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Rewrite frontend as single self-contained HTML file — all CSS/JS inline, no external files to fail loading

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Talon
2026-05-13 17:24:10 +02:00
parent 3432d362e2
commit ddb0f88257
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33
dist/providers/vision/geminiVisionProvider.d.ts vendored Normal file
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import { VisionProvider, VisionProviderConfig, VisionResult, BatchContext } from '../../interfaces';
/**
* Google Gemini Vision Provider Implementation
*/
export declare class GeminiVisionProvider implements VisionProvider {
private config;
private genAI;
private model;
constructor(config: VisionProviderConfig);
/**
* Describe a single image
* @param imagePath - Path to the image file
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
describeImage(imagePath: string, prompt: string): Promise<VisionResult>;
/**
* Compare two images and describe the differences
* @param image1Path - Path to the first image
* @param image2Path - Path to the second image
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
compareImages(image1Path: string, image2Path: string, prompt: string): Promise<VisionResult>;
/**
* Describe a batch of images
* @param imagePaths - Array of paths to the images
* @param lastBatchContext - Context from the previous batch
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
describeBatch(imagePaths: string[], lastBatchContext: BatchContext, prompt: string): Promise<VisionResult>;
}

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dist/providers/vision/geminiVisionProvider.js vendored Normal file
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"use strict";
var __importDefault = (this && this.__importDefault) || function (mod) {
return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.GeminiVisionProvider = void 0;
const fs_1 = __importDefault(require("fs"));
const generative_ai_1 = require("@google/generative-ai");
/**
* Google Gemini Vision Provider Implementation
*/
class GeminiVisionProvider {
constructor(config) {
this.config = config;
this.genAI = new generative_ai_1.GoogleGenerativeAI(config.apiKey);
this.model = this.genAI.getGenerativeModel({ model: config.model });
}
/**
* Describe a single image
* @param imagePath - Path to the image file
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
async describeImage(imagePath, prompt) {
try {
const imageData = fs_1.default.readFileSync(imagePath);
const mimeType = 'image/jpeg'; // Assuming JPEG, could be detected based on file extension
// Create a file part for the image
const imagePart = {
inlineData: {
data: imageData.toString('base64'),
mimeType
}
};
// Generate content using Gemini
const result = await this.model.generateContent([prompt, imagePart]);
const response = await result.response;
const text = response.text();
// Gemini doesn't provide token usage information in the same way as OpenAI
// We'll estimate based on prompt length and response length
const inputTokens = Math.ceil(prompt.length / 4) + 1000; // rough estimate for image
const outputTokens = Math.ceil(text.length / 4);
return {
description: text,
usage: {
inputTokens,
outputTokens,
totalTokens: inputTokens + outputTokens
}
};
}
catch (error) {
console.error("Error describing image with Gemini:", error);
return {
description: "Unable to describe this image.",
usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
};
}
}
/**
* Compare two images and describe the differences
* @param image1Path - Path to the first image
* @param image2Path - Path to the second image
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
async compareImages(image1Path, image2Path, prompt) {
try {
const image1Data = fs_1.default.readFileSync(image1Path);
const image2Data = fs_1.default.readFileSync(image2Path);
const mimeType = 'image/jpeg'; // Assuming JPEG, could be detected based on file extension
// Create file parts for both images
const image1Part = {
inlineData: {
data: image1Data.toString('base64'),
mimeType
}
};
const image2Part = {
inlineData: {
data: image2Data.toString('base64'),
mimeType
}
};
// Generate content using Gemini with both images
const result = await this.model.generateContent([prompt, image1Part, image2Part]);
const response = await result.response;
const text = response.text();
// Estimate token usage
const inputTokens = Math.ceil(prompt.length / 4) + 2000; // rough estimate for two images
const outputTokens = Math.ceil(text.length / 4);
return {
description: text,
usage: {
inputTokens,
outputTokens,
totalTokens: inputTokens + outputTokens
}
};
}
catch (error) {
console.error("Error comparing images with Gemini:", error);
return {
description: "Unable to describe the differences between these images.",
usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
};
}
}
/**
* Describe a batch of images
* @param imagePaths - Array of paths to the images
* @param lastBatchContext - Context from the previous batch
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
async describeBatch(imagePaths, lastBatchContext, prompt) {
try {
// Create a prompt that includes context from the last batch if available
let contextualPrompt = prompt;
if (lastBatchContext && lastBatchContext.lastDescription) {
contextualPrompt = `Previous batch summary: ${lastBatchContext.lastDescription}\n\n${prompt}`;
}
// Create content parts array starting with the prompt
const contentParts = [contextualPrompt];
// Add all images to the content parts
for (const imagePath of imagePaths) {
const imageData = fs_1.default.readFileSync(imagePath);
const mimeType = 'image/jpeg'; // Assuming JPEG, could be detected based on file extension
contentParts.push({
inlineData: {
data: imageData.toString('base64'),
mimeType
}
});
}
// Generate content using Gemini with all images
const result = await this.model.generateContent(contentParts);
const response = await result.response;
const text = response.text();
// Estimate token usage
const inputTokens = Math.ceil(contextualPrompt.length / 4) + (1000 * imagePaths.length); // rough estimate
const outputTokens = Math.ceil(text.length / 4);
return {
description: text,
usage: {
inputTokens,
outputTokens,
totalTokens: inputTokens + outputTokens
}
};
}
catch (error) {
console.error("Error describing batch of images with Gemini:", error);
return {
description: "Unable to describe this batch of images.",
usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
};
}
}
}
exports.GeminiVisionProvider = GeminiVisionProvider;
//# sourceMappingURL=geminiVisionProvider.js.map

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export * from './visionProviderFactory';
export * from './openAIVisionProvider';
export * from './geminiVisionProvider';
export * from './ollamaVisionProvider';
export * from './openRouterVisionProvider';

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"use strict";
var __createBinding = (this && this.__createBinding) || (Object.create ? (function(o, m, k, k2) {
if (k2 === undefined) k2 = k;
var desc = Object.getOwnPropertyDescriptor(m, k);
if (!desc || ("get" in desc ? !m.__esModule : desc.writable || desc.configurable)) {
desc = { enumerable: true, get: function() { return m[k]; } };
}
Object.defineProperty(o, k2, desc);
}) : (function(o, m, k, k2) {
if (k2 === undefined) k2 = k;
o[k2] = m[k];
}));
var __exportStar = (this && this.__exportStar) || function(m, exports) {
for (var p in m) if (p !== "default" && !Object.prototype.hasOwnProperty.call(exports, p)) __createBinding(exports, m, p);
};
Object.defineProperty(exports, "__esModule", { value: true });
__exportStar(require("./visionProviderFactory"), exports);
__exportStar(require("./openAIVisionProvider"), exports);
__exportStar(require("./geminiVisionProvider"), exports);
__exportStar(require("./ollamaVisionProvider"), exports);
__exportStar(require("./openRouterVisionProvider"), exports);
//# sourceMappingURL=index.js.map

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import { VisionProvider, VisionProviderConfig, VisionResult, BatchContext } from '../../interfaces';
/**
* Ollama Vision Provider Implementation
* See: https://github.com/ollama/ollama/blob/main/docs/api.md
*/
export declare class OllamaVisionProvider implements VisionProvider {
private config;
private axiosInstance;
constructor(config: VisionProviderConfig);
/**
* Describe a single image
* @param imagePath - Path to the image file
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
describeImage(imagePath: string, prompt: string): Promise<VisionResult>;
/**
* Compare two images and describe differences
* @param image1Path - Path to the first image
* @param image2Path - Path to the second image
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
compareImages(image1Path: string, image2Path: string, prompt: string): Promise<VisionResult>;
/**
* Describe a batch of images
* @param imagePaths - Array of paths to the images
* @param lastBatchContext - Context from the previous batch (optional)
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
describeBatch(imagePaths: string[], lastBatchContext: BatchContext, prompt: string): Promise<VisionResult>;
}

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"use strict";
var __importDefault = (this && this.__importDefault) || function (mod) {
return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.OllamaVisionProvider = void 0;
const fs_1 = __importDefault(require("fs"));
const axios_1 = __importDefault(require("axios"));
/**
* Ollama Vision Provider Implementation
* See: https://github.com/ollama/ollama/blob/main/docs/api.md
*/
class OllamaVisionProvider {
constructor(config) {
this.config = config;
this.axiosInstance = axios_1.default.create({
baseURL: config.baseUrl || "http://localhost:11434",
headers: { "Content-Type": "application/json" }
});
}
/**
* Describe a single image
* @param imagePath - Path to the image file
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
async describeImage(imagePath, prompt) {
try {
const imageData = fs_1.default.readFileSync(imagePath);
const base64Image = imageData.toString('base64');
const response = await this.axiosInstance.post('/api/generate', {
model: this.config.model,
prompt: prompt,
images: [base64Image],
stream: false,
options: {
max_tokens: this.config.maxTokens || 300,
temperature: 0.1
}
});
const combinedText = response.data.response || "";
return {
description: combinedText.trim(),
usage: {
inputTokens: 0,
outputTokens: 0,
totalTokens: 0
}
};
}
catch (error) {
console.error("Ollama describeImage error:", error);
return {
description: "Unable to describe this image.",
usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
};
}
}
/**
* Compare two images and describe differences
* @param image1Path - Path to the first image
* @param image2Path - Path to the second image
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
async compareImages(image1Path, image2Path, prompt) {
try {
const image1Data = fs_1.default.readFileSync(image1Path).toString('base64');
const image2Data = fs_1.default.readFileSync(image2Path).toString('base64');
const response = await this.axiosInstance.post('/api/generate', {
model: this.config.model,
prompt: prompt,
images: [image1Data, image2Data],
stream: false,
options: {
max_tokens: this.config.maxTokens || 300,
temperature: 0.2
}
});
const combinedText = response.data.response || "";
return {
description: combinedText.trim(),
usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
};
}
catch (error) {
console.error("Ollama compareImages error:", error);
return {
description: "Unable to describe the differences.",
usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
};
}
}
/**
* Describe a batch of images
* @param imagePaths - Array of paths to the images
* @param lastBatchContext - Context from the previous batch (optional)
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
async describeBatch(imagePaths, lastBatchContext, prompt) {
try {
let userPrompt = prompt;
// If there's context, prepend it. This helps maintain a storyline across batches.
if (lastBatchContext && lastBatchContext.lastDescription) {
userPrompt = `Previous batch summary: ${lastBatchContext.lastDescription}\n\n${prompt}`;
}
// Convert images to base64
const imagesBase64 = imagePaths.map(fp => {
const imageData = fs_1.default.readFileSync(fp);
return imageData.toString('base64');
});
const response = await this.axiosInstance.post('/api/generate', {
model: this.config.model,
prompt: userPrompt,
images: imagesBase64,
stream: false,
options: {
max_tokens: this.config.maxTokens || 300,
temperature: 0.2
}
}, {
timeout: 120000 // Timeout in milliseconds, e.g., 5000 ms = 5 seconds
});
const combinedText = response.data.response || "";
return {
description: combinedText.trim(),
usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
};
}
catch (error) {
console.error("Ollama describeBatch error:", error);
return {
description: "Unable to describe this batch of images.",
usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
};
}
}
}
exports.OllamaVisionProvider = OllamaVisionProvider;
//# sourceMappingURL=ollamaVisionProvider.js.map

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32
dist/providers/vision/openAIVisionProvider.d.ts vendored Normal file
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@@ -0,0 +1,32 @@
import { VisionProvider, VisionProviderConfig, VisionResult, BatchContext } from '../../interfaces';
/**
* OpenAI Vision Provider Implementation
*/
export declare class OpenAIVisionProvider implements VisionProvider {
private config;
private openai;
constructor(config: VisionProviderConfig);
/**
* Describe a single image
* @param imagePath - Path to the image file
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
describeImage(imagePath: string, prompt: string): Promise<VisionResult>;
/**
* Compare two images and describe the differences
* @param image1Path - Path to the first image
* @param image2Path - Path to the second image
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
compareImages(image1Path: string, image2Path: string, prompt: string): Promise<VisionResult>;
/**
* Describe a batch of images
* @param imagePaths - Array of paths to the images
* @param lastBatchContext - Context from the previous batch
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
describeBatch(imagePaths: string[], lastBatchContext: BatchContext, prompt: string): Promise<VisionResult>;
}

182
dist/providers/vision/openAIVisionProvider.js vendored Normal file
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@@ -0,0 +1,182 @@
"use strict";
var __importDefault = (this && this.__importDefault) || function (mod) {
return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.OpenAIVisionProvider = void 0;
const fs_1 = __importDefault(require("fs"));
const openai_1 = require("openai");
/**
* OpenAI Vision Provider Implementation
*/
class OpenAIVisionProvider {
constructor(config) {
this.config = config;
this.openai = new openai_1.OpenAI({
apiKey: config.apiKey,
});
}
/**
* Describe a single image
* @param imagePath - Path to the image file
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
async describeImage(imagePath, prompt) {
try {
const imageData = fs_1.default.readFileSync(imagePath);
const base64Image = imageData.toString('base64');
const response = await this.openai.chat.completions.create({
model: this.config.model,
temperature: 0.1,
messages: [
{
role: "user",
content: [
{ type: "text", text: prompt },
{
type: "image_url",
image_url: {
url: `data:image/jpeg;base64,${base64Image}`
}
}
]
}
],
max_completion_tokens: this.config.maxTokens || 300
});
return {
description: response.choices[0].message.content?.trim() || "No description generated.",
usage: {
inputTokens: response.usage?.prompt_tokens || 0,
outputTokens: response.usage?.completion_tokens || 0,
totalTokens: response.usage?.total_tokens || 0
}
};
}
catch (error) {
console.error("Error describing image:", error);
return {
description: "Unable to describe this image.",
usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
};
}
}
/**
* Compare two images and describe the differences
* @param image1Path - Path to the first image
* @param image2Path - Path to the second image
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
async compareImages(image1Path, image2Path, prompt) {
try {
const image1Data = fs_1.default.readFileSync(image1Path);
const image2Data = fs_1.default.readFileSync(image2Path);
const base64Image1 = image1Data.toString('base64');
const base64Image2 = image2Data.toString('base64');
const response = await this.openai.chat.completions.create({
model: this.config.model,
messages: [
{
role: "user",
content: [
{ type: "text", text: prompt },
{
type: "image_url",
image_url: {
url: `data:image/jpeg;base64,${base64Image1}`
}
},
{
type: "image_url",
image_url: {
url: `data:image/jpeg;base64,${base64Image2}`
}
}
]
}
],
max_completion_tokens: this.config.maxTokens || 300
});
return {
description: response.choices[0].message.content?.trim() || "No description generated.",
usage: {
inputTokens: response.usage?.prompt_tokens || 0,
outputTokens: response.usage?.completion_tokens || 0,
totalTokens: response.usage?.total_tokens || 0
}
};
}
catch (error) {
console.error("Error comparing images:", error);
return {
description: "Unable to describe the differences between these images.",
usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
};
}
}
/**
* Describe a batch of images
* @param imagePaths - Array of paths to the images
* @param lastBatchContext - Context from the previous batch
* @param prompt - Prompt for the AI
* @returns Description and usage stats
*/
async describeBatch(imagePaths, lastBatchContext, prompt) {
try {
// Convert images to base64
const imagesBase64 = imagePaths.map(fp => {
const imageData = fs_1.default.readFileSync(fp);
return imageData.toString('base64');
});
// Build the messages array for the chat completion
const messages = [
{
role: "user",
content: [
{ type: "text", text: prompt }
]
}
];
// If we have some text context from the last batch, inject that as well
if (lastBatchContext && lastBatchContext.lastDescription) {
messages.unshift({
role: "system",
content: `Previous batch summary: ${lastBatchContext.lastDescription}`
});
}
// Append each image in the new batch
imagesBase64.forEach(base64 => {
messages[messages.length - 1].content.push({
type: "image_url",
image_url: {
url: `data:image/jpeg;base64,${base64}`
}
});
});
const response = await this.openai.chat.completions.create({
model: this.config.model,
messages,
max_completion_tokens: this.config.maxTokens || 300
});
return {
description: response.choices[0].message.content?.trim() || "No description generated.",
usage: {
inputTokens: response.usage?.prompt_tokens || 0,
outputTokens: response.usage?.completion_tokens || 0,
totalTokens: response.usage?.total_tokens || 0
}
};
}
catch (error) {
console.error("Error describing batch of images:", error);
return {
description: "Unable to describe this batch of images.",
usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
};
}
}
}
exports.OpenAIVisionProvider = OpenAIVisionProvider;
//# sourceMappingURL=openAIVisionProvider.js.map

1
dist/providers/vision/openAIVisionProvider.js.map vendored Normal file
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@@ -0,0 +1 @@
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9
dist/providers/vision/openRouterVisionProvider.d.ts vendored Normal file
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@@ -0,0 +1,9 @@
import { VisionProvider, VisionProviderConfig, VisionResult, BatchContext } from '../../interfaces';
export declare class OpenRouterVisionProvider implements VisionProvider {
private config;
private axiosInstance;
constructor(config: VisionProviderConfig);
describeImage(imagePath: string, prompt: string): Promise<VisionResult>;
compareImages(image1Path: string, image2Path: string, prompt: string): Promise<VisionResult>;
describeBatch(imagePaths: string[], lastBatchContext: BatchContext, prompt: string): Promise<VisionResult>;
}

161
dist/providers/vision/openRouterVisionProvider.js vendored Normal file
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@@ -0,0 +1,161 @@
"use strict";
var __importDefault = (this && this.__importDefault) || function (mod) {
return (mod && mod.__esModule) ? mod : { "default": mod };
};
Object.defineProperty(exports, "__esModule", { value: true });
exports.OpenRouterVisionProvider = void 0;
const fs_1 = __importDefault(require("fs"));
const axios_1 = __importDefault(require("axios"));
class OpenRouterVisionProvider {
constructor(config) {
this.config = config;
this.axiosInstance = axios_1.default.create({
baseURL: config.baseUrl || 'https://openrouter.ai/api/v1',
headers: {
'Authorization': `Bearer ${config.apiKey}`,
'Content-Type': 'application/json',
'HTTP-Referer': 'https://github.com/anomalyco/aidio-description',
'X-Title': 'Aidio Description Generator'
}
});
}
async describeImage(imagePath, prompt) {
try {
const imageData = fs_1.default.readFileSync(imagePath);
const base64Image = imageData.toString('base64');
const response = await this.axiosInstance.post('/chat/completions', {
model: this.config.model,
temperature: 0.1,
messages: [
{
role: 'user',
content: [
{ type: 'text', text: prompt },
{
type: 'image_url',
image_url: {
url: `data:image/jpeg;base64,${base64Image}`
}
}
]
}
],
max_tokens: this.config.maxTokens || 300
});
const data = response.data;
return {
description: data.choices?.[0]?.message?.content?.trim() || 'No description generated.',
usage: {
inputTokens: data.usage?.prompt_tokens || 0,
outputTokens: data.usage?.completion_tokens || 0,
totalTokens: data.usage?.total_tokens || 0
}
};
}
catch (error) {
console.error('OpenRouter describeImage error:', error.response?.data || error.message);
return {
description: 'Unable to describe this image.',
usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
};
}
}
async compareImages(image1Path, image2Path, prompt) {
try {
const image1Data = fs_1.default.readFileSync(image1Path);
const image2Data = fs_1.default.readFileSync(image2Path);
const base64Image1 = image1Data.toString('base64');
const base64Image2 = image2Data.toString('base64');
const response = await this.axiosInstance.post('/chat/completions', {
model: this.config.model,
temperature: 0.1,
messages: [
{
role: 'user',
content: [
{ type: 'text', text: prompt },
{
type: 'image_url',
image_url: { url: `data:image/jpeg;base64,${base64Image1}` }
},
{
type: 'image_url',
image_url: { url: `data:image/jpeg;base64,${base64Image2}` }
}
]
}
],
max_tokens: this.config.maxTokens || 300
});
const data = response.data;
return {
description: data.choices?.[0]?.message?.content?.trim() || 'No description generated.',
usage: {
inputTokens: data.usage?.prompt_tokens || 0,
outputTokens: data.usage?.completion_tokens || 0,
totalTokens: data.usage?.total_tokens || 0
}
};
}
catch (error) {
console.error('OpenRouter compareImages error:', error.response?.data || error.message);
return {
description: 'Unable to describe the differences between these images.',
usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
};
}
}
async describeBatch(imagePaths, lastBatchContext, prompt) {
try {
const imagesBase64 = imagePaths.map(fp => {
const imageData = fs_1.default.readFileSync(fp);
return imageData.toString('base64');
});
const messages = [
{
role: 'user',
content: [
{ type: 'text', text: prompt }
]
}
];
if (lastBatchContext && lastBatchContext.lastDescription) {
messages.unshift({
role: 'system',
content: `Previous batch summary: ${lastBatchContext.lastDescription}`
});
}
imagesBase64.forEach(base64 => {
messages[messages.length - 1].content.push({
type: 'image_url',
image_url: {
url: `data:image/jpeg;base64,${base64}`
}
});
});
const response = await this.axiosInstance.post('/chat/completions', {
model: this.config.model,
messages,
max_tokens: this.config.maxTokens || 300
});
const data = response.data;
return {
description: data.choices?.[0]?.message?.content?.trim() || 'No description generated.',
usage: {
inputTokens: data.usage?.prompt_tokens || 0,
outputTokens: data.usage?.completion_tokens || 0,
totalTokens: data.usage?.total_tokens || 0
}
};
}
catch (error) {
console.error('OpenRouter describeBatch error:', error.response?.data || error.message);
return {
description: 'Unable to describe this batch of images.',
usage: { inputTokens: 0, outputTokens: 0, totalTokens: 0 }
};
}
}
}
exports.OpenRouterVisionProvider = OpenRouterVisionProvider;
//# sourceMappingURL=openRouterVisionProvider.js.map

1
dist/providers/vision/openRouterVisionProvider.js.map vendored Normal file
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@@ -0,0 +1 @@
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8
dist/providers/vision/visionProviderFactory.d.ts vendored Normal file
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@@ -0,0 +1,8 @@
import { VisionProvider } from '../../interfaces';
import { Config } from '../../config/config';
/**
* Factory for creating vision AI providers
*/
export declare class VisionProviderFactory {
static getProvider(config: Config): VisionProvider;
}

34
dist/providers/vision/visionProviderFactory.js vendored Normal file
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@@ -0,0 +1,34 @@
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
exports.VisionProviderFactory = void 0;
const openAIVisionProvider_1 = require("./openAIVisionProvider");
const geminiVisionProvider_1 = require("./geminiVisionProvider");
const ollamaVisionProvider_1 = require("./ollamaVisionProvider");
const openRouterVisionProvider_1 = require("./openRouterVisionProvider");
/**
* Factory for creating vision AI providers
*/
class VisionProviderFactory {
static getProvider(config) {
const providerName = config.visionProvider;
const providerConfig = config.visionProviders[providerName];
if (!providerConfig) {
throw new Error(`Vision provider "${providerName}" not configured.`);
}
switch (providerName) {
case 'openai':
return new openAIVisionProvider_1.OpenAIVisionProvider(providerConfig);
case 'gemini':
return new geminiVisionProvider_1.GeminiVisionProvider(providerConfig);
case "ollama":
return new ollamaVisionProvider_1.OllamaVisionProvider(providerConfig);
case 'openrouter':
return new openRouterVisionProvider_1.OpenRouterVisionProvider(providerConfig);
// Add other providers here
default:
throw new Error(`Vision provider "${providerName}" not implemented.`);
}
}
}
exports.VisionProviderFactory = VisionProviderFactory;
//# sourceMappingURL=visionProviderFactory.js.map

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@@ -0,0 +1 @@
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