Merge branch 'other-providers'
commit
6ffb3f45ba
192
index.js
192
index.js
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@ -28,6 +28,11 @@ const defaultConfig = {
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model: "gpt-4o",
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maxTokens: 300
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},
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gemini: {
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apiKey: process.env.GOOGLE_API_KEY,
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model: "gemini-2.0-flash",
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maxTokens: 300
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}
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// Add other vision providers here
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},
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@ -76,6 +81,8 @@ class VisionProviderFactory {
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switch (providerName) {
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case 'openai':
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return new OpenAIVisionProvider(providerConfig);
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case 'gemini':
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return new GeminiVisionProvider(providerConfig);
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// Add other providers here
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default:
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throw new Error(`Vision provider "${providerName}" not implemented.`);
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@ -288,6 +295,179 @@ class OpenAIVisionProvider {
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}
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}
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/**
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* Google Gemini Vision Provider Implementation
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*/
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class GeminiVisionProvider {
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constructor(config) {
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this.config = config;
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// Import the Google Generative AI SDK
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const { GoogleGenerativeAI } = require("@google/generative-ai");
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// Initialize the API
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this.genAI = new GoogleGenerativeAI(config.apiKey);
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this.model = this.genAI.getGenerativeModel({ model: config.model });
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}
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/**
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* Describe a single image
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* @param {string} imagePath - Path to the image file
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* @param {string} prompt - Prompt for the AI
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* @returns {Promise<{description: string, usage: object}>} Description and usage stats
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*/
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async describeImage(imagePath, prompt) {
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try {
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const imageData = fs.readFileSync(imagePath);
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const mimeType = 'image/jpeg'; // Assuming JPEG, could be detected based on file extension
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// Create a file part for the image
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const imagePart = {
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inlineData: {
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data: imageData.toString('base64'),
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mimeType
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}
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};
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// Generate content using Gemini
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const result = await this.model.generateContent([prompt, imagePart]);
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const response = await result.response;
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const text = response.text();
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// Gemini doesn't provide token usage information in the same way as OpenAI
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// We'll estimate based on prompt length and response length
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const inputTokens = Math.ceil(prompt.length / 4) + 1000; // rough estimate for image
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const outputTokens = Math.ceil(text.length / 4);
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return {
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description: text,
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usage: {
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inputTokens,
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outputTokens,
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totalTokens: inputTokens + outputTokens
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}
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};
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} catch (error) {
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console.error("Error describing image with Gemini:", error);
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return {
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description: "Unable to describe this image.",
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usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
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};
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}
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}
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/**
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* Compare two images and describe the differences
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* @param {string} image1Path - Path to the first image
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* @param {string} image2Path - Path to the second image
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* @param {string} prompt - Prompt for the AI
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* @returns {Promise<{description: string, usage: object}>} Description and usage stats
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*/
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async compareImages(image1Path, image2Path, prompt) {
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try {
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const image1Data = fs.readFileSync(image1Path);
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const image2Data = fs.readFileSync(image2Path);
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const mimeType = 'image/jpeg'; // Assuming JPEG, could be detected based on file extension
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// Create file parts for both images
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const image1Part = {
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inlineData: {
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data: image1Data.toString('base64'),
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mimeType
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}
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};
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const image2Part = {
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inlineData: {
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data: image2Data.toString('base64'),
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mimeType
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}
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};
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// Generate content using Gemini with both images
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const result = await this.model.generateContent([prompt, image1Part, image2Part]);
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const response = await result.response;
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const text = response.text();
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// Estimate token usage
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const inputTokens = Math.ceil(prompt.length / 4) + 2000; // rough estimate for two images
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const outputTokens = Math.ceil(text.length / 4);
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return {
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description: text,
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usage: {
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inputTokens,
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outputTokens,
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totalTokens: inputTokens + outputTokens
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}
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};
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} catch (error) {
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console.error("Error comparing images with Gemini:", error);
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return {
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description: "Unable to describe the differences between these images.",
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usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
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};
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}
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}
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/**
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* Describe a batch of images
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* @param {string[]} imagePaths - Array of paths to the images
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* @param {object} lastBatchContext - Context from the previous batch
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* @param {string} prompt - Prompt for the AI
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* @returns {Promise<{description: string, usage: object}>} Description and usage stats
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*/
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async describeBatch(imagePaths, lastBatchContext, prompt) {
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try {
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// Create a prompt that includes context from the last batch if available
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let contextualPrompt = prompt;
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if (lastBatchContext && lastBatchContext.lastDescription) {
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contextualPrompt = `Previous batch summary: ${lastBatchContext.lastDescription}\n\n${prompt}`;
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}
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// Create content parts array starting with the prompt
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const contentParts = [contextualPrompt];
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// Add all images to the content parts
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for (const imagePath of imagePaths) {
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const imageData = fs.readFileSync(imagePath);
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const mimeType = 'image/jpeg'; // Assuming JPEG, could be detected based on file extension
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contentParts.push({
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inlineData: {
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data: imageData.toString('base64'),
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mimeType
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}
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});
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}
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// Generate content using Gemini with all images
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const result = await this.model.generateContent(contentParts);
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const response = await result.response;
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const text = response.text();
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// Estimate token usage
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const inputTokens = Math.ceil(contextualPrompt.length / 4) + (1000 * imagePaths.length); // rough estimate
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const outputTokens = Math.ceil(text.length / 4);
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return {
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description: text,
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usage: {
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inputTokens,
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outputTokens,
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totalTokens: inputTokens + outputTokens
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}
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};
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} catch (error) {
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console.error("Error describing batch of images with Gemini:", error);
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return {
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description: "Unable to describe this batch of images.",
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usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
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};
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}
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}
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}
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/**
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* OpenAI TTS Provider Implementation
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*/
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@ -1081,6 +1261,12 @@ function printStats(stats, settings) {
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output: 0.01 // per 1K output tokens
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}
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// Add other OpenAI models here
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},
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gemini: {
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'gemini-pro-vision': {
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input: 0.0025, // per 1K input tokens
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output: 0.0025 // per 1K output tokens
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}
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}
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// Add other vision providers here
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},
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@ -1169,6 +1355,12 @@ async function estimateCost(videoFilePath, options = {}) {
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output: 0.01 // per 1K output tokens
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}
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// Add other OpenAI models here
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},
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gemini: {
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'gemini-pro-vision': {
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input: 0.0025, // per 1K input tokens
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output: 0.0025 // per 1K output tokens
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}
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}
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// Add other vision providers here
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},
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@ -8,6 +8,7 @@
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"name": "video-audio-description-generator",
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"version": "1.0.0",
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"dependencies": {
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"@google/generative-ai": "^0.24.0",
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"axios": "^1.6.2",
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"dotenv": "^16.3.1",
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"fluent-ffmpeg": "^2.1.2",
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@ -18,6 +19,14 @@
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"node": ">=14.0.0"
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}
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},
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"node_modules/@google/generative-ai": {
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"version": "0.24.0",
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"resolved": "https://registry.npmjs.org/@google/generative-ai/-/generative-ai-0.24.0.tgz",
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"integrity": "sha512-fnEITCGEB7NdX0BhoYZ/cq/7WPZ1QS5IzJJfC3Tg/OwkvBetMiVJciyaan297OvE4B9Jg1xvo0zIazX/9sGu1Q==",
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"engines": {
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"node": ">=18.0.0"
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}
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},
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"node_modules/@types/node": {
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"version": "18.19.79",
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"resolved": "https://registry.npmjs.org/@types/node/-/node-18.19.79.tgz",
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@ -7,6 +7,7 @@
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"start": "node index.js"
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},
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"dependencies": {
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"@google/generative-ai": "^0.24.0",
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"axios": "^1.6.2",
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"dotenv": "^16.3.1",
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"fluent-ffmpeg": "^2.1.2",
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