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Ken Yasue
2025-08-17 17:47:54 +02:00
parent 60a5272054
commit dc4e57db2c
15 changed files with 944 additions and 68 deletions
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279
package-lock.json generated
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6
package.json
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@ -6,19 +6,23 @@
"scripts": {
"start": "tsc && node dist/index.js",
"build": "tsc",
"test": "echo \"Error: no test specified\" && exit 1"
"test": "echo \"Error: no test specified\" && exit 1",
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"keywords": [],
"author": "",
"license": "ISC",
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81
src/generateImage.ts Normal file
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@ -0,0 +1,81 @@
import { query } from './lib/mysql';
import { generateImage } from './lib/image-generator';
import { logger } from './lib/logger';
import dotenv from 'dotenv';
dotenv.config();
interface VideoRecord {
id: number;
genre: string;
sub_genre: string;
image_prompt: string;
// Add other fields if necessary
}
const servers = [
{
baseUrl: process.env.SERVER1_COMFY_BASE_URL,
outputDir: process.env.SERVER1_COMFY_OUTPUT_DIR,
},
{
baseUrl: process.env.SERVER2_COMFY_BASE_URL,
outputDir: process.env.SERVER2_COMFY_OUTPUT_DIR,
},
];
async function processServerQueue(server: any, videos: any[]) {
for (const video of videos) {
try {
const fileName = `${video.id}_${video.genre}_${video.sub_genre}.png`.replace(/\s/g, '_');
const imagePath = await generateImage(
video.image_prompt,
fileName,
server.baseUrl,
server.outputDir,
'flux',
{ width: 720, height: 1280 }
);
await query('UPDATE video SET image_path = ? WHERE id = ?', [imagePath, video.id]);
logger.info(`Generated and saved image for video ${video.id} at ${imagePath}`);
} catch (error) {
logger.error(`Failed to generate image for video ${video.id}:`, error);
}
}
}
async function main() {
try {
const videosToProcess = await query(
"SELECT * FROM video WHERE image_prompt IS NOT NULL AND (image_path IS NULL OR image_path = '')"
) as any[];
if (videosToProcess.length === 0) {
logger.info('No images to generate.');
return;
}
const queues: VideoRecord[][] = servers.map(() => []);
videosToProcess.forEach((video, index) => {
queues[index % servers.length].push(video);
});
const promises = servers.map((server, index) => {
if (!server.baseUrl || !server.outputDir) {
logger.warn(`Server ${index + 1} is not configured. Skipping.`);
return Promise.resolve();
}
return processServerQueue(server, queues[index]);
});
await Promise.all(promises);
logger.info('Finished generating all images.');
} catch (error) {
logger.error('An error occurred during image generation:', error);
} finally {
process.exit();
}
}
main();

116
src/generatePrompt.ts Normal file
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@ -0,0 +1,116 @@
import fs from 'fs';
import path from 'path';
import { query } from './lib/mysql';
import { logger } from './lib/logger';
import { callLMStudio } from './lib/lmstudio';
async function main() {
//await insertGenres();
await generatePrompts();
process.exit();
}
async function insertGenres() {
logger.info('Starting genre insertion...');
try {
const genresPath = path.join(__dirname, 'data', 'genres.json');
const genresData = JSON.parse(fs.readFileSync(genresPath, 'utf-8'));
for (const genre in genresData) {
if (genresData.hasOwnProperty(genre)) {
const items = genresData[genre];
for (let item of items) {
if (Array.isArray(item)) {
for (const subItem of item) {
await processItem(genre, subItem);
}
} else {
await processItem(genre, item);
}
}
}
}
logger.info('Finished processing genres.');
} catch (error) {
logger.error('Error processing genres:', error);
}
}
async function generatePrompts() {
logger.info('Starting prompt generation...');
try {
const videosToUpdate = await query(
'SELECT * FROM video WHERE image_prompt IS NULL OR video_prompt IS NULL'
) as any[];
for (const video of videosToUpdate) {
const prompt = `
Based on the following video details, generate an image prompt and a video prompt.
Each prompt should be around 100 words.
The output should be a JSON object in the format: { "imagePrompt": "...", "videoPrompt": "..." }
For video prompt, it takes 8 seconds. So start with current scene, describe action and camera work.
Please include lighting details too.
Details:
- Genre: ${video.genre}
- Sub-Genre: ${video.sub_genre}
- Scene: ${video.scene}
- Action: ${video.action}
- Camera: ${video.camera}
`;
logger.info(`Generating prompt for video ID: ${video.id}`);
logger.debug(prompt);
let success = false;
for (let i = 0; i < 10; i++) {
try {
const response = await callLMStudio(prompt);
if (response && response.imagePrompt && response.videoPrompt) {
await query(
'UPDATE video SET image_prompt = ?, video_prompt = ? WHERE id = ?',
[response.imagePrompt, response.videoPrompt, video.id]
);
logger.info(`Successfully updated prompts for video ID: ${video.id}`);
success = true;
break;
}
} catch (error) {
logger.warn(`Attempt ${i + 1} failed for video ID ${video.id}:`, error);
}
}
if (!success) {
logger.error(`Failed to generate and parse prompts for video ID: ${video.id} after 10 attempts.`);
}
}
logger.info('Finished generating prompts.');
} catch (error) {
logger.error('Error generating prompts:', error);
}
}
async function processItem(genre: string, item: any) {
if (item && typeof item === 'object' && !Array.isArray(item)) {
const { subGenre, scene, action, camera } = item;
// Check if the item already exists
const existing = await query(
'SELECT id FROM video WHERE genre = ? AND sub_genre = ? AND scene = ? AND action = ? AND camera = ?',
[genre, subGenre, scene, action, camera]
) as any[];
if (existing.length === 0) {
// Insert the new item
await query(
'INSERT INTO video (genre, sub_genre, scene, action, camera) VALUES (?, ?, ?, ?, ?)',
[genre, subGenre, scene, action, camera]
);
logger.info(`Inserted: ${genre} - ${subGenre} - ${scene}`);
} else {
logger.info(`Skipped (exists): ${genre} - ${subGenre} - ${scene}`);
}
}
}
main();

20
src/index_prompt.ts
View File

@ -3,6 +3,12 @@ import { generateVideo } from './lib/video-generator';
import { logger } from './lib/logger';
import { Scene } from './types';
import scenes from './scenes/space.json';
import dotenv from 'dotenv';
dotenv.config();
const COMFY_BASE_URL = process.env.COMFY_BASE_URL;
const COMFY_OUTPUT_DIR = process.env.COMFY_OUTPUT_DIR;
interface ProcessedScene {
scene: Scene;
@ -10,6 +16,11 @@ interface ProcessedScene {
}
async function prepareImageForScene(scene: Scene): Promise<ProcessedScene | null> {
if (!COMFY_BASE_URL || !COMFY_OUTPUT_DIR) {
logger.error('COMFY_BASE_URL and COMFY_OUTPUT_DIR must be set in the environment variables.');
return null;
}
try {
const startTime = Date.now();
logger.info(`--- Preparing image for scene: ${scene.idea} ---`);
@ -19,6 +30,8 @@ async function prepareImageForScene(scene: Scene): Promise<ProcessedScene | null
const generatedImagePath = await generateImage(
scene.image_prompt,
imageFileName,
COMFY_BASE_URL,
COMFY_OUTPUT_DIR,
'flux',
{ width: 1280, height: 720 }
);
@ -35,6 +48,11 @@ async function prepareImageForScene(scene: Scene): Promise<ProcessedScene | null
}
async function generateVideoForScene(processedScene: ProcessedScene) {
if (!COMFY_BASE_URL || !COMFY_OUTPUT_DIR) {
logger.error('COMFY_BASE_URL and COMFY_OUTPUT_DIR must be set in the environment variables.');
return;
}
const { scene, generatedImagePath } = processedScene;
try {
const startTime = Date.now();
@ -45,6 +63,8 @@ async function generateVideoForScene(processedScene: ProcessedScene) {
scene.video_prompt,
generatedImagePath,
videoFileName,
COMFY_BASE_URL,
COMFY_OUTPUT_DIR,
{ width: 1280, height: 720 }
);
const endTime = Date.now();

53
src/lib/db/schema.ts Normal file
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@ -0,0 +1,53 @@
import { query } from '../mysql';
async function createSchema() {
try {
console.log('Creating tables...');
await query(`
CREATE TABLE IF NOT EXISTS video (
id INT AUTO_INCREMENT PRIMARY KEY,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
modified_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
genre VARCHAR(255),
sub_genre VARCHAR(255),
scene TEXT,
action TEXT,
camera TEXT,
image_prompt TEXT,
video_prompt TEXT,
image_path VARCHAR(255),
video_path VARCHAR(255)
)
`);
await query(`
CREATE TABLE IF NOT EXISTS tag (
id INT AUTO_INCREMENT PRIMARY KEY,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
modified_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
tag VARCHAR(255) UNIQUE
)
`);
await query(`
CREATE TABLE IF NOT EXISTS tag_video (
id INT AUTO_INCREMENT PRIMARY KEY,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
modified_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
tag_id INT,
video_id INT,
FOREIGN KEY (tag_id) REFERENCES tag(id),
FOREIGN KEY (video_id) REFERENCES video(id)
)
`);
console.log('Tables created successfully.');
} catch (error) {
console.error('Error creating tables:', error);
} finally {
process.exit();
}
}
createSchema();

58
src/lib/db/tag.ts Normal file
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@ -0,0 +1,58 @@
import { query } from '../mysql';
export interface Tag {
id: number;
created_at: Date;
modified_at: Date;
tag: string;
}
export class TagModel {
static async create(tag: Omit<Tag, 'id' | 'created_at' | 'modified_at'>): Promise<number> {
const sql = 'INSERT INTO tag (tag) VALUES (?)';
const params = [tag.tag];
const result: any = await query(sql, params);
return result.insertId;
}
static async getById(id: number): Promise<Tag | null> {
const sql = 'SELECT * FROM tag WHERE id = ?';
const params = [id];
const rows: any = await query(sql, params);
return rows.length > 0 ? rows[0] as Tag : null;
}
static async getByTag(tag: string): Promise<Tag | null> {
const sql = 'SELECT * FROM tag WHERE tag = ?';
const params = [tag];
const rows: any = await query(sql, params);
return rows.length > 0 ? rows[0] as Tag : null;
}
static async getAll(): Promise<Tag[]> {
const sql = 'SELECT * FROM tag';
const rows: any = await query(sql);
return rows as Tag[];
}
static async update(id: number, tag: Partial<Omit<Tag, 'id' | 'created_at' | 'modified_at'>>): Promise<boolean> {
const fields = Object.keys(tag);
const values = Object.values(tag);
if (fields.length === 0) {
return false;
}
const sql = `UPDATE tag SET ${fields.map(field => `${field} = ?`).join(', ')} WHERE id = ?`;
const params = [...values, id];
const result: any = await query(sql, params);
return result.affectedRows > 0;
}
static async delete(id: number): Promise<boolean> {
const sql = 'DELETE FROM tag WHERE id = ?';
const params = [id];
const result: any = await query(sql, params);
return result.affectedRows > 0;
}
}

46
src/lib/db/tag_video.ts Normal file
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@ -0,0 +1,46 @@
import { query } from '../mysql';
export interface TagVideo {
id: number;
created_at: Date;
modified_at: Date;
tag_id: number;
video_id: number;
}
export class TagVideoModel {
static async create(tagVideo: Omit<TagVideo, 'id' | 'created_at' | 'modified_at'>): Promise<number> {
const sql = 'INSERT INTO tag_video (tag_id, video_id) VALUES (?, ?)';
const params = [tagVideo.tag_id, tagVideo.video_id];
const result: any = await query(sql, params);
return result.insertId;
}
static async getByVideoId(videoId: number): Promise<TagVideo[]> {
const sql = 'SELECT * FROM tag_video WHERE video_id = ?';
const params = [videoId];
const rows: any = await query(sql, params);
return rows as TagVideo[];
}
static async getByTagId(tagId: number): Promise<TagVideo[]> {
const sql = 'SELECT * FROM tag_video WHERE tag_id = ?';
const params = [tagId];
const rows: any = await query(sql, params);
return rows as TagVideo[];
}
static async delete(id: number): Promise<boolean> {
const sql = 'DELETE FROM tag_video WHERE id = ?';
const params = [id];
const result: any = await query(sql, params);
return result.affectedRows > 0;
}
static async deleteByTagAndVideo(tagId: number, videoId: number): Promise<boolean> {
const sql = 'DELETE FROM tag_video WHERE tag_id = ? AND video_id = ?';
const params = [tagId, videoId];
const result: any = await query(sql, params);
return result.affectedRows > 0;
}
}

59
src/lib/db/video.ts Normal file
View File

@ -0,0 +1,59 @@
import { query } from '../mysql';
export interface Video {
id: number;
created_at: Date;
modified_at: Date;
genre: string;
sub_genre: string;
scene: string;
action: string;
camera: string;
image_prompt: string;
video_prompt: string;
image_path: string;
video_path: string;
}
export class VideoModel {
static async create(video: Omit<Video, 'id' | 'created_at' | 'modified_at'>): Promise<number> {
const sql = 'INSERT INTO video (genre, sub_genre, scene, action, camera, image_prompt, video_prompt, image_path, video_path) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)';
const params = [video.genre, video.sub_genre, video.scene, video.action, video.camera, video.image_prompt, video.video_prompt, video.image_path, video.video_path];
const result: any = await query(sql, params);
return result.insertId;
}
static async getById(id: number): Promise<Video | null> {
const sql = 'SELECT * FROM video WHERE id = ?';
const params = [id];
const rows: any = await query(sql, params);
return rows.length > 0 ? rows[0] as Video : null;
}
static async getAll(): Promise<Video[]> {
const sql = 'SELECT * FROM video';
const rows: any = await query(sql);
return rows as Video[];
}
static async update(id: number, video: Partial<Omit<Video, 'id' | 'created_at' | 'modified_at'>>): Promise<boolean> {
const fields = Object.keys(video);
const values = Object.values(video);
if (fields.length === 0) {
return false;
}
const sql = `UPDATE video SET ${fields.map(field => `${field} = ?`).join(', ')} WHERE id = ?`;
const params = [...values, id];
const result: any = await query(sql, params);
return result.affectedRows > 0;
}
static async delete(id: number): Promise<boolean> {
const sql = 'DELETE FROM video WHERE id = ?';
const params = [id];
const result: any = await query(sql, params);
return result.affectedRows > 0;
}
}

63
src/lib/image-describer.ts
View File

@ -1,66 +1,7 @@
import fs from 'fs';
import path from 'path';
import dotenv from 'dotenv';
import { logger } from './logger';
dotenv.config();
const LLM_BASE_URL = process.env.LLM_BASE_URL;
import { callLMStudioWithFile } from './lmstudio';
async function describeImage(imagePath: string, prompt: string): Promise<any> {
if (!LLM_BASE_URL) {
throw new Error('LLM_BASE_URL is not defined in the .env file');
}
const imageBuffer = fs.readFileSync(imagePath);
const base64Image = imageBuffer.toString('base64');
for (let i = 0; i < 10; i++) {
let llmResponse = "";
try {
const requestUrl = new URL('v1/chat/completions', LLM_BASE_URL);
const response = await fetch(requestUrl, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
model: 'local-model',
messages: [
{
role: 'user',
content: [
{ type: 'image_url', image_url: { url: `data:image/jpeg;base64,${base64Image}` } },
{ type: 'text', text: prompt },
],
},
],
temperature: 0.7,
}),
});
const data = await response.json();
if (data.choices && data.choices.length > 0) {
const content = data.choices[0].message.content;
llmResponse = content;
const jsonMatch = content.match(/\{[\s\S]*\}/);
if (jsonMatch) {
return JSON.parse(jsonMatch[0]);
}
} else {
logger.error('Unexpected API response:', data);
}
} catch (error) {
logger.error(`Attempt ${i + 1} failed:`, error);
if (error instanceof TypeError && error.message.includes('fetch failed')) {
logger.error('Could not connect to the LM Studio server. Please ensure the server is running and accessible at the specified LLM_BASE_URL.');
}
logger.debug(`LLM response: ${llmResponse}`)
}
}
throw new Error('Failed to describe image after 10 attempts');
return await callLMStudioWithFile(imagePath, prompt);
}
export { describeImage };

7
src/lib/image-generator.ts
View File

@ -5,9 +5,6 @@ import dotenv from 'dotenv';
dotenv.config();
const COMFY_BASE_URL = process.env.COMFY_BASE_URL?.replace(/\/$/, '');
const COMFY_OUTPUT_DIR = process.env.COMFY_OUTPUT_DIR;
interface ImageSize {
width: number;
height: number;
@ -16,9 +13,13 @@ interface ImageSize {
async function generateImage(
prompt: string,
newFileName: string,
comfyBaseUrl: string,
comfyOutputDir: string,
imageModel: 'qwen' | 'flux' = 'qwen',
size: ImageSize = { width: 720, height: 1280 }
): Promise<string> {
const COMFY_BASE_URL = comfyBaseUrl.replace(/\/$/, '');
const COMFY_OUTPUT_DIR = comfyOutputDir;
let workflow;
if (imageModel === 'qwen') {
workflow = JSON.parse(await fs.readFile('src/comfyworkflows/generate_image.json', 'utf-8'));

115
src/lib/lmstudio.ts Normal file
View File

@ -0,0 +1,115 @@
import fs from 'fs';
import dotenv from 'dotenv';
import { logger } from './logger';
dotenv.config();
const LLM_BASE_URL = process.env.LLM_BASE_URL;
async function callLMStudio(prompt: string): Promise<any> {
if (!LLM_BASE_URL) {
throw new Error('LLM_BASE_URL is not defined in the .env file');
}
for (let i = 0; i < 10; i++) {
let llmResponse = "";
try {
const requestUrl = new URL('v1/chat/completions', LLM_BASE_URL);
const response = await fetch(requestUrl, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
model: 'local-model',
messages: [
{
role: 'user',
content: prompt,
},
],
temperature: 0.7,
}),
});
const data = await response.json();
if (data.choices && data.choices.length > 0) {
const content = data.choices[0].message.content;
llmResponse = content;
const jsonMatch = content.match(/\{[\s\S]*\}/);
if (jsonMatch) {
return JSON.parse(jsonMatch[0]);
}
} else {
logger.error('Unexpected API response:', data);
}
} catch (error) {
logger.error(`Attempt ${i + 1} failed:`, error);
if (error instanceof TypeError && error.message.includes('fetch failed')) {
logger.error('Could not connect to the LM Studio server. Please ensure the server is running and accessible at the specified LLM_BASE_URL.');
}
logger.debug(`LLM response: ${llmResponse}`)
}
}
throw new Error('Failed to get response from LLM after 10 attempts');
}
async function callLMStudioWithFile(imagePath: string, prompt: string): Promise<any> {
if (!LLM_BASE_URL) {
throw new Error('LLM_BASE_URL is not defined in the .env file');
}
const imageBuffer = fs.readFileSync(imagePath);
const base64Image = imageBuffer.toString('base64');
for (let i = 0; i < 10; i++) {
let llmResponse = "";
try {
const requestUrl = new URL('v1/chat/completions', LLM_BASE_URL);
const response = await fetch(requestUrl, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({
model: 'local-model',
messages: [
{
role: 'user',
content: [
{ type: 'image_url', image_url: { url: `data:image/jpeg;base64,${base64Image}` } },
{ type: 'text', text: prompt },
],
},
],
temperature: 0.7,
}),
});
const data = await response.json();
if (data.choices && data.choices.length > 0) {
const content = data.choices[0].message.content;
llmResponse = content;
const jsonMatch = content.match(/\{[\s\S]*\}/);
if (jsonMatch) {
return JSON.parse(jsonMatch[0]);
}
} else {
logger.error('Unexpected API response:', data);
}
} catch (error) {
logger.error(`Attempt ${i + 1} failed:`, error);
if (error instanceof TypeError && error.message.includes('fetch failed')) {
logger.error('Could not connect to the LM Studio server. Please ensure the server is running and accessible at the specified LLM_BASE_URL.');
}
logger.debug(`LLM response: ${llmResponse}`)
}
}
throw new Error('Failed to describe image after 10 attempts');
}
export { callLMStudio, callLMStudioWithFile };

33
src/lib/mysql.ts Normal file
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@ -0,0 +1,33 @@
import mysql from 'mysql2/promise';
import dotenv from 'dotenv';
dotenv.config();
const pool = mysql.createPool({
host: process.env.MYSQL_HOST,
user: process.env.MYSQL_USER,
password: process.env.MYSQL_PASSWOWD,
database: process.env.MYSQL_DB,
waitForConnections: true,
connectionLimit: 10,
queueLimit: 0
});
export async function query(sql: string, params: any[] = []) {
const [rows] = await pool.execute(sql, params);
return rows;
}
export async function testConnection() {
try {
const connection = await pool.getConnection();
console.log('Successfully connected to the database.');
connection.release();
return true;
} catch (error) {
console.error('Failed to connect to the database:', error);
return false;
}
}
export default pool;

7
src/lib/video-generator.ts
View File

@ -5,9 +5,6 @@ import dotenv from 'dotenv';
dotenv.config();
const COMFY_BASE_URL = process.env.COMFY_BASE_URL?.replace(/\/$/, '');
const COMFY_OUTPUT_DIR = process.env.COMFY_OUTPUT_DIR;
interface VideoSize {
width: number;
height: number;
@ -17,8 +14,12 @@ async function generateVideo(
prompt: string,
imagePath: string,
newFileName: string,
comfyBaseUrl: string,
comfyOutputDir: string,
size: VideoSize = { width: 720, height: 1280 }
): Promise<string> {
const COMFY_BASE_URL = comfyBaseUrl.replace(/\/$/, '');
const COMFY_OUTPUT_DIR = comfyOutputDir;
const workflow = JSON.parse(await fs.readFile('src/comfyworkflows/generate_video.json', 'utf-8'));
workflow['6']['inputs']['text'] = prompt;
workflow['52']['inputs']['image'] = imagePath;

69
src/testmysql.ts Normal file
View File

@ -0,0 +1,69 @@
import { VideoModel } from './lib/db/video';
import { TagModel } from './lib/db/tag';
import { TagVideoModel } from './lib/db/tag_video';
import pool from './lib/mysql';
async function main() {
try {
console.log('Starting CRUD operations test...');
// Create a video
const videoData = {
genre: 'Sci-Fi',
sub_genre: 'Cyberpunk',
scene: 'A rainy night in a futuristic city',
action: 'A detective chasing a rogue android',
camera: 'Low-angle shot, neon lights reflecting on wet pavement',
image_prompt: 'cyberpunk city rain neon',
video_prompt: 'detective chase android rain',
image_path: '/images/test.png',
video_path: '/videos/test.mp4',
};
const videoId = await VideoModel.create(videoData);
console.log(`Created video with ID: ${videoId}`);
// Create a tag
const tagData = { tag: 'cyberpunk' };
const tagId = await TagModel.create(tagData);
console.log(`Created tag with ID: ${tagId}`);
// Associate tag with video
const tagVideoId = await TagVideoModel.create({ tag_id: tagId, video_id: videoId });
console.log(`Associated tag ${tagId} with video ${videoId}. Association ID: ${tagVideoId}`);
// Retrieve and verify
const video = await VideoModel.getById(videoId);
console.log('Retrieved video:', video);
const tag = await TagModel.getById(tagId);
console.log('Retrieved tag:', tag);
const videoTags = await TagVideoModel.getByVideoId(videoId);
console.log('Retrieved video tags:', videoTags);
// Update
await VideoModel.update(videoId, { scene: 'A sunny day in a futuristic city' });
const updatedVideo = await VideoModel.getById(videoId);
console.log('Updated video:', updatedVideo);
await TagModel.update(tagId, { tag: 'dystopian' });
const updatedTag = await TagModel.getById(tagId);
console.log('Updated tag:', updatedTag);
// Cleanup
console.log('Cleaning up created records...');
await TagVideoModel.delete(tagVideoId);
console.log(`Deleted tag_video association with ID: ${tagVideoId}`);
await VideoModel.delete(videoId);
console.log(`Deleted video with ID: ${videoId}`);
await TagModel.delete(tagId);
console.log(`Deleted tag with ID: ${tagId}`);
console.log('Test completed successfully.');
} catch (error) {
console.error('An error occurred during the test:', error);
} finally {
await pool.end();
process.exit();
}
}
main();