),\n {\n headers: { \"Content-Type\": \"application/json\" },\n },\n );\n }\n\n // Handle initial GET request to load the page\n return new Response(generateHtml(req.url), {\n headers: { \"Content-Type\": \"text/html\" },\n });\n}","deleted","label",[98],"permalink","https://www.val.town/x/join/campy/code/main.tsx","extension","actionData","errors"]