---
title: MoodSyncAI
emoji: 🎭
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 5.50.0
app_file: app.py
pinned: false
license: apache-2.0
short_description: Multi-modal emotion analyser (face, text, audio)
---

# 🎭 MoodSyncAI

**Multi-Modal Sentiment & Emotion Analyser** — combines facial emotion (Vision Transformer), text sentiment (Transformer), a fusion layer (with mismatch detection), and a generative model that summarises the emotional state in plain language. Includes a **webcam / short-video timeline** view.

All models are **100% free & open-source** (Hugging Face Hub).

## Components

| Stage | Model | Type | Requirement satisfied |
|---|---|---|---|
| Visual emotion | `trpakov/vit-face-expression` | **ViT** | CNN/ViT for facial emotion ✅ |
| Text sentiment | `j-hartmann/emotion-english-distilroberta-base` | **Transformer** | RNN/LSTM/Transformer ✅ |
| Speech-to-text | `openai/whisper-tiny` | **Whisper encoder-decoder** | Audio → text channel ✅ |
| Fusion | Valence-aligned multimodal fusion | rule-based + weighted | Fusion + mismatch ✅ |
| Generative | `google/flan-t5-base` | seq2seq Transformer | Generative summary ✅ |
| Webcam / video | OpenCV frame sampling + Plotly timeline | — | Real-time / video input ✅ |
| Attention viz | ViT attention rollout + last-layer text attention | interpretability | Attention visualisation ✅ |

## Run

**Prerequisite:** Python **3.10 – 3.13** (CPU is enough — no GPU required, no system ffmpeg required).

```powershell
# 1. Clone / copy this folder onto the new machine, then:
cd "<path-to-folder>"

# 2. Create a virtual env
python -m venv .venv
.\.venv\Scripts\Activate.ps1        # Windows
# source .venv/bin/activate         # macOS / Linux

# 3. Install (use --only-binary to skip Rust/MSVC compilation on Py3.13)
python -m pip install --upgrade pip
pip install -r requirements.txt --only-binary=:all:

# 4. Launch
python app.py
```

Browser opens at `http://127.0.0.1:7860`.

**To stop the app:** press `Ctrl+C` in the terminal running `python app.py`.

**First launch only:** downloads ~1.2 GB of models from Hugging Face into `~/.cache/huggingface/` (cached for all future runs, fully offline afterwards).

That's it — no system packages, no ffmpeg, no GPU, no model files to download manually.

## Tabs

1. **🖼️ Image + Text** — upload a face photo + type the spoken sentence → visual emotion bars, text emotion bars, fusion badge, generative summary. *Optional* attention-rollout heatmap on the face + per-token attention HTML when the toggle is on.
2. **📹 Webcam / Video + Text** — record a 3–10 s clip in the browser → per-frame emotion **timeline chart**, aggregated bars, fusion, summary.
3. **🎙️ Audio + Image** — record/upload audio + face photo. Whisper transcribes the audio; the transcript drives the text channel; full fusion + summary.
4. **🎬 Video with Audio** — record/upload a video *with sound*. Audio is extracted (imageio-ffmpeg), transcribed by Whisper, fed to the text classifier; frames produce the visual timeline; fused result + summary — no typing needed.
5. **ℹ️ About** — architecture & fusion logic.

## Fusion / mismatch rule

Each modality's emotion distribution is mapped to a **valence** in `[-1, +1]`.

- Opposite-sign valences → **MISMATCH DETECTED** (amber 🟠)
- Small delta → **ALIGNED** (green 🟢)
- Otherwise → **PARTIALLY ALIGNED** (yellow 🟡)

The generative model is prompted with the structured signals and writes a 2–3 sentence empathetic summary.