imagees del agente generadas y subidas con proxy server
This commit is contained in:
Jordan Diaz
@@ -2,19 +2,13 @@ import { z } from "zod";
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import axios from "axios";
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import fs from "fs";
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import path from "path";
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import sharp from "sharp";
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import { withAuth, getSessionCredentials } from "../../auth/index.js";
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import { withAuth } from "../../auth/index.js";
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import { handleToolError } from "../helpers/errorHandler.js";
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import { withAuthParams } from "../helpers/authSchema.js";
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import { pythonPost } from "../helpers/pythonServerClient.js";
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// --- Verificación de créditos y reporte de uso ---
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// --- Verificacion de creditos ---
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const WS_BASE = "https://ws.cocosolution.com/api/handler_acaicode.php";
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// Precios Gemini 2.5 Flash: input $0.15/1M tokens, output $0.60/1M tokens
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function calcCost(usageMetadata) {
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const input = usageMetadata?.promptTokenCount || 0;
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const output = usageMetadata?.candidatesTokenCount || 0;
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return Math.round(((input * 0.15 + output * 0.60) / 1_000_000) * 1e6) / 1e6;
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}
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function getAcaiToken() {
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const projectDir = process.env.ACAI_PROJECT_DIR || "";
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@@ -39,31 +33,6 @@ async function checkCredits() {
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} catch { return false; }
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}
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function reportImageUsage(usageMetadata, model) {
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const token = getAcaiToken();
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if (!token) return;
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const testParam = process.env.STRIPE_MODE === "test" ? "&test" : "";
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const cost = calcCost(usageMetadata);
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const payload = {
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action: "reportUsage",
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model: model || "gemini-2.5-flash-image",
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input_tokens: usageMetadata?.promptTokenCount || 0,
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output_tokens: usageMetadata?.candidatesTokenCount || 0,
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cache_read_tokens: 0,
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cache_creation_tokens: 0,
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cost_usd: cost,
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session_id: "",
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};
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// Fire and forget
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axios.put(`${WS_BASE}?action=reportUsage${testParam}`, payload, {
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headers: { Authorization: `Bearer ${token}`, "Content-Type": "application/json" },
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timeout: 10000,
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}).then(resp => {
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if (resp.data?.success) console.error(`[generate_image] Usage reported: ${model} cost=$${cost}`);
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else console.error(`[generate_image] Usage report failed:`, resp.data);
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}).catch(err => console.error(`[generate_image] Usage report error:`, err.message));
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}
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export function registerGenerateImageTool(server) {
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server.tool(
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"generate_image",
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@@ -78,140 +47,46 @@ export function registerGenerateImageTool(server) {
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{ readOnlyHint: false, destructiveHint: false },
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withAuth(async ({ prompt, width = 1024, height = 1024, style, fileName }, extra) => {
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try {
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const nanoBananaApiKey = process.env.NANO_BANANA_API_KEY;
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if (!nanoBananaApiKey) {
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return {
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content: [{ type: "text", text: "Error: NANO_BANANA_API_KEY not set." }],
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isError: true,
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};
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}
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const projectDir = process.env.ACAI_PROJECT_DIR || "";
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if (!projectDir) {
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return {
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content: [{ type: "text", text: "Error: ACAI_PROJECT_DIR not set." }],
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isError: true,
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};
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}
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// Verificar créditos antes de generar
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// Verificar creditos antes de generar
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const exceeded = await checkCredits();
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if (exceeded) {
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return {
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content: [{ type: "text", text: "Error: No te quedan créditos. Mejora tu plan para seguir usando el asistente." }],
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content: [{ type: "text", text: "Error: No te quedan creditos. Mejora tu plan para seguir usando el asistente." }],
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isError: true,
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};
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}
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// Build prompt with style hint
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const fullPrompt = style ? `${prompt}. Style: ${style}` : prompt;
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const projectSlug = path.basename(process.env.ACAI_PROJECT_DIR || "");
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const safeFileName = fileName || `generated-${Date.now()}`;
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const destRelativePath = `cms/uploads/generated/${safeFileName}.jpg`;
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const fullPrompt = style ? `${style} style: ${prompt}` : prompt;
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// Generate image via Google Gemini
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const geminiModel = process.env.NANO_BANANA_MODEL || "gemini-2.5-flash-image";
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const apiUrl = process.env.NANO_BANANA_URL ||
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`https://generativelanguage.googleapis.com/v1beta/models/${geminiModel}:generateContent`;
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const generateResponse = await axios.post(
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apiUrl,
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{
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contents: [{ parts: [{ text: fullPrompt }] }],
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generationConfig: {
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responseModalities: ["TEXT", "IMAGE"],
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},
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},
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{
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headers: {
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"x-goog-api-key": nanoBananaApiKey,
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"Content-Type": "application/json",
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},
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timeout: 120000,
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validateStatus: (status) => status < 500,
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}
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);
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// Extract image from response
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let imageBuffer = null;
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if (generateResponse.data.candidates?.[0]?.content?.parts) {
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for (const part of generateResponse.data.candidates[0].content.parts) {
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if (part.inlineData?.data) {
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imageBuffer = Buffer.from(part.inlineData.data, "base64");
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break;
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}
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if (part.text?.startsWith("data:image")) {
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const match = part.text.match(/data:image\/[^;]+;base64,(.+)/);
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if (match) {
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imageBuffer = Buffer.from(match[1], "base64");
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break;
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}
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}
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}
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}
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if (!imageBuffer) {
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return {
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content: [{
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type: "text",
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text: `Error: Could not extract image from API response. Status: ${generateResponse.status}. Response: ${JSON.stringify(generateResponse.data).substring(0, 1000)}`
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}],
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isError: true,
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};
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}
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// Compress to JPEG
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const originalSize = imageBuffer.length;
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let result;
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try {
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imageBuffer = await sharp(imageBuffer)
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.jpeg({ quality: 85 })
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.toBuffer();
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console.error(`[generate_image] Compressed: ${Math.round(originalSize / 1024)}KB → ${Math.round(imageBuffer.length / 1024)}KB`);
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} catch (e) {
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console.error(`[generate_image] Compression failed, using original:`, e.message);
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result = await pythonPost("/api/generate-image", {
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project: projectSlug,
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prompt: fullPrompt,
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destRelativePath,
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}, 180000); // 3min timeout para generacion IA
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} catch (pyErr) {
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return handleToolError(new Error(`Python generate-image failed: ${pyErr.response?.data?.error || pyErr.message}`), 'generate_image', { prompt });
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}
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// Save to cms/uploads/generated/
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const uploadsDir = path.join(projectDir, "cms", "uploads", "generated");
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fs.mkdirSync(uploadsDir, { recursive: true });
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const safeName = fileName
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? fileName.replace(/[^\w\-]/g, "_") + ".jpg"
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: `generated-${Date.now()}.jpg`;
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const filePath = path.join(uploadsDir, safeName);
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fs.writeFileSync(filePath, imageBuffer);
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const relativePath = `cms/uploads/generated/${safeName}`;
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const dockerUrl = `http://localhost/${relativePath}`;
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// Reportar uso (fire and forget)
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reportImageUsage(generateResponse.data.usageMetadata, geminiModel);
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const credentials = await getSessionCredentials(extra.sessionId);
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const fullUrl = credentials.web_url ? `${credentials.web_url}/${relativePath}` : dockerUrl;
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// En modo produccion el archivo solo existe en el filesystem local del
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// container agentic, no en el servidor real. Devolvemos el path absoluto
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// como uploadUrl para que upload_record_image lo lea del disco (via base64).
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// En local/forge el container web puede servirlo desde web:80, asi que
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// seguimos devolviendo la URL HTTP.
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const uploadUrl = credentials.mode === "production"
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? filePath // absolute filesystem path
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: (credentials.web_url ? fullUrl : dockerUrl);
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if (!result?.success) {
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return { content: [{ type: "text", text: JSON.stringify({ error: result?.error || "Generation failed" }, null, 2) }], isError: true };
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}
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return {
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content: [{
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type: "text",
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text: JSON.stringify({
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success: true,
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prompt: fullPrompt,
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fileName: safeName,
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filePath,
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relativePath,
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dockerUrl,
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fullUrl,
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uploadUrl,
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size: `${Math.round(imageBuffer.length / 1024)}KB`,
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note: credentials.mode === "production"
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? `Image saved locally. Use imageUrl="${uploadUrl}" with upload_record_image — it is a filesystem path that will be read and uploaded as base64 to production.`
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: `Image saved. To assign it with upload_record_image, use imageUrl="${uploadUrl}". dockerUrl is mainly for local preview/debugging.`,
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message: `Image generated and saved to ${result.relativePath}`,
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uploadUrl: result.fullUrl || result.dockerUrl,
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fullUrl: result.fullUrl || result.dockerUrl,
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relativePath: result.relativePath,
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fileName: result.fileName,
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size: result.size,
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}, null, 2),
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}],
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};
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@@ -5,6 +5,7 @@ import { withAuth, getSessionCredentials } from "../../auth/index.js";
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import { handleToolError, validateRequired, handleApiResponse } from "../helpers/errorHandler.js";
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import { withAuthParams } from "../helpers/authSchema.js";
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import { AcaiHttpClient } from "../helpers/acaiHttpClient.js";
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import { pythonPost } from "../helpers/pythonServerClient.js";
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/**
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* Helper: POST to mcp_respond.php via viewer_functions.php
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@@ -117,36 +118,44 @@ export function registerUploadRecordImageTool(server) {
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);
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if (validationError) return validationError;
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const credentials = await getSessionCredentials(extra.sessionId);
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const projectSlug = path.basename(process.env.ACAI_PROJECT_DIR || "");
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// Si la URL es local, descargar y enviar base64 (el servidor remoto no
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// puede descargarla en modo produccion).
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const localFile = await resolveLocalImageAsBase64(imageUrl);
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const uploadPayload = localFile
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? { tableName, recordId, fieldName, alt, fileBase64: localFile.fileBase64, fileName: localFile.fileName }
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: { tableName, recordId, fieldName, imageUrl, alt };
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// Intentar via Python server (tiene sync + optimizacion)
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let result;
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try {
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result = await pythonPost("/api/cms/upload-to-field", {
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project: projectSlug,
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table: tableName,
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num: recordId,
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field: fieldName,
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imageUrl: imageUrl,
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});
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} catch (pyErr) {
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// Fallback: si Python no es accesible, usar el flujo directo al PHP
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console.error(`[upload_record_image] Python server failed (${pyErr.message}), falling back to PHP direct`);
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const credentials = await getSessionCredentials(extra.sessionId);
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const localFile = await resolveLocalImageAsBase64(imageUrl);
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const uploadPayload = localFile
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? { tableName, recordId, fieldName, alt, fileBase64: localFile.fileBase64, fileName: localFile.fileName }
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: { tableName, recordId, fieldName, imageUrl, alt };
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const response = await mcpPost(credentials, "uploadRecordImage", uploadPayload, credentials.token, credentials.tokenHash);
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const apiError = handleApiResponse(response.data, 'upload_record_image');
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if (apiError) return apiError;
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result = { success: true, data: response.data };
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}
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const response = await mcpPost(
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credentials,
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"uploadRecordImage",
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uploadPayload,
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credentials.token,
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credentials.tokenHash
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);
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const apiError = handleApiResponse(response.data, 'upload_record_image');
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if (apiError) return apiError;
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if (!result?.success && !result?.data?.success) {
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const errMsg = result?.error || result?.data?.error || "Unknown error";
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return { content: [{ type: "text", text: JSON.stringify({ error: errMsg }, null, 2) }], isError: true };
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}
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const uploadData = result.data || result;
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return {
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content: [{
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type: "text",
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text: JSON.stringify({
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success: true,
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message: "Image uploaded successfully",
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tableName,
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recordId,
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fieldName,
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...response.data
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...uploadData,
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}, null, 2)
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}],
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};
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