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Short Video Analyzer

Dockerized short-video analysis service with two processing modes:

  • analyzer: key-frame extraction through byjlw/video-analyzer.
  • direct_video: sends the full video directly to a Qwen OpenAI-compatible vision API.

Videos are read from videos/. Results are written to output/<video-file-name>/. The web UI can also download public TikTok or Douyin videos into videos/ before analysis. Both processing modes produce the same normalized analysis.json schema, so DeepSeek postprocess works the same way for both.

Files

  • Dockerfile: builds the analyzer image and installs video-analyzer, Whisper, ffmpeg, requests, and yt-dlp.
  • docker-compose.yml: runs the service with local videos/ and output/ mounts.
  • scripts/analyze_one.sh: runs the existing key-frame video-analyzer flow.
  • scripts/direct_video_analyze.py: sends a small full video to Qwen using video_url content.
  • scripts/tiktok_download.py: downloads a public TikTok or Douyin video into videos/ (yt-dlp for TikTok, Playwright media capture for Douyin).
  • scripts/standardize_analysis.py: normalizes video-analyzer output to the shared schema.
  • scripts/translate_analysis.py: translates analyzer or audit JSON output into Simplified Chinese.
  • scripts/deepseek_postprocess.py: reads analysis.json and writes audit_result.json.
  • scripts/sociavault_tiktok_shop.py: extracts TikTok Shop product, review, or storefront data through SociaVault.
  • scripts/deepseek_shop_analyze.py: turns TikTok Shop extraction JSON into a Chinese DeepSeek analysis report.
  • scripts/web_app.py: serves the upload/analyze/result web UI.
  • scripts/run_web.sh: starts the web UI on port 4000, or the next available port.
  • scripts/setup_amazon_scraper.sh: checks or installs the ClawHub amazon-scraper Docker image on the remote server.
  • .env.example: template for runtime settings.

Environment

Create .env from the example:

cp .env.example .env
nano .env

Required and commonly used values:

VISION_API_KEY=your-vision-api-key
VISION_API_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
VISION_MODEL=qwen3-vl-flash

ANALYSIS_MODE=analyzer
DIRECT_VIDEO_MODEL=qwen3-vl-flash
DIRECT_VIDEO_FPS=2
DIRECT_VIDEO_AUDIO_MODE=whisper
DIRECT_VIDEO_UPLOAD_MODE=auto
TIKTOK_MAX_BYTES=2147483648
TIKTOK_PROXY_URL=
DOUYIN_PROXY_URL=
DOUYIN_COOKIE=

SOCIAVAULT_API_KEY=your-sociavault-api-key
SOCIAVAULT_API_BASE=https://api.sociavault.com
SOCIAVAULT_REGION=US
SOCIAVAULT_MAX_PAGES=1
SOCIAVAULT_REVIEW_PAGES=1

AMAZON_PROXY=
AMAZON_PROXIES=
AMAZON_MAX_PAGES=1

DEEPSEEK_API_KEY=your-deepseek-api-key
HF_ENDPOINT=https://hf-mirror.com

Optional cost estimation:

VISION_INPUT_PRICE_PER_1M=0
VISION_OUTPUT_PRICE_PER_1M=0

.env, videos/, and output/ are ignored by Git and should not be committed.

Ubuntu Server Setup

Clone and build:

cd /home/openclaw
git clone https://github.com/Joe1905/Video_analyzer.git
cd Video_analyzer
mkdir -p videos output
cp .env.example .env
nano .env
bash scripts/setup_amazon_scraper.sh
docker compose -p short-video-analyzer build

If the server uses legacy Compose:

docker-compose -p short-video-analyzer build

Run Compose with -p short-video-analyzer to keep containers and networks isolated from other Docker applications on the same server.

Web UI

Start the web UI:

bash scripts/run_web.sh

The script starts at port 4000 and automatically advances to the next available port if needed. Open the printed URL in your browser.

The page supports:

  • downloading a public TikTok or Douyin video URL into videos/
  • uploading a video into videos/
  • choosing 关键帧提取模式（video-analyzer） or 直接视频理解模式（Qwen）
  • showing and editing the analysis prompt before a run
  • optional DeepSeek postprocess
  • showing processing mode, model, token usage, estimated cost, and total elapsed time
  • viewing 提取内容（中文） and 分析结果（中文）
  • switching each result tab back to original JSON with 显示原文

Daily Hot Video Report

A daily hot-video report page is available on the same web port:

http://<server>:4000/report

The home page shows a banner for today's report. The v1 report manually collects TikTok hot-video candidates through SociaVault, stores the daily snapshot in local SQLite, and displays the ranked list. It does not generate a knowledge base or DeepSeek strategy summary yet.

The report API uses separate endpoints:

GET /api/report/today
GET /api/report/history
POST /api/report/run

Results are stored in:

data/hot_video_report.sqlite

It uses the existing SociaVault settings, especially SOCIAVAULT_API_KEY, SOCIAVAULT_API_BASE, and SOCIAVAULT_REGION. The primary hot-video source is SociaVault videos-popular, using days, page, count, and sort_by. HOT_VIDEO_POPULAR_SORTS controls the sort list (default: views,likes), and HOT_VIDEO_POPULAR_MAX_PAGES caps dynamic pagination (default: 5). The report target count is the report analysis count setting (default: 10), so displayed and analyzed videos stay aligned. Each collection round fetches ceil(remaining_target * 2) total videos across configured sorts, then advances the API page until the target is filled or max pages is reached. If videos-popular is unavailable, the report falls back to the older trending and search-top sources so the daily job can still attempt to run.

Recent-window filtering for hot-videos is controlled by HOT_VIDEO_RECENT_DAYS (default: 7), so only videos published in the last N days are considered by default. Expired videos that are older than this window are cleaned from report records on report queries/sync runs, so old cards disappear end-to-end.

Report pipeline safeguards:

  • REPORT_JOB_TIMEOUT (seconds, default 1800) limits total time spent on a single /api/report/run task.
  • REPORT_DEEPSEEK_TIMEOUT (seconds, default 180) limits each per-video deep-dive audit call.

Amazon Scraper

A separate Amazon scraper page is available at:

http://<server>:4000/amazon

It calls the ClawHub jiafar/amazon-scraper skill through its Docker image:

docker run --rm --network host -e AMAZON_PROXY -e AMAZON_PROXIES amazon-scraper node assets/amazon_handler.js "<amazon-url>" --pages 1

The page supports Amazon URLs, ASINs, and keyword searches. It intentionally uses only assets/amazon_handler.js; the skill's generic non-Amazon scraping mode is not exposed.

Results are saved under:

output/amazon/<job-id>/result.json

bash scripts/run_web.sh runs scripts/setup_amazon_scraper.sh before starting Compose. The setup script is idempotent: if the amazon-scraper image already exists it exits successfully; otherwise it runs openclaw skills install amazon-scraper. If the remote server does not have the OpenClaw CLI, install it or build the skill manually before starting the web service.

The web container needs access to the host Docker socket so it can start the scraper container:

- /var/run/docker.sock:/var/run/docker.sock

Optional proxy settings:

AMAZON_PROXY=http://user:pass@host:port
AMAZON_PROXIES=http://u:p@h1:8001,http://u:p@h2:8002
AMAZON_MAX_PAGES=1

The scraper container uses --network host so AMAZON_PROXY can reference 127.0.0.1. If the host proxy is on 127.0.0.1:7890, set:

AMAZON_PROXY=http://127.0.0.1:7890

Docker bridge networking on some hosts cannot forward container-to-host traffic. --network host is the reliable workaround.

TikTok Shop Extraction

A separate TikTok Shop page is available at:

http://<server>:4000/shop

It calls SociaVault with SOCIAVAULT_API_KEY to extract either a TikTok Shop product detail plus reviews, or a storefront product list. If DEEPSEEK_API_KEY is configured, it can also generate a Chinese product and content analysis report.

Results are saved under:

output/tiktok_shop/<job-id>/

The page uses separate endpoints and does not change the existing video analyzer page:

POST /api/shop-extract
GET /api/shop-events?id=<job-id>
GET /api/shop-job?id=<job-id>

TikTok / Douyin Download

The downloader is exposed on the same web port as the analyzer but uses separate endpoints:

POST /api/download
GET /api/download-job?id=<job-id>

Example API call:

curl -X POST http://127.0.0.1:4000/api/download \
  -H 'Content-Type: application/json' \
  -d '{"url":"https://v.douyin.com/xxxxxx/"}'

The API accepts only http or https URLs whose host is under tiktok.com, tiktokv.com, douyin.com, or iesdouyin.com. TikTok uses yt-dlp; Douyin uses Playwright to open the page and capture the largest media response. Downloaded videos are saved as videos/shortvideo_<platform>_<id>.mp4 when possible and then appear in the existing uploaded-video list.

Size limit is controlled by:

TIKTOK_MAX_BYTES=2147483648

TikTok may require a US-region proxy. In a Docker bridge container, use the Docker host gateway instead of 127.0.0.1 for a proxy running on the server host:

TIKTOK_PROXY_URL=http://172.17.0.1:7890

DOUYIN_PROXY_URL is optional and usually should stay empty for China-region Douyin access.

Some Douyin links require fresh browser cookies even when Playwright is used. Export a normal browser cookie header for douyin.com and put it in .env when needed:

DOUYIN_COOKIE=passport_csrf_token=...; sid_guard=...; ...

Do not commit .env.

Processing Modes

analyzer

Default mode. It uses video-analyzer to extract key frames, call Qwen on frames, keep frames, and run Whisper transcription. This is better for larger videos because it does not send the whole video payload to the vision API.

Run it directly:

bash scripts/analyze_one.sh test.mp4

The script uses:

  • --client openai_api
  • --api-url "$VISION_API_URL"
  • --model "$VISION_MODEL"
  • --output "output/test.mp4"
  • --max-frames 20
  • --keep-frames
  • --whisper-model small
  • --language zh

Override defaults:

MAX_FRAMES=30 WHISPER_MODEL=medium LANGUAGE=zh bash scripts/analyze_one.sh test.mp4

direct_video

Direct-video mode sends the full video to the OpenAI-compatible Qwen API using content type video_url.

For files under 7MB, it embeds the video as a Base64 data URL:

python scripts/direct_video_analyze.py test.mp4

Override defaults:

DIRECT_VIDEO_FPS=1 DIRECT_VIDEO_MODEL=qwen3-vl-flash python scripts/direct_video_analyze.py test.mp4

For files over 7MB, Base64 mode fails with a clear error. Automatic OSS upload is not implemented yet. A public URL hook is reserved:

python scripts/direct_video_analyze.py test.mp4 --public-url "https://example.com/test.mp4"

Current audio mode support:

DIRECT_VIDEO_AUDIO_MODE=whisper

Analysis Schema

Both modes write:

output/test.mp4/analysis.json

The shared schema includes:

  • schema_version
  • processing_mode
  • vision_model
  • audio_mode
  • metadata
  • summary
  • transcript
  • timeline
  • visual_evidence
  • raw_model_output
  • usage

usage records:

  • input_tokens
  • output_tokens
  • total_tokens
  • api_calls
  • elapsed_seconds
  • estimated_cost_usd

For analyzer, token counts are 0 unless the upstream tool exposes token usage; API call count and elapsed time are still recorded.

DeepSeek Postprocess

After analysis.json is generated:

docker compose -p short-video-analyzer run --rm analyzer python scripts/deepseek_postprocess.py output/test.mp4

With legacy Compose:

docker-compose -p short-video-analyzer run --rm analyzer python scripts/deepseek_postprocess.py output/test.mp4

Outputs:

output/test.mp4/audit_result.json
output/test.mp4/audit_result_zh.json

Direct Compose Usage

Run analyzer mode inside the container:

docker compose -p short-video-analyzer run --rm analyzer bash scripts/analyze_one.sh test.mp4

Run direct-video mode inside the container:

docker compose -p short-video-analyzer run --rm analyzer python scripts/direct_video_analyze.py test.mp4

Open a shell in the container:

docker compose -p short-video-analyzer run --rm analyzer bash

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