Trend2Video Pro

Trend-to-Video Agent Framework

Turn trends into publish-ready short video packages with trend intelligence, creator fit, viral prediction, and quality control.

Quick Start · What It Builds · Architecture · Output Example · Why Different

The Idea

Trend2Video Pro is a Trend Intelligence + Content Execution System.

It is not a dashboard.
It is not a simple AI video generator.
It is an execution-first agent framework that decides what to make, generates the assets, checks quality, and exports a ready-to-review content package.

Input trend -> system decides -> system generates -> publish-ready package

What It Builds

Input	Output
Trend title or URL	Vertical MP4 video
Target platform	Title
Creator style	Thumbnail
Duration	Description
Creator profile	Hashtags
Quality rules	Subtitles
Optional page screenshot	Quality report

The final artifact is a local folder, not only a video file.

Architecture

flowchart LR
    A["Trend"] --> B["Scoring"]
    B --> C["Creator Fit"]
    C --> D["Script"]
    D --> E["Fact Check"]
    E --> F["Storyboard"]
    F --> G["Video"]
    G --> H["Quality Review"]
    H --> I["Publish Package"]

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Agent layer:

src/agents/
  trend_scout.py
  trend_analyzer.py
  creator_strategy.py
  script_writer.py
  fact_checker.py
  storyboard.py
  video_producer.py
  quality_reviewer.py
  orchestrator.py

Main entry point:

from src.agents.orchestrator import run_trend_to_video

result = run_trend_to_video(
    topic={"title": "AI Agent Trend", "url": ""},
    platform="Bilibili",
    style="Tech News",
    duration=60,
)

Output Example

outputs/publish_packages/20260613_222144_ai-agent-trend/
  video.mp4
  title.txt
  description.txt
  hashtags.txt
  thumbnail.png
  subtitles.srt
  quality_report.md
  metadata.json

This package is the product output: a creator can review it, edit copy if needed, and publish manually.

Why It Is Different

Capability	AI video tools	Script generators	Trend2Video Pro
Trend discovery	Sometimes	No	Yes
Viral prediction	Rare	No	Yes
Creator memory	No	No	Yes
Creator-topic fit	No	No	Yes
Script generation	Yes	Yes	Yes
Fact/risk review	Limited	Limited	Yes
Storyboard	Sometimes	No	Yes
MP4 output	Yes	No	Yes
Publish package	Rare	No	Yes
Local mock mode	Often no	Usually yes	Yes

Trend2Video Pro focuses on the full execution loop, not one isolated generation step.

Execution Console

The Streamlit UI has only three sections:

1. Input: trend/url + platform + style + duration
2. Execute: one-click agent pipeline
3. Output: video preview + score + package downloads

No analytics dashboard. No charts. No raw logs. The UI is built for creator execution.

Screenshots

Execution console	Output quality

Trend input	Demo workflow

Quick Start

git clone https://github.com/2417467487-hub/Trend2Video-Pro.git
cd Trend2Video-Pro
python -m venv .venv

Windows:

.venv\Scripts\activate

macOS/Linux:

source .venv/bin/activate

Install dependencies:

pip install -r requirements.txt
playwright install chromium
copy .env.example .env

macOS/Linux:

cp .env.example .env

Run the product UI:

streamlit run app.py

CLI

python main.py generate --title "AI Agent Trend" --platform "Bilibili" --style "Tech News" --duration 60
python main.py update-topics
python main.py list-topics
python main.py generate-from-topic --topic-id 1 --platform "Xiaohongshu" --style "Tech News" --duration 60

API

uvicorn api:app --reload

Endpoints:

• POST /api/generate
• POST /api/score
• GET /api/topics
• POST /api/update-topics
• POST /api/generate-from-topic?topic_id=1
• GET /health

Creator Intelligence

Creator memory lives in src/creator/ and stores:

• niche
• tone
• target platforms
• audience
• past content memory
• creator-topic fit signals

It influences scoring and content angle recommendation before generation.

Viral Prediction MVP

The prediction engine lives in src/prediction/ and outputs:

• viral_probability
• predicted_views
• confidence_level
• explanation

Inputs include trend score, competition, urgency, creator fit, and hook strength. The current baseline is transparent and rule-based, with room for an sklearn model once enough history exists.

Quality Control

Quality checks are implemented in code:

• 3-second hook signal
• 3 key points
• CTA presence
• hook score
• clarity score
• density score
• factual risk score
• overall score

Scripts below quality threshold are rewritten once before the package is finalized.

Benchmark

python evaluation/run_benchmark.py

Mock-mode benchmark metrics:

• hook score
• viral accuracy proxy
• script quality
• publish readiness

Tests

pytest

The test suite runs without API keys.

Roadmap

• Citation-aware fact checking
• More creator profile presets
• More visual templates and subtitle styles
• Trained viral prediction once enough package history exists
• Optional publishing integrations after local export is stable

License

MIT. See LICENSE.