Update README.md

README.md

@@ -1,122 +1,133 @@
-# 🎨 CycleGAN Face-Sketch Converter
-
-Convert face photos to sketches and vice versa using CycleGAN.
-
-## 🚀 Deployment on Hugging Face Spaces
-
-### Step 1: Prepare Your Models
-
-After training, run this cell in your notebook:
-
-```python
-# Run this in your training notebook
-export_for_huggingface(checkpoint_epoch=100)  # Replace 100 with your best epoch
-```
-
-This creates two files in `deployment_models/`:
-- `photo_to_sketch.pth` (~44 MB)
-- `sketch_to_photo.pth` (~44 MB)
-
-### Step 2: Create Hugging Face Space
-
-1. Go to [Hugging Face Spaces](https://huggingface.co/spaces)
-2. Click **"Create new Space"**
-3. Configure:
-   - **Space name**: `cyclegan-face-sketch`
-   - **License**: Choose appropriate license
-   - **Select SDK**: **Streamlit**
-   - **Space hardware**: CPU Basic (free) or GPU for faster inference
-
-### Step 3: Upload Files
-
-Upload these files to your Space:
-
-```
-your-space/
-├── app.py                    # Streamlit application
-├── requirements.txt          # Python dependencies
-├── photo_to_sketch.pth       # Model file (Photo → Sketch)
-├── sketch_to_photo.pth       # Model file (Sketch → Photo)
-└── README.md                 # This file (optional)
-```
-
-### Step 4: Your Space will Auto-Build
-
-Hugging Face will automatically:
-1. Install dependencies from `requirements.txt`
-2. Run your Streamlit app
-3. Provide a public URL
-
-## 📦 File Sizes
-
-- `photo_to_sketch.pth`: ~44 MB
-- `sketch_to_photo.pth`: ~44 MB
-- **Total**: ~88 MB (well within free tier limits)
-
-## 🎯 Features
-
-- ✅ Photo to Sketch conversion
-- ✅ Sketch to Photo conversion  
-- ✅ Auto-detection of input type
-- ✅ Camera input support
-- ✅ Download results
-- ✅ Responsive UI
-
-## 💡 Usage Tips
-
-### For Best Results:
-- Use clear, front-facing photos
-- Ensure good lighting
-- Images are automatically resized to 256x256
-
-### Hardware:
-- **CPU Basic (Free)**: ~3-5 seconds per image
-- **GPU T4 (Paid)**: ~0.5 seconds per image
-
-## 🔧 Local Testing
-
-Before deploying, test locally:
-
-```bash
-# Install dependencies
-pip install -r requirements.txt
-
-# Run Streamlit app
-streamlit run app.py
-```
-
-Then open http://localhost:8501
-
-## 📊 Model Details
-
-- **Architecture**: CycleGAN with ResNet-based generators
-- **Input Size**: 256x256 RGB images
-- **Training**: Unpaired face-sketch dataset
-- **Loss Functions**: 
-  - Adversarial loss (LSGAN)
-  - Cycle consistency loss
-  - Identity loss
-
-## 🐛 Troubleshooting
-
-### Space doesn't start:
-- Check all files are uploaded correctly
-- Verify file names match exactly in `app.py`
-- Check Space logs for errors
-
-### Out of memory:
-- Use CPU Basic hardware
-- Models are optimized for CPU inference
-
-### Slow inference:
-- Upgrade to GPU hardware in Space settings
-- Or use batch processing
-
-## 📝 License
-
-[Your License Here]
-
-## 🙏 Acknowledgments
-
-- CycleGAN paper: [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/abs/1703.10593)
+---
+title: CycleGAN Face-Sketch Converter
+emoji: 🎨
+colorFrom: blue
+colorTo: purple
+sdk: streamlit
+sdk_version: "1.28.0"
+app_file: app.py
+pinned: false
+---
+
+# 🎨 CycleGAN Face-Sketch Converter
+
+Convert face photos to sketches and vice versa using CycleGAN.
+
+## 🚀 Deployment on Hugging Face Spaces
+
+### Step 1: Prepare Your Models
+
+After training, run this cell in your notebook:
+
+```python
+# Run this in your training notebook
+export_for_huggingface(checkpoint_epoch=100)  # Replace 100 with your best epoch
+```
+
+This creates two files in `deployment_models/`:
+- `photo_to_sketch.pth` (~44 MB)
+- `sketch_to_photo.pth` (~44 MB)
+
+### Step 2: Create Hugging Face Space
+
+1. Go to [Hugging Face Spaces](https://huggingface.co/spaces)
+2. Click **"Create new Space"**
+3. Configure:
+   - **Space name**: `cyclegan-face-sketch`
+   - **License**: Choose appropriate license
+   - **Select SDK**: **Streamlit**
+   - **Space hardware**: CPU Basic (free) or GPU for faster inference
+
+### Step 3: Upload Files
+
+Upload these files to your Space:
+
+```
+your-space/
+├── app.py                    # Streamlit application
+├── requirements.txt          # Python dependencies
+├── photo_to_sketch.pth       # Model file (Photo → Sketch)
+├── sketch_to_photo.pth       # Model file (Sketch → Photo)
+└── README.md                 # This file (optional)
+```
+
+### Step 4: Your Space will Auto-Build
+
+Hugging Face will automatically:
+1. Install dependencies from `requirements.txt`
+2. Run your Streamlit app
+3. Provide a public URL
+
+## 📦 File Sizes
+
+- `photo_to_sketch.pth`: ~44 MB
+- `sketch_to_photo.pth`: ~44 MB
+- **Total**: ~88 MB (well within free tier limits)
+
+## 🎯 Features
+
+- ✅ Photo to Sketch conversion
+- ✅ Sketch to Photo conversion  
+- ✅ Auto-detection of input type
+- ✅ Camera input support
+- ✅ Download results
+- ✅ Responsive UI
+
+## 💡 Usage Tips
+
+### For Best Results:
+- Use clear, front-facing photos
+- Ensure good lighting
+- Images are automatically resized to 256x256
+
+### Hardware:
+- **CPU Basic (Free)**: ~3-5 seconds per image
+- **GPU T4 (Paid)**: ~0.5 seconds per image
+
+## 🔧 Local Testing
+
+Before deploying, test locally:
+
+```bash
+# Install dependencies
+pip install -r requirements.txt
+
+# Run Streamlit app
+streamlit run app.py
+```
+
+Then open http://localhost:8501
+
+## 📊 Model Details
+
+- **Architecture**: CycleGAN with ResNet-based generators
+- **Input Size**: 256x256 RGB images
+- **Training**: Unpaired face-sketch dataset
+- **Loss Functions**: 
+  - Adversarial loss (LSGAN)
+  - Cycle consistency loss
+  - Identity loss
+
+## 🐛 Troubleshooting
+
+### Space doesn't start:
+- Check all files are uploaded correctly
+- Verify file names match exactly in `app.py`
+- Check Space logs for errors
+
+### Out of memory:
+- Use CPU Basic hardware
+- Models are optimized for CPU inference
+
+### Slow inference:
+- Upgrade to GPU hardware in Space settings
+- Or use batch processing
+
+## 📝 License
+
+[Your License Here]
+
+## 🙏 Acknowledgments
+
+- CycleGAN paper: [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/abs/1703.10593)
 - Person Face Sketches dataset: [Kaggle](https://www.kaggle.com/datasets/almightyj/person-face-sketches)