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Audience is an open-source AI platform that simulates how a real audience would react to your content — before you upload it anywhere.
Upload a video, image, audio file, animation, or artwork. Choose your target platform. Get a full Audience Reaction Report — with emotion scores, peak moment timestamps, dopamine hit detection, simulated comments, and a demographic breakdown of who will love it and who will scroll past.
No sugarcoating. No empty praise. Just honest, data-driven feedback from a simulated crowd.
- 🎥 Multi-media support — Video, Image, Audio, Animation, Artwork
- 📱 Platform-aware analysis — Tune reactions for Instagram, YouTube, TikTok, and more
- ⚡ Peak Moment Detection — Timestamps of when your audience spikes or drops off
- 📊 Emotion Score Dashboard — Interest, Excitement, Happiness, Dopamine Hit, Boring Rate
- 💬 Simulated Comment Section — Real-feeling comments, arguments included
- 👥 Demographic Breakdown — Age groups, gender perception, regional/cultural flags
- 🔍 Honest Critique — Actionable feedback with no sugarcoating
- 🏆 Final Verdict + Performance Prediction — Viral potential, retention rate, share likelihood
📊 AUDIENCE REACTION REPORT
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🎯 Content Summary:
A 30-second Instagram Reel featuring fast-cut dark humor about
Monday mornings. Strong hook, slow middle, punchy ending.
⚡ Peak Moments:
🔥 [0:04] Opening hook lands hard → Immediate interest spike
🔥 [0:18] Dark humor punchline → Dopamine hit expected
😐 [0:11] Slow transition → Possible scroll-away zone
📈 Emotion Scores:
Interest Score ████████░░ 78/100
Excitement Score ███████░░░ 68/100
Happiness Score ████████░░ 75/100
Boring Rate ███░░░░░░░ 28/100
Dopamine Hit Score ████████░░ 80/100
Re-watch Potential ███████░░░ 70/100
💬 Simulated Comments (Instagram):
@user_98x: bro the timing at 0:18 was PERFECT 💀
@reel_critic: first 5 seconds almost lost me ngl
@darkhumorfan: no because why is this so accurate 💀
@just_scrolling99: i've watched this 4 times already why
@real_talk_22: the editing in the middle dragged a bit
🏆 Final Verdict:
"Strong hook, shaky middle, great ending — one edit away from viral."
📊 Predicted Performance on Instagram Reels:
Viral Potential ██████░░░░ 62%
Retention Rate █████░░░░░ 50%
Share Likelihood ███████░░░ 72%
| Layer | Technology |
|---|---|
| AI Model | Meta Tribe V2 |
| Backend | Python |
| Frontend | (Coming soon) |
| License | MIT (Non-Commercial) |
⚠️ This project is currently in early development. Full setup instructions will be added as the project progresses.
- Python 3.10 or above
- Git installed on your machine
- Basic understanding of running terminal commands
# 1. Clone the repository
git clone https://github.com/YOUR_USERNAME/audience.git
# 2. Navigate into the project folder
cd audience
# 3. Install dependencies (once requirements.txt is added)
pip install -r requirements.txt
# 4. Run the app
python main.pyFull setup guide will be added in the
/docsfolder as development progresses.
audience/
│
├── backend/ → Core AI logic and API integration
├── frontend/ → User interface
├── docs/ → Documentation and guides
├── .gitignore
├── LICENSE
└── README.md
This project is released under the MIT License for open-source and educational use.
Important: This project uses Meta Tribe V2, which is governed by Meta's non-commercial license. Therefore, this project may not be used for any commercial purpose. It is strictly a learning and open-source initiative.
See the LICENSE file for full details.
We are beginners learning as we build — and we welcome everyone at any skill level!
If you have ideas, bug reports, or want to contribute code:
- Fork this repo
- Create a new branch (
git checkout -b feature/your-idea) - Commit your changes
- Open a Pull Request — describe what you changed and why
You can also open an Issue or start a Discussion if you have questions or suggestions.
Built with curiosity and zero prior AI/ML experience — learning in public, one commit at a time.
🎭 Audience — Simulating the crowd so you don't have to guess.