@daitanjs/senses/dist/index.cjs.map

A library for AI-powered image generation and analysis using OpenAI.

{
  "version": 3,
  "sources": ["../src/index.js", "../src/imagegeneration.js", "../src/vision.js", "../src/capture.js"],
  "sourcesContent": ["// senses/src/index.js\n/**\n * @file Main entry point for the @daitanjs/senses package.\n * @module @daitanjs/senses\n *\n * @description\n * This package provides a suite of utilities for AI-powered sensory tasks,\n * bridging the gap between digital content and machine perception.\n *\n * Key Features:\n * - **Image Generation**: `generateImage` function to create images from text\n *   prompts using OpenAI's DALL-E models.\n * - **Image Analysis**: `analyzeImage` function to interpret and describe images\n *   using OpenAI's vision-capable models (e.g., GPT-4o).\n * - **Media Capture (Browser-only)**:\n *   - `captureAudio`: Captures audio from a user's microphone.\n *   - `captureVideo`: Captures video (and optionally audio) from a user's webcam.\n *\n * It integrates with the DaitanJS ecosystem for configuration, logging, and error handling.\n */\n\nimport { getLogger } from '@daitanjs/development';\n\nconst sensesIndexLogger = getLogger('daitan-senses-index');\n\nsensesIndexLogger.debug('Exporting DaitanJS Senses module functionalities...');\n\n// --- Image Generation (DALL-E) ---\nexport { generateImage } from './imagegeneration.js';\n\n// --- Image Analysis (Vision API - e.g., GPT-4o, GPT-4V) ---\nexport { analyzeImage } from './vision.js';\n\n// --- Media Capture (Browser-only) ---\nexport { captureAudio, captureVideo } from './capture.js';\n\nsensesIndexLogger.info('DaitanJS Senses module exports ready.');", "// senses/src/imagegeneration.js\n/**\n * @file OpenAI DALL-E image generation functionalities.\n * @module @daitanjs/senses/imagegeneration\n */\nimport { getLogger } from '@daitanjs/development';\nimport { getConfigManager } from '@daitanjs/config';\nimport {\n  DaitanConfigurationError,\n  DaitanApiError,\n  DaitanFileOperationError,\n  DaitanInvalidInputError,\n  DaitanError,\n} from '@daitanjs/error';\nimport { query as apiQuery } from '@daitanjs/apiqueries'; // Retained for now if needed, though generateIntelligence is preferred\nimport fs from 'fs/promises';\nimport path from 'path';\nimport { Buffer } from 'buffer';\n\nconst logger = getLogger('daitan-senses-imagegen');\n\nconst OPENAI_IMAGE_GENERATION_API_URL =\n  'https://api.openai.com/v1/images/generations';\n\n/**\n * @typedef {Object} ImageGenerationResult\n * @property {string | string[]} [outputPath]\n * @property {string | string[]} [base64Data]\n * @property {string[]} [urls]\n * @property {string | string[]} [revisedPrompt]\n * @property {number} created\n * @property {Array<object>} rawData\n */\n\n/**\n * @typedef {Object} GenerateImageParams\n * @property {string} prompt\n * @property {string} [outputPath]\n * @property {number} [n=1]\n * @property {'256x256'|'512x512'|'1024x1024'|'1792x1024'|'1024x1792'} [size='1024x1024']\n * @property {'url'|'b64_json'} [response_format='b64_json']\n * @property {string} [model='dall-e-3']\n * @property {'standard'|'hd'} [quality]\n * @property {'vivid'|'natural'} [style]\n * @property {string} [user]\n */\n\n/**\n * Generates an image using OpenAI's DALL-E API.\n * @public\n * @async\n * @param {GenerateImageParams} params\n * @returns {Promise<ImageGenerationResult>}\n */\nexport const generateImage = async ({\n  prompt,\n  outputPath,\n  n = 1,\n  size = '1024x1024',\n  response_format = 'b64_json',\n  model = 'dall-e-3',\n  quality,\n  style,\n  user,\n}) => {\n  const callId = `imageGen-${Date.now().toString(36)}`;\n  logger.info(`[${callId}] generateImage: Initiated.`, {\n    model,\n    n,\n    size,\n    promptPreview: String(prompt).substring(0, 50) + '...',\n  });\n\n  if (!prompt || typeof prompt !== 'string' || !prompt.trim()) {\n    throw new DaitanInvalidInputError('Prompt must be a non-empty string.');\n  }\n  if (model === 'dall-e-3' && n > 1) {\n    logger.warn(`[${callId}] DALL-E 3 only supports n=1. Setting n to 1.`);\n    n = 1;\n  }\n  const dalle2_sizes = ['256x256', '512x512', '1024x1024'];\n  const dalle3_sizes = ['1024x1024', '1792x1024', '1024x1792'];\n  if (model === 'dall-e-2' && !dalle2_sizes.includes(size)) {\n    throw new DaitanInvalidInputError(\n      `DALL-E 2 does not support size ${size}.`\n    );\n  }\n  if (model === 'dall-e-3' && !dalle3_sizes.includes(size)) {\n    throw new DaitanInvalidInputError(\n      `DALL-E 3 does not support size ${size}.`\n    );\n  }\n\n  const configManager = getConfigManager();\n\n  const apiKey = configManager.getApiKeyForProvider('openai');\n  if (!apiKey) {\n    throw new DaitanConfigurationError('OpenAI API key is not configured.');\n  }\n\n  const requestBody = { prompt, n, size, response_format, model };\n  if (quality && model === 'dall-e-3') requestBody.quality = quality;\n  if (style && model === 'dall-e-3') requestBody.style = style;\n  if (user) requestBody.user = user;\n\n  try {\n    // Using apiQuery directly as this is a specific non-LLM OpenAI endpoint\n    const responseData = await apiQuery({\n      url: OPENAI_IMAGE_GENERATION_API_URL,\n      method: 'POST',\n      headers: { Authorization: `Bearer ${apiKey}` },\n      data: requestBody,\n      summary: `OpenAI Image Generation: ${prompt.substring(0, 30)}`,\n    });\n\n    if (!responseData?.data?.[0]) {\n      throw new DaitanApiError(\n        'OpenAI API returned an unexpected successful response structure.',\n        'OpenAI Image Generation'\n      );\n    }\n\n    const results = {\n      revisedPrompt: responseData.data\n        .map((item) => item.revised_prompt)\n        .filter(Boolean),\n      created: responseData.created,\n      rawData: responseData.data,\n    };\n    if (results.revisedPrompt.length === 1)\n      results.revisedPrompt = results.revisedPrompt[0];\n    if (results.revisedPrompt.length === 0) delete results.revisedPrompt;\n\n    if (response_format === 'b64_json') {\n      results.base64Data = responseData.data.map((item) => item.b64_json);\n      if (results.base64Data.length === 1)\n        results.base64Data = results.base64Data[0];\n\n      if (outputPath && typeof window === 'undefined') {\n        const savedPaths = await saveBase64Images(\n          outputPath,\n          results.base64Data\n        );\n        results.outputPath =\n          savedPaths.length === 1 ? savedPaths[0] : savedPaths;\n      }\n    } else {\n      // response_format === 'url'\n      results.urls = responseData.data.map((item) => item.url);\n    }\n\n    return results;\n  } catch (error) {\n    logger.error(\n      `[${callId}] Error during OpenAI image generation: ${error.message}`\n    );\n    if (error instanceof DaitanError) throw error;\n    throw new DaitanApiError(\n      `OpenAI image generation failed: ${error.message}`,\n      'OpenAI Image Generation',\n      error.response?.status,\n      { responseData: error.response?.data },\n      error\n    );\n  }\n};\n\n/** @private */\nasync function saveBase64Images(outputPath, base64Data) {\n  const imagesToSave = Array.isArray(base64Data) ? base64Data : [base64Data];\n  const savedPaths = [];\n  for (let i = 0; i < imagesToSave.length; i++) {\n    const imageBuffer = Buffer.from(imagesToSave[i], 'base64');\n    let currentOutputPath = outputPath;\n    if (imagesToSave.length > 1) {\n      const ext = path.extname(outputPath) || '.png';\n      const base = path.basename(outputPath, ext);\n      const dir = path.dirname(outputPath);\n      currentOutputPath = path.join(dir, `${base}_${i}${ext}`);\n    }\n    try {\n      await fs.mkdir(path.dirname(currentOutputPath), { recursive: true });\n      await fs.writeFile(currentOutputPath, imageBuffer);\n      savedPaths.push(currentOutputPath);\n    } catch (fileError) {\n      throw new DaitanFileOperationError(\n        `Failed to save image to ${currentOutputPath}: ${fileError.message}`,\n        { path: currentOutputPath },\n        fileError\n      );\n    }\n  }\n  return savedPaths;\n}\n", "// senses/src/vision.js\n/**\n * @file OpenAI Vision API (e.g., GPT-4o, GPT-4 Vision) image analysis functionalities.\n * @module @daitanjs/senses/vision\n */\nimport fs from 'fs/promises';\nimport { getLogger } from '@daitanjs/development';\nimport { getConfigManager } from '@daitanjs/config';\nimport {\n  DaitanConfigurationError,\n  DaitanApiError,\n  DaitanFileOperationError,\n  DaitanInvalidInputError,\n  DaitanError,\n} from '@daitanjs/error';\nimport { query as apiQuery } from '@daitanjs/apiqueries'; // FIXED: Using apiqueries instead of intelligence\nimport { isValidURL } from '@daitanjs/validation';\nimport { Buffer } from 'buffer';\n\nconst logger = getLogger('daitan-senses-vision');\n\nconst OPENAI_VISION_API_URL = 'https://api.openai.com/v1/chat/completions';\nconst DEFAULT_OPENAI_VISION_MODEL = 'gpt-4o-mini';\n\n/** @private */\nconst encodeImageLocalFileToBase64WithMime = async (imagePath) => {\n  if (typeof window !== 'undefined') {\n    throw new DaitanConfigurationError(\n      'encodeImageLocalFileToBase64 is for Node.js only.'\n    );\n  }\n  try {\n    const buffer = await fs.readFile(imagePath);\n    const mimeTypes = await import('mime-types');\n    const contentType =\n      mimeTypes.lookup(imagePath) || 'application/octet-stream';\n    return { base64: buffer.toString('base64'), contentType };\n  } catch (error) {\n    throw new DaitanFileOperationError(\n      `Error reading image file \"${imagePath}\": ${error.message}`,\n      { path: imagePath },\n      error\n    );\n  }\n};\n\n/**\n * @typedef {Object} ImageAnalysisResult\n * @property {string} analysis\n * @property {object | null} usage\n * @property {string} modelUsed\n * @property {object} [rawResponse]\n */\n\n/**\n * @typedef {Object} AnalyzeImageParams\n * @property {string} imageSource\n * @property {string} [prompt=\"Describe this image in detail.\"]\n * @property {string} [model]\n * @property {number} [max_tokens=500]\n * @property {'low'|'high'|'auto'} [detailLevel='auto']\n * @property {object} [llmConfigOptions]\n */\n\n/**\n * Analyzes an image using an OpenAI vision-capable model.\n * @public\n * @async\n * @param {AnalyzeImageParams} params\n * @returns {Promise<ImageAnalysisResult>}\n */\nexport const analyzeImage = async ({\n  imageSource,\n  prompt = 'Describe this image in detail.',\n  model,\n  max_tokens = 500,\n  detailLevel = 'auto',\n  llmConfigOptions = {}, // Kept for API consistency but not used in this direct call\n}) => {\n  const callId = `analyzeImage-${Date.now().toString(36)}`;\n  logger.info(`[${callId}] analyzeImage: Initiated.`);\n\n  if (!imageSource || typeof imageSource !== 'string' || !imageSource.trim()) {\n    throw new DaitanInvalidInputError(\n      'Image source (URL, local path, or data URL) must be a non-empty string.'\n    );\n  }\n\n  const configManager = getConfigManager();\n  const apiKey = configManager.get('OPENAI_API_KEY');\n  if (!apiKey) {\n    throw new DaitanConfigurationError('OpenAI API key is not configured.');\n  }\n\n  const effectiveModel =\n    model ||\n    configManager.get('OPENAI_VISION_MODEL') ||\n    DEFAULT_OPENAI_VISION_MODEL;\n\n  let imageUrlObject;\n  if (imageSource.startsWith('data:image/')) {\n    imageUrlObject = { url: imageSource, detail: detailLevel };\n  } else if (isValidURL(imageSource)) {\n    imageUrlObject = { url: imageSource, detail: detailLevel };\n  } else {\n    const { base64, contentType } = await encodeImageLocalFileToBase64WithMime(\n      imageSource\n    );\n    imageUrlObject = { url: `data:${contentType};base64,${base64}`, detail: detailLevel };\n  }\n\n  const requestBody = {\n    model: effectiveModel,\n    messages: [\n      {\n        role: 'user',\n        content: [\n          { type: 'text', text: prompt },\n          { type: 'image_url', image_url: imageUrlObject },\n        ],\n      },\n    ],\n    max_tokens: max_tokens,\n  };\n\n  logger.debug(\n    `[${callId}] Sending vision analysis request to OpenAI. Model: ${effectiveModel}`\n  );\n\n  try {\n    const responseData = await apiQuery({\n        url: OPENAI_VISION_API_URL,\n        method: 'POST',\n        headers: { Authorization: `Bearer ${apiKey}` },\n        data: requestBody,\n        summary: `OpenAI Vision Analysis: \"${prompt.substring(0, 30)}...\"`,\n      });\n\n    const analysisText = responseData?.choices?.[0]?.message?.content;\n    if (typeof analysisText !== 'string') {\n        throw new DaitanApiError('Invalid response structure from OpenAI Vision API.', 'OpenAI Vision', 200, { responseData });\n    }\n    \n    return {\n      analysis: analysisText,\n      usage: responseData.usage || null,\n      modelUsed: responseData.model || effectiveModel,\n      rawResponse: responseData,\n    };\n  } catch (error) {\n    if (error instanceof DaitanError) throw error;\n    throw new DaitanApiError(\n      `OpenAI Vision API request failed: ${error.message}`,\n      'OpenAI Vision',\n      error.httpStatusCode || 500,\n      { modelUsed: effectiveModel },\n      error\n    );\n  }\n};", "// senses/src/capture.js\n/**\n * @file Browser-based media capture (microphone audio, webcam video).\n * @module @daitanjs/senses/capture\n *\n * @description\n * This module provides high-level functions for capturing audio from the user's microphone\n * and video from their webcam using the browser's `MediaDevices` API. This functionality is\n * strictly for front-end browser environments.\n */\n\nimport { getLogger } from '@daitanjs/development';\nimport {\n  DaitanBrowserSpecificError,\n  DaitanOperationError,\n} from '@daitanjs/error';\n\nconst captureLogger = getLogger('daitan-senses-capture');\n\n/**\n * Checks if the required browser APIs (`navigator.mediaDevices`) are available.\n * @private\n * @throws {DaitanBrowserSpecificError} If not in a browser environment or if APIs are missing.\n */\nfunction ensureBrowserMediaAPIs() {\n  if (\n    typeof window === 'undefined' ||\n    !navigator.mediaDevices ||\n    !navigator.mediaDevices.getUserMedia\n  ) {\n    throw new DaitanBrowserSpecificError(\n      'Media capture is only available in a browser environment with support for the MediaDevices API.'\n    );\n  }\n}\n\n/**\n * @typedef {Object} MediaRecorderControls\n * @property {() => void} start - Starts the recording.\n * @property {() => void} stop - Stops the recording and resolves the promise with the result.\n * @property {() => void} pause - Pauses the recording.\n * @property {() => void} resume - Resumes a paused recording.\n * @property {() => 'recording' | 'paused' | 'inactive'} state - Gets the current state of the recorder.\n */\n\n/**\n * @typedef {Object} AudioRecordingResult\n * @property {Blob} blob - The recorded audio data as a Blob.\n * @property {string} objectURL - A temporary URL for the Blob, useful for playback.\n * @property {string} mimeType - The MIME type of the recorded audio Blob.\n */\n\n/**\n * Captures audio from the user's microphone. This function returns a Promise that\n * resolves when the recording is stopped, along with controls to manage the recording.\n *\n * @public\n * @async\n * @param {object} [options={}] - Options for audio capture.\n * @param {string} [options.mimeType='audio/webm;codecs=opus'] - The desired MIME type for the recording. Browser support may vary. 'audio/mpeg' or 'audio/wav' are other possibilities.\n * @returns {Promise<{ recording: Promise<AudioRecordingResult>, controls: MediaRecorderControls }>} An object containing a `recording` promise and `controls` to start/stop the capture.\n * @throws {DaitanBrowserSpecificError} If not run in a supported browser environment.\n * @throws {DaitanOperationError} If permission to use the microphone is denied or another error occurs.\n *\n * @example\n * async function startRecording() {\n *   try {\n *     const { recording, controls } = await captureAudio();\n *     console.log(\"Ready to record. Call controls.start()\");\n *     // In a UI button click:\n *     // recordButton.onclick = controls.start;\n *     // stopButton.onclick = controls.stop;\n *\n *     const { blob, objectURL } = await recording;\n *     console.log(\"Recording finished!\", blob.size, objectURL);\n *     // Now you can play the audio or upload the blob\n *     const audioPlayer = new Audio(objectURL);\n *     audioPlayer.play();\n *   } catch (error) {\n *     console.error(\"Failed to start audio capture:\", error.message);\n *   }\n * }\n */\nexport const captureAudio = async (options = {}) => {\n  ensureBrowserMediaAPIs();\n  const { mimeType = 'audio/webm;codecs=opus' } = options;\n\n  try {\n    const stream = await navigator.mediaDevices.getUserMedia({\n      audio: true,\n      video: false,\n    });\n    const mediaRecorder = new MediaRecorder(stream, { mimeType });\n    const audioChunks = [];\n\n    mediaRecorder.addEventListener('dataavailable', (event) => {\n      audioChunks.push(event.data);\n    });\n\n    const recordingPromise = new Promise((resolve, reject) => {\n      mediaRecorder.addEventListener('stop', () => {\n        const audioBlob = new Blob(audioChunks, {\n          type: mediaRecorder.mimeType,\n        });\n        const objectURL = URL.createObjectURL(audioBlob);\n        stream.getTracks().forEach((track) => track.stop()); // Stop the mic stream\n        resolve({\n          blob: audioBlob,\n          objectURL,\n          mimeType: mediaRecorder.mimeType,\n        });\n      });\n      mediaRecorder.addEventListener('error', (event) => {\n        stream.getTracks().forEach((track) => track.stop());\n        reject(\n          new DaitanOperationError(\n            `MediaRecorder error: ${event.error.message}`\n          )\n        );\n      });\n    });\n\n    const controls = {\n      start: () => mediaRecorder.start(),\n      stop: () => mediaRecorder.stop(),\n      pause: () => mediaRecorder.pause(),\n      resume: () => mediaRecorder.resume(),\n      state: () => mediaRecorder.state,\n    };\n\n    return { recording: recordingPromise, controls };\n  } catch (error) {\n    let errorMessage = `Failed to get microphone access: ${error.message}`;\n    if (\n      error.name === 'NotAllowedError' ||\n      error.name === 'PermissionDeniedError'\n    ) {\n      errorMessage =\n        'Permission to use the microphone was denied. Please allow microphone access in your browser settings.';\n    } else if (\n      error.name === 'NotFoundError' ||\n      error.name === 'DevicesNotFoundError'\n    ) {\n      errorMessage = 'No microphone was found on this device.';\n    }\n    captureLogger.error('Error initializing audio capture.', {\n      errorName: error.name,\n      message: errorMessage,\n    });\n    throw new DaitanOperationError(errorMessage, { originalError: error });\n  }\n};\n\n/**\n * @typedef {Object} VideoRecordingResult\n * @property {Blob} blob - The recorded video data as a Blob.\n * @property {string} objectURL - A temporary URL for the Blob, useful for playback.\n * @property {string} mimeType - The MIME type of the recorded video Blob.\n */\n\n/**\n * Captures video (and optionally audio) from the user's webcam and microphone.\n * Returns a Promise that resolves when the recording is stopped, along with controls.\n *\n * @public\n * @async\n * @param {object} [options={}] - Options for video capture.\n * @param {string} [options.mimeType='video/webm;codecs=vp8,opus'] - The desired MIME type.\n * @param {boolean} [options.includeAudio=true] - Whether to include audio in the recording.\n * @param {object} [options.constraints] - Custom `MediaStreamConstraints` to pass to `getUserMedia`.\n * @returns {Promise<{ recording: Promise<VideoRecordingResult>, controls: MediaRecorderControls }>}\n * @throws {DaitanBrowserSpecificError} If not run in a supported browser environment.\n * @throws {DaitanOperationError} If permission is denied or another error occurs.\n */\nexport const captureVideo = async (options = {}) => {\n  ensureBrowserMediaAPIs();\n  const {\n    mimeType = 'video/webm;codecs=vp8,opus',\n    includeAudio = true,\n    constraints = { video: true, audio: includeAudio },\n  } = options;\n\n  try {\n    const stream = await navigator.mediaDevices.getUserMedia(constraints);\n    const mediaRecorder = new MediaRecorder(stream, { mimeType });\n    const videoChunks = [];\n\n    mediaRecorder.addEventListener('dataavailable', (event) => {\n      videoChunks.push(event.data);\n    });\n\n    const recordingPromise = new Promise((resolve, reject) => {\n      mediaRecorder.addEventListener('stop', () => {\n        const videoBlob = new Blob(videoChunks, {\n          type: mediaRecorder.mimeType,\n        });\n        const objectURL = URL.createObjectURL(videoBlob);\n        stream.getTracks().forEach((track) => track.stop()); // Stop camera and mic\n        resolve({\n          blob: videoBlob,\n          objectURL,\n          mimeType: mediaRecorder.mimeType,\n        });\n      });\n      mediaRecorder.addEventListener('error', (event) => {\n        stream.getTracks().forEach((track) => track.stop());\n        reject(\n          new DaitanOperationError(\n            `MediaRecorder error: ${event.error.message}`\n          )\n        );\n      });\n    });\n\n    const controls = {\n      start: () => mediaRecorder.start(),\n      stop: () => mediaRecorder.stop(),\n      pause: () => mediaRecorder.pause(),\n      resume: () => mediaRecorder.resume(),\n      state: () => mediaRecorder.state,\n    };\n\n    return { recording: recordingPromise, controls };\n  } catch (error) {\n    let errorMessage = `Failed to get camera/microphone access: ${error.message}`;\n    if (\n      error.name === 'NotAllowedError' ||\n      error.name === 'PermissionDeniedError'\n    ) {\n      errorMessage =\n        'Permission to use the camera and/or microphone was denied.';\n    } else if (\n      error.name === 'NotFoundError' ||\n      error.name === 'DevicesNotFoundError'\n    ) {\n      errorMessage = 'No camera and/or microphone was found on this device.';\n    }\n    captureLogger.error('Error initializing video capture.', {\n      errorName: error.name,\n      message: errorMessage,\n    });\n    throw new DaitanOperationError(errorMessage, { originalError: error });\n  }\n};\n"],
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}