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alfman99/tribeV2-ViralAnalyser

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App README

TRIBE Review MVP

Hosted Hugging Face Space deployment of tribeV2_ViralAnalyser v1.2.2.

Upload your own videos to analyze short video ads with Meta TRIBE v2 and get an editing-friendly review. Also includes experimental website and visual-grid review modes for attention maps.

Private local web app for analyzing short video ads with Meta TRIBE v2 and presenting the result as an editing-friendly review. It also includes experimental website and visual-grid review modes for attention maps.

The app runs the official TRIBE v2 inference path, visualizes the predicted brain-response curve and heatmap, and adds a practical recommendation layer for comparing cuts and finding weak moments in the timeline.

Website review mode does not claim eye-tracking. It captures landing pages in a local browser and builds a visual attention estimate from page structure, contrast, color, and layout signals.

Visual grid review mode is a fast attention pre-check for static creatives. It ranks options inside one uploaded image grid using visual salience signals; it does not claim guaranteed post or ad performance.

Credits and sources

Application shell, interface, workflow layer, and practical editing wrapper:

Official model and research sources:

All inference is run through the official TRIBE v2 model as released by Meta on Hugging Face under the CC BY-NC 4.0 license.

This repository is a non-commercial community prototype built around the official Meta TRIBE v2 model. TRIBE v2 is used under the CC BY-NC 4.0 license, and Meta remains the owner of the model, its weights, and associated research materials. This project is not affiliated with, endorsed by, sponsored by, or officially connected to Meta. It does not claim ownership over TRIBE v2, Meta research materials, model weights, brand assets, uploaded videos, or third-party materials. No sales, paid distribution, sublicensing, or commercial use are intended.

What it does

  • —Reviews one uploaded video as a deep analysis.
  • —Compares 2-4 uploaded versions side by side.
  • —Shows a predicted response-over-time curve.
  • —Shows a 3D brain activity visualization.
  • —Explains the major brain zones used by the model.
  • —Adds practical editing recommendations.
  • —Exports JSON and PDF reports.
  • —Uses a local Whisper speech layer for transcript/timing hints.
  • —Optionally uses Ollama for local recommendation copy rewriting when a supported local model is available.
  • —Reviews landing-page URLs with desktop and mobile captures.
  • —Shows original/heatmap toggles, fold markers, section scores, and layout recommendations.
  • —Attempts to close common cookie/GDPR banners before website screenshots.
  • —Compares two website URLs for before/after design checks.
  • —Reviews one uploaded visual grid and ranks the strongest post, thumbnail, product, or ad options.
  • —Shows a visual-grid heatmap, top picks, per-cell scores, and practical reasons.

What it is not

  • —It is not an official Meta application.
  • —It is not affiliated with, endorsed by, sponsored by, or officially connected to Meta.
  • —It is not a commercial product.
  • —It is not a guaranteed virality predictor.
  • —It does not measure one specific real viewer.
  • —It does not claim ownership over TRIBE v2, Meta research assets, uploaded videos, or any third-party materials.

See NOTICE.md for the non-commercial notice and official source links.

Main idea

The response graph is the primary tool.

Use the curve first, then use the video player, brain map, and recommendation cards to understand what is happening around the marked timestamps.

Workflows

A. Compare 2-4 versions

Use compare mode for several versions of the same creative. The goal is not to blindly pick one full video. The goal is to find the strongest sections across versions and use them as an editing map for the next cut.

[image]

Typical use:

  1. 1.Upload 2-4 variants of the same ad/video idea.
  2. 2.Compare the overlaid curves.
  3. 3.Mark which version has the strongest hook, middle hold, transitions, and later useful section.
  4. 4.Build a new edit from the best-performing blocks.
  5. 5.Re-run the new edit against the current leader.

B. Improve one video

Use solo mode when you only have one cut. Look for real dips in the graph, click the timestamp, inspect nearby frames, and test one edit at a time.

[image]

Typical edits:

  • —Cut or shorten slow fragments.
  • —Speed up a section that drags.
  • —Move the main action or caption earlier.
  • —Make the subject larger or clearer.
  • —Remove visual clutter.
  • —Add a new beat before the graph drops.

C. Review a website URL

Use website mode for landing pages, hero sections, product pages, and before/after redesign checks. Paste a URL, let the app capture the page in a local browser, then inspect the predicted visual attention map by fold and scroll section.

[image]

Typical use:

  1. 1.Paste one website URL.
  2. 2.Review the desktop and mobile captures.
  3. 3.Toggle between original and heatmap views.
  4. 4.Use fold markers to inspect each scroll section.
  5. 5.Check which blocks pull attention and which sections are quiet.
  6. 6.Read the layout recommendations and adjust the page.

For before/after checks, paste a second URL in the optional compare field. The app compares the desktop and mobile attention scores side by side.

D. Pick from a visual grid

Use visual grid mode when you made several static options and do not know which one to post first.

Typical use:

  1. 1.Put 4-12 visual options into one image grid.
  2. 2.Upload that grid into Visual grid review.
  3. 3.Leave rows/columns empty for auto-detect, or fill them manually for unusual grids.
  4. 4.Compare the heatmap and top-3 ranked options.
  5. 5.Use the text notes to understand whether the winner is stronger because of contrast, color, position, focus, or lower visual clutter.

This is an attention pre-check, not a promise that the winning image will outperform everywhere.

Full workflow notes: docs/WORKFLOWS.md

Project structure

text
.
|-- app.py                         # FastAPI app and report routes
|-- bootstrap_models.py            # First-launch dependency and model preparation
|-- tribe_runtime.py               # TRIBE v2 model loading and inference wrapper
|-- official_report.py             # Official-output report layer
|-- review_engine.py               # Local recommendation and comparison logic
|-- brain_visualization.py         # Brain heatmap visualization data
|-- visual_grid_analysis.py        # Static visual-grid attention scoring
|-- website_capture.py             # Local browser website screenshot capture
|-- website_analysis.py            # Website attention heatmap and section scoring
|-- report_localization.py         # UI/report copy layer
|-- pdf_report.py                  # Chrome-based HTML-to-PDF export
|-- templates/index.html           # Main web UI
|-- static/vendor/                 # Local browser dependencies
|-- runtime_media/                 # Local runtime uploads/reports, ignored by Git
|-- docs/INSTALL_WINDOWS.md        # Windows setup guide
`-- docs/TROUBLESHOOTING.md        # Common issues

Quick start on Windows

For non-technical users:

  1. 1.Click the green Code button on GitHub.
  2. 2.Click Download ZIP.
  3. 3.Extract the ZIP into a normal folder, for example Desktop or Downloads.
  4. 4.Open the extracted folder.
  5. 5.Double-click Start_TRIBE_Review.cmd.
  6. 6.Keep the black terminal window open while the app prepares itself.
  7. 7.When the terminal says setup is complete, close it.
  8. 8.Double-click Start_TRIBE_Review.cmd again.
  9. 9.The app should open in your browser.

The first launch can take a while because the app downloads and installs everything it needs. Later launches are much faster. Website URL mode uses a locally installed Chrome or Microsoft Edge browser for screenshots.

Quick command version:

powershell
Start_TRIBE_Review.cmd

If the browser does not open automatically, open:

url
http://127.0.0.1:8000

Full setup notes: docs/INSTALL_WINDOWS.md

Requirements

Recommended local setup:

  • —Windows 10/11 64-bit
  • —Python 3.11
  • —16 GB RAM
  • —Modern 8-core CPU or better
  • —NVIDIA GPU
  • —6 GB VRAM minimum, 12 GB+ preferred
  • —30 GB+ free disk space, preferably on SSD

Runtime data and privacy

Uploaded videos and generated reports are written to runtime_media/.

That folder is intentionally ignored by Git. Do not commit runtime media, reports, transcripts, tokens, logs, model weights, or cache folders.

Development checks

Run a syntax check:

powershell
python -m py_compile app.py bootstrap_models.py tribe_runtime.py speech_runtime.py official_report.py review_engine.py report_localization.py pdf_report.py brain_visualization.py runtime_setup.py website_capture.py website_analysis.py visual_grid_analysis.py

Run a smoke test with a local video:

powershell
python smoke_test.py C:\path\to\test-video.mp4

License and use

No open-source license is granted yet. Treat this repository as private, all-rights-reserved, non-commercial evaluation code unless a license is explicitly added later.

Review the official TRIBE v2 license and all third-party licenses before any redistribution or public use.

TRIBE v2 model weights are not included in this repository. They are downloaded from the official Hugging Face model page by the local setup flow and remain subject to the official TRIBE v2 license.