Update README with French docs and Space link

README.md

@@ -14,67 +14,57 @@ datasets:
 pipeline_tag: image-classification
 ---
 
-# Multimodal Deepfake Detection Model
+# 🕵️ Multimodal Deepfake Detection Model
 
-A multimodal ensemble model that classifies images, video frames, and text as **real** or **AI-generated/fake**, with confidence scores and GradCAM explainability maps.
+Modèle d'ensemble multimodal pour détecter les contenus générés par IA (images, vidéos, texte) avec explicabilité GradCAM.
 
-## Architecture
+## 🏗️ Architecture
 
-**Visual Branch**: EfficientNet-B0 (ImageNet pretrained) with L2-normalized features for image/video frame classification
-**Text Branch**: RoBERTa-base with mean pooling and MLP head for AI-generated text detection
-**Fusion Layer**: Learnable weighted late ensemble combining visual + text probabilities
-**Explainability**: GradCAM heatmaps on EfficientNet convolutional layers
+```
+Visual Branch    EfficientNet-B0/B4  ──┐
+                                       ├──► Fusion pondérée ──► Confidence [0-1]
+Text Branch      RoBERTa-base         ──┘
+                    ↓
+               GradCAM Heatmap (explicabilité)
+```
 
-## Usage
+## 🚀 Utilisation Rapide
 
 ```python
-from inference import load_model, classify_image, classify_text, classify_video, classify_multimodal
+from inference import load_model, classify_image, classify_text, classify_multimodal
 
 model, config = load_model('multimodal_ensemble.pt', device='cuda')
 
-# Image with GradCAM explainability
-result = classify_image(model, 'face.jpg', device='cuda', return_gradcam=True)
-print(f"Prediction: {result['prediction']} (confidence: {result['confidence']:.2%})")
-# result['gradcam'] contains the explainability heatmap
-
-# Text classification
-result = classify_text(model, 'This text was written by...')
-print(f"Prediction: {result['prediction']} (confidence: {result['confidence']:.2%})")
+# Image + GradCAM
+result = classify_image(model, 'face.jpg', return_gradcam=True)
+print(f"{result['prediction']} — confidence: {result['confidence']:.2%}")
+# result['gradcam'] → heatmap (224, 224)
 
-# Video classification
-result = classify_video(model, 'video.mp4', num_frames=32, aggregation='mean')
-print(f"Video: {result['prediction']} (confidence: {result['confidence']:.2%})")
+# Texte
+result = classify_text(model, 'Cet essai analyse les impacts...')
+print(f"{result['prediction']} — confidence: {result['confidence']:.2%}")
 
-# Multimodal (image + text)
+# Multimodal (image + texte)
 result = classify_multimodal(model, image_path_or_pil='face.jpg', text='Caption...')
-print(f"Combined: {result['prediction']} — Weights: {result['fusion_weights']}")
+print(f"Fusion: {result['prediction']} — poids: {result['fusion_weights']}")
 ```
 
-## Training
-
-### Datasets
-- **Visual**: [Hemg/deepfake-and-real-images](https://huggingface.co/datasets/Hemg/deepfake-and-real-images) — 140K+ face images (real vs deepfake)
-- **Text**: [artem9k/ai-text-detection-pile](https://huggingface.co/datasets/artem9k/ai-text-detection-pile) — 1.9GB human vs AI-generated text
-
-### Recipe
-| Component | Config |
-|-----------|--------|
-| Visual backbone | EfficientNet-B0 |
-| Visual optimizer | Adam, lr=1e-4, cosine annealing, 8 epochs |
-| Text backbone | RoBERTa-base |
-| Text optimizer | AdamW, lr=2e-5, warmup+cosine, 5 epochs |
-| Augmentations | RandomFlip, Rotation, ColorJitter, GaussianBlur, RandomErasing |
+## 📊 Jeux de Données
+- **Visuel**: [Hemg/deepfake-and-real-images](https://huggingface.co/datasets/Hemg/deepfake-and-real-images) — 528K images
+- **Texte**: [artem9k/ai-text-detection-pile](https://huggingface.co/datasets/artem9k/ai-text-detection-pile) — 1.88GB
 
-### Based on Research
-- **AWARE-NET** (arxiv:2505.00312): Learnable weighted fusion
-- **CLIP Deepfake** (arxiv:2503.19683): L2-normalized feature spaces
-- **DeTeCtive** (arxiv:2410.20964): RoBERTa for AI text detection
+## 📁 Fichiers
+| Fichier | Description |
+|---------|-------------|
+| `model.py` | Architecture complète |
+| `preprocessing.py` | Pipeline de données |
+| `inference.py` | API d'inférence |
+| `train.py` / `train_optimised.py` | Scripts d'entraînement |
+| `multimodal_ensemble.pt` | Checkpoint principal |
+| `gradcam_examples/` | Visualisations d'explicabilité |
 
-## Files
-- `model.py` — Architecture (GradCAM, EfficientNet, RoBERTa, Fusion)
-- `preprocessing.py` — Data pipeline (images, video frames, text)
-- `inference.py` — Inference API (single/modality, multimodal, video)
-- `train.py` — Training script
+## 🔗 Space de Démo
+[alianassmaaa/multimodal-deepfake-space](https://huggingface.co/spaces/alianassmaaa/multimodal-deepfake-space)
 
-## License
+## 📄 Licence
 Apache-2.0