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COMPLETE_SETUP_GUIDE.md

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+# TRIQA Hugging Face Demo - Complete Setup Guide
+
+## 📋 Overview
+
+This guide documents the complete process of creating and deploying a TRIQA Image Quality Assessment demo on Hugging Face Spaces, including all troubleshooting steps and solutions.
+
+## 🎯 What We Built
+
+- **Web Demo**: Interactive Gradio interface for image quality assessment
+- **Space URL**: https://huggingface.co/spaces/S-Rajesh/triqa-iqa
+- **Features**: Upload images, get quality scores (1-5 scale), sample images, paper links
+- **Architecture**: ConvNeXt-based content-aware and quality-aware feature extraction
+
+## 📁 File Structure
+
+```
+huggingface_demo/
+├── app.py                          # Main Gradio application
+├── README.md                       # Space description (Hugging Face format)
+├── requirements.txt                # Python dependencies
+├── package.json                    # Node.js configuration
+├── convnext_original.py            # Original ConvNeXt model
+├── convnext_finetune.py            # Fine-tuned ConvNeXt model
+├── sample_image/                   # Sample images for testing
+│   ├── 233045618.jpg
+│   ├── 25239707.jpg
+│   ├── 44009500.jpg
+│   ├── 5129172.jpg
+│   └── 85119046.jpg
+├── test_demo.py                    # Local testing script
+├── upload_to_space.py              # Python upload script
+├── upload_with_git.sh              # Git upload script
+├── QUICK_START.md                  # Quick start guide
+├── UPLOAD_METHODS.md               # Upload methods documentation
+├── HUGGINGFACE_SETUP.md            # Detailed setup guide
+└── COMPLETE_SETUP_GUIDE.md         # This file
+```
+
+## 🚀 Step-by-Step Setup Process
+
+### Step 1: Create Hugging Face Space
+
+1. **Go to Hugging Face Spaces**: https://huggingface.co/spaces
+2. **Click "Create new Space"**
+3. **Fill in the details**:
+   - **Space name**: `triqa-iqa` (or your preferred name)
+   - **License**: MIT
+   - **SDK**: Gradio
+   - **Hardware**: CPU (or GPU if available)
+   - **Visibility**: Public
+
+### Step 2: Prepare Files
+
+#### Core Application Files:
+- `app.py` - Main Gradio application
+- `README.md` - Space description (with proper metadata)
+- `requirements.txt` - Python dependencies
+- `package.json` - Node.js configuration
+
+#### Model Architecture Files:
+- `convnext_original.py` - Original ConvNeXt model
+- `convnext_finetune.py` - Fine-tuned ConvNeXt model
+
+#### Sample Images:
+- `sample_image/` directory with 5 sample images
+
+### Step 3: Upload Files
+
+#### Method 1: Python CLI (Used in this project)
+```bash
+# Login to Hugging Face
+huggingface-cli login
+
+# Run upload script
+python upload_to_space.py
+```
+
+#### Method 2: Git (Alternative)
+```bash
+# Clone your space
+git clone https://huggingface.co/spaces/your-username/triqa-iqa
+cd triqa-iqa
+
+# Copy files
+cp ../huggingface_demo/* .
+cp -r ../huggingface_demo/sample_image .
+
+# Commit and push
+git add . && git commit -m "Add TRIQA demo" && git push
+```
+
+#### Method 3: Web Interface
+- Go to your space on Hugging Face
+- Click "Files" tab
+- Drag and drop files
+
+### Step 4: Upload Model Files
+
+**IMPORTANT**: Model files must be uploaded separately due to size:
+
+1. **Download from Box**: https://utexas.box.com/s/8aw6axc2lofouja65uc726lca8b1cduf
+2. **Create directories** in your space:
+   - `feature_models/`
+   - `Regression_Models/`
+3. **Upload model files**:
+   - `feature_models/convnext_tiny_22k_224.pth` (170MB)
+   - `feature_models/triqa_quality_aware.pth` (107MB)
+   - `Regression_Models/KonIQ_scaler.save`
+   - `Regression_Models/KonIQ_TRIQA.save` (111MB)
+
+## 🔧 Troubleshooting & Solutions
+
+### Issue 1: Short Description Too Long
+**Error**: `"short_description" length must be less than or equal to 60 characters long`
+
+**Solution**:
+```yaml
+# In README.md metadata
+short_description: TRIQA-IQA  # 9 characters - under limit
+```
+
+**Original**: `Image Quality Assessment using ConvNeXt features` (78 characters)
+**Fixed**: `TRIQA-IQA` (9 characters)
+
+### Issue 2: Missing timm Module
+**Error**: `ModuleNotFoundError: No module named 'timm'`
+
+**Solution**: Add `timm` to requirements.txt
+```txt
+gradio>=4.0.0
+torch>=1.8.0
+torchvision>=0.9.0
+timm>=0.6.0          # Added this
+pillow>=8.0.0
+numpy>=1.19.0
+scikit-learn>=0.24.0
+```
+
+### Issue 3: Model Files Not Found
+**Error**: `FileNotFoundError: [Errno 2] No such file or directory: 'feature_models/...'`
+
+**Solution**: 
+1. Download model files from Box
+2. Create required directories in space
+3. Upload model files to correct locations
+
+### Issue 4: Build Failures
+**Common Causes**:
+- Missing dependencies in requirements.txt
+- Incorrect file paths
+- Syntax errors in code
+
+**Solution**:
+1. Check space logs for specific errors
+2. Verify all dependencies are listed
+3. Test locally first: `python app.py`
+
+## 📝 Key Files Explained
+
+### app.py
+- **Purpose**: Main Gradio web interface
+- **Key Features**:
+  - Image upload interface
+  - Quality prediction function
+  - Sample images display
+  - Paper links and citation
+  - Error handling for missing models
+
+### README.md
+- **Purpose**: Space description and metadata
+- **Key Sections**:
+  - YAML frontmatter (title, emoji, colors, SDK)
+  - Project description
+  - Usage instructions
+  - Model file requirements
+  - Citation and paper links
+
+### requirements.txt
+- **Purpose**: Python dependencies
+- **Key Dependencies**:
+  - `gradio>=4.0.0` - Web interface
+  - `torch>=1.8.0` - Deep learning framework
+  - `timm>=0.6.0` - Model architectures
+  - `scikit-learn>=0.24.0` - Regression models
+
+### convnext_original.py & convnext_finetune.py
+- **Purpose**: Model architectures
+- **convnext_original.py**: Content-aware features
+- **convnext_finetune.py**: Quality-aware features
+
+## 🎯 Demo Features
+
+### User Interface:
+- **Image Upload**: Drag & drop or click to upload
+- **Quality Assessment**: Click button to get score
+- **Sample Images**: Pre-loaded examples for testing
+- **Results Display**: Quality score (1-5 scale)
+- **Paper Links**: Direct links to research papers
+- **Citation**: Proper BibTeX citation
+
+### Technical Features:
+- **Multi-scale Processing**: Original + half-size images
+- **Hook-based Feature Extraction**: Same as original code
+- **Error Handling**: Graceful handling of missing files
+- **Responsive Design**: Works on desktop and mobile
+
+## 📊 Upload Methods Comparison
+
+| Method | Pros | Cons | Best For |
+|--------|------|------|----------|
+| Python CLI | Automated, handles multiple files | Requires Python setup | Multiple files, automation |
+| Git | Version control, familiar | Manual steps | Developers, version control |
+| Web Interface | Simple, no setup | Manual, one file at a time | Single files, quick uploads |
+
+## 🔍 Testing & Validation
+
+### Local Testing:
+```bash
+# Test file structure
+python test_demo.py
+
+# Test app locally
+python app.py
+```
+
+### Space Testing:
+1. Check build logs for errors
+2. Upload sample image
+3. Verify quality score output
+4. Test all sample images
+
+## 📚 Dependencies & Versions
+
+### Python Dependencies:
+- `gradio>=4.0.0` - Web interface framework
+- `torch>=1.8.0` - PyTorch deep learning
+- `torchvision>=0.9.0` - Computer vision utilities
+- `timm>=0.6.0` - Model architectures
+- `pillow>=8.0.0` - Image processing
+- `numpy>=1.19.0` - Numerical computing
+- `scikit-learn>=0.24.0` - Machine learning
+
+### System Requirements:
+- Python 3.8+
+- CUDA GPU (recommended) or CPU
+- 4GB+ RAM (for model loading)
+
+## 🚨 Common Pitfalls & Solutions
+
+### 1. File Size Limits
+- **Issue**: Model files too large for regular upload
+- **Solution**: Use Git LFS or upload via web interface
+
+### 2. Build Timeouts
+- **Issue**: Space build fails due to timeout
+- **Solution**: Optimize requirements, use CPU hardware
+
+### 3. Memory Issues
+- **Issue**: Out of memory during model loading
+- **Solution**: Use CPU hardware, optimize model loading
+
+### 4. Path Issues
+- **Issue**: File not found errors
+- **Solution**: Check file paths, verify directory structure
+
+## 📈 Performance Optimization
+
+### Model Loading:
+- Load models once at startup
+- Use `torch.no_grad()` for inference
+- Clear GPU cache if needed
+
+### Memory Management:
+- Process images in batches
+- Use appropriate image sizes
+- Monitor memory usage
+
+## 🔄 Maintenance & Updates
+
+### Regular Updates:
+1. Check for dependency updates
+2. Test with new sample images
+3. Update documentation
+4. Monitor space performance
+
+### Version Control:
+- Keep track of model file versions
+- Document changes in commit messages
+- Test thoroughly before updates
+
+## 📞 Support & Resources
+
+### Documentation:
+- This guide: `COMPLETE_SETUP_GUIDE.md`
+- Quick start: `QUICK_START.md`
+- Upload methods: `UPLOAD_METHODS.md`
+
+### External Resources:
+- Hugging Face Spaces: https://huggingface.co/spaces
+- Gradio Documentation: https://gradio.app/docs/
+- PyTorch Documentation: https://pytorch.org/docs/
+
+### Contact:
+- GitHub: https://github.com/rajeshsureddi/triqa
+- Paper: https://arxiv.org/pdf/2507.12687
+
+## ✅ Success Checklist
+
+- [ ] Space created on Hugging Face
+- [ ] All files uploaded successfully
+- [ ] Model files uploaded to correct directories
+- [ ] Build completed without errors
+- [ ] Demo loads and displays interface
+- [ ] Sample images work correctly
+- [ ] Quality prediction works
+- [ ] Paper links are accessible
+- [ ] Citation is properly formatted
+
+## 🎉 Final Result
+
+Your TRIQA demo is now live at: **https://huggingface.co/spaces/S-Rajesh/triqa-iqa**
+
+The demo provides:
+- Interactive image quality assessment
+- Professional web interface
+- Academic paper integration
+- Easy-to-use sample images
+- Proper citation and documentation
+
+This complete setup guide ensures that anyone can recreate and maintain the TRIQA Hugging Face demo successfully! 🚀