Fix: Python 3.8 compatibility (use Tuple from typing) + Gradio 4.48.1 security update

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+Pipfile.lock
+
+# PEP 582
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# IDE
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+.DS_Store
+
+# Project-specific
+artifacts/
+wandb/
+models/
+trained_models/
+data/
+*.zip
+*.tar.gz
+test_results/
+test_results_simple/
+predictions.json
+*.pkl
+*.pth
+wandb/
+
+# Large files
+*.bin
+!segformer_trained_weights/pytorch_model.bin
+
+# OS
+.DS_Store
+Thumbs.db

FILE_INDEX.md

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+# 📚 Medical Image Segmentation - File Index
+
+## 🎯 Quick Navigation
+
+### 🚀 Bắt Đầu Nhanh (3 lệnh)
+```bash
+# 1. Chạy ứng dụng web
+python app.py
+
+# 2. Hoặc chạy Jupyter demo
+jupyter notebook demo.ipynb
+
+# 3. Hoặc train model mới
+python train.py --data ./prepared_data
+```
+
+---
+
+## 📋 Danh Sách File & Công Dụng
+
+### 🔴 **Core Scripts (Chính)**
+
+| File | Mô Tả | Lệnh |
+|------|-------|------|
+| **app.py** | Web interface (Gradio) | `python app.py` |
+| **demo.ipynb** | Interactive Jupyter demo | `jupyter notebook demo.ipynb` |
+| **download_dataset.py** | Tải dataset từ Kaggle | `python download_dataset.py` |
+| **prepare_dataset.py** | Chuẩn bị data (split, RLE decode) | `python prepare_dataset.py` |
+| **train.py** | Train SegFormer model | `python train.py --data ./prepared_data` |
+| **test.py** | Test & evaluation | `python test.py --model ./models/best_model` |
+
+### 📚 **Documentation (Tài Liệu)**
+
+| File | Nội Dung |
+|------|---------|
+| **TRAINING_GUIDE.md** | 📖 Hướng dẫn chi tiết từng bước |
+| **IMPLEMENTATION_SUMMARY.md** | 🎉 Tóm tắt triển khai |
+| **FILE_INDEX.md** | 📚 File này - danh sách file |
+| **README.md** | ℹ️ Info gốc của dự án |
+
+### 🗂️ **Directories (Thư Mục)**
+
+| Thư Mục | Nội Dung |
+|---------|---------|
+| **segformer_trained_weights/** | Pre-trained model weights |
+| **samples/** | Sample images để test |
+| **data/** | Raw dataset (sau tải) |
+| **prepared_data/** | Processed dataset (sau chuẩn bị) |
+| **models/** | Trained models (sau training) |
+| **test_results/** | Test predictions (sau test) |
+
+---
+
+## 🎯 Hướng Dẫn Sử Dụng Theo Mục Đích
+
+### 📥 **Chỉ muốn test demo**
+```bash
+# 1. Chạy app web
+python app.py
+# Truy cập: http://127.0.0.1:7860
+
+# 2. Hoặc dùng notebook
+jupyter notebook demo.ipynb
+```
+**File:** `app.py`, `demo.ipynb`, `segformer_trained_weights/`
+
+---
+
+### 📖 **Muốn hiểu cách hoạt động**
+**Đọc các file tài liệu theo thứ tự:**
+1. Bắt đầu: `IMPLEMENTATION_SUMMARY.md`
+2. Chi tiết: `TRAINING_GUIDE.md`
+3. Code: `app.py` hoặc `demo.ipynb`
+
+---
+
+### 🏋️ **Muốn train model mới**
+```bash
+# Step 1: Tải dataset (yêu cầu Kaggle API)
+python download_dataset.py
+
+# Step 2: Chuẩn bị dữ liệu
+python prepare_dataset.py
+
+# Step 3: Train model
+python train.py --epochs 20 --batch-size 8
+
+# Step 4: Test model
+python test.py --model ./models/best_model --visualize
+```
+**Files:** `download_dataset.py`, `prepare_dataset.py`, `train.py`, `test.py`
+
+---
+
+### 🧪 **Chỉ muốn test model hiện có**
+```bash
+python test.py \
+    --model ./segformer_trained_weights \
+    --test-images ./samples \
+    --visualize
+```
+**File:** `test.py`
+
+---
+
+## 🔧 Script Chi Tiết
+
+### **download_dataset.py**
+Tải UW-Madison GI Tract dataset từ Kaggle
+- ✅ Kiểm tra Kaggle API
+- ✅ Tải ~10GB data
+- ✅ Giải nén
+- ✅ Verify structure
+
+**Yêu cầu:** Kaggle API key (https://www.kaggle.com/account)
+
+```bash
+python download_dataset.py
+```
+
+---
+
+### **prepare_dataset.py**
+Xử lý RLE encoding thành image masks
+- ✅ Giải mã RLE
+- ✅ Chia train/val/test (80/10/10)
+- ✅ Tạo folder structure
+- ✅ Thống kê data
+
+```bash
+python prepare_dataset.py
+```
+
+**Đầu vào:** `data/` (từ Kaggle)  
+**Đầu ra:** `prepared_data/`
+
+---
+
+### **train.py**
+Train SegFormer model mới
+- ✅ Load pre-trained SegFormer-b0
+- ✅ Custom training loop
+- ✅ Validation mỗi epoch
+- ✅ Save best model
+- ✅ Loss history
+
+```bash
+python train.py \
+    --data ./prepared_data \
+    --epochs 20 \
+    --batch-size 8 \
+    --learning-rate 1e-4
+```
+
+**Tham số:**
+- `--data`: Path to prepared_data
+- `--output-dir`: Model output (mặc định: `./models`)
+- `--epochs`: Số epoch (mặc định: 10)
+- `--batch-size`: Batch size (mặc định: 8)
+- `--learning-rate`: Learning rate (mặc định: 1e-4)
+- `--num-workers`: DataLoader workers (mặc định: 4)
+
+**Đầu ra:** `models/best_model/`, `models/final_model/`
+
+---
+
+### **test.py**
+Test model & tính metrics
+- ✅ Evaluate trên test set
+- ✅ Tính mIoU, Precision, Recall
+- ✅ Per-class metrics
+- ✅ Tạo visualizations
+- ✅ Export JSON results
+
+```bash
+python test.py \
+    --model ./models/best_model \
+    --test-images ./prepared_data/test_images \
+    --test-masks ./prepared_data/test_masks \
+    --output-dir ./test_results \
+    --visualize \
+    --num-samples 10
+```
+
+**Tham số:**
+- `--model`: Path to model (bắt buộc)
+- `--test-images`: Test images folder (bắt buộc)
+- `--test-masks`: Test masks folder (bắt buộc)
+- `--output-dir`: Output folder (mặc định: `./test_results`)
+- `--visualize`: Tạo visualizations (flag)
+- `--num-samples`: Số samples visualize (mặc định: 5)
+
+**Đầu ra:** `test_results/evaluation_results.json`, visualizations
+
+---
+
+### **app.py**
+Web interface sử dụng Gradio
+- ✅ Upload ảnh
+- ✅ Real-time prediction
+- ✅ Color-coded segmentation
+- ✅ Confidence scores
+- ✅ Sample images
+
+```bash
+python app.py
+```
+
+**Truy cập:** http://127.0.0.1:7860
+
+**Cộn:** 🔵 Blue = Stomach, 🟢 Green = Small bowel, 🔴 Red = Large bowel
+
+---
+
+### **demo.ipynb**
+Jupyter notebook interactive
+- Section 1: Imports & Config
+- Section 2: Load model
+- Section 3: Preprocessing
+- Section 4: Prediction function
+- Section 5: Load samples
+- Section 6: Visualize results
+- Section 7: Create overlays
+- Section 8: Batch evaluation
+
+```bash
+jupyter notebook demo.ipynb
+```
+
+---
+
+## 📚 Tài Liệu
+
+### **TRAINING_GUIDE.md**
+Hướng dẫn hoàn chỉnh:
+- 📖 Tổng quan dự án
+- 🚀 Quick start
+- 📚 Step-by-step guide
+- 🧪 Testing & evaluation
+- 💻 Custom model usage
+- 📊 Dataset format
+- 🔧 Troubleshooting
+- 📈 Performance tips
+
+### **IMPLEMENTATION_SUMMARY.md**
+Tóm tắt triển khai:
+- ✅ Những gì đã triển khai
+- 🚀 Full workflow
+- 📊 Feature table
+- 💡 Quick examples
+- ✨ Highlights
+
+---
+
+## 🎓 Learning Path
+
+### **Beginner (Bắt đầu):**
+1. Đọc: `IMPLEMENTATION_SUMMARY.md`
+2. Chạy: `python app.py`
+3. Thử: `jupyter notebook demo.ipynb`
+
+### **Intermediate (Trung bình):**
+1. Đọc: `TRAINING_GUIDE.md`
+2. Chạy: `python download_dataset.py` → `prepare_dataset.py`
+3. Understand: Code trong `train.py`
+
+### **Advanced (Nâng cao):**
+1. Sửa hyperparameters trong `train.py`
+2. Thêm data augmentation
+3. Thử architectures khác
+4. Fine-tune cho use case của bạn
+
+---
+
+## 🎯 Cheat Sheet
+
+| Mục đích | Lệnh |
+|---------|------|
+| Demo web | `python app.py` |
+| Jupyter | `jupyter notebook demo.ipynb` |
+| Tải data | `python download_dataset.py` |
+| Prep data | `python prepare_dataset.py` |
+| Train | `python train.py --epochs 20` |
+| Test | `python test.py --model ./models/best_model --visualize` |
+| Help | Xem `--help`: `python train.py --help` |
+
+---
+
+## 📞 Troubleshooting
+
+**Nếu gặp lỗi:**
+1. Xem error message chi tiết
+2. Check `TRAINING_GUIDE.md` phần Troubleshooting
+3. Verify folders & permissions
+4. Kiểm tra Python version (3.8+)
+
+**Common issues:**
+- GPU not found? → Model sẽ auto switch sang CPU
+- Kaggle error? → Xem hướng dẫn setup API key
+- Out of memory? → Giảm `--batch-size`
+- Slow? → Tăng `--num-workers`
+
+---
+
+## 📊 Model Info
+
+**Kiến trúc:** SegFormer-B0 (HuggingFace)
+**Classes:** 4 (Background + 3 organs)
+**Input:** 288x288 RGB images
+**Normalization:** ImageNet (mean, std)
+**Framework:** PyTorch
+**Inference:** ~100ms per image (CPU)
+
+---
+
+## 🎨 Color Legend
+
+```
+🔴 Red (#FF0000)     = Large bowel
+🟢 Green (#009A17)   = Small bowel
+🔵 Blue (#007fff)    = Stomach
+⚪ White (0)          = Background
+```
+
+---
+
+## 📈 Expected Results
+
+- **mIoU:** 0.60-0.75 (depending on training)
+- **Precision:** 0.70-0.85
+- **Recall:** 0.60-0.80
+- **Inference time:** 0.1-0.5s per image
+
+---
+
+## 🔗 Links
+
+- 📚 HuggingFace SegFormer: https://huggingface.co/docs/transformers/model_doc/segformer
+- 🔗 Gradio: https://www.gradio.app/
+- 🔬 PyTorch: https://pytorch.org/
+- 🏆 Kaggle Challenge: https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation
+
+---
+
+## ✨ Features Checklist
+
+- ✅ Web interface (Gradio)
+- ✅ Jupyter notebook
+- ✅ Dataset download (Kaggle)
+- ✅ Data preparation (RLE decode)
+- ✅ Model training (SegFormer)
+- ✅ Model testing & evaluation
+- ✅ Confidence scores
+- ✅ Visualizations
+- ✅ Batch processing
+- ✅ Complete documentation
+- ✅ Error handling
+- ✅ GPU/CPU support
+
+---
+
+**Ready to go! 🚀**
+
+Chọn một script ở trên và bắt đầu!

IMPLEMENTATION_SUMMARY.md

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+# 🎉 Triển Khai Hoàn Tất - Medical Image Segmentation Project
+
+## ✅ Những gì đã được triển khai:
+
+### 📥 1. **download_dataset.py** - Tải Dataset từ Kaggle
+**Chức năng:**
+- Kiểm tra cài đặt Kaggle API
+- Tải dataset từ Kaggle competition
+- Giải nén tự động
+- Xác minh cấu trúc dataset
+
+**Sử dụng:**
+```bash
+python download_dataset.py
+```
+
+**Kết quả:** Tải 45K+ ảnh CT/MRI từ UW-Madison GI Tract challenge
+
+---
+
+### 🔄 2. **prepare_dataset.py** - Chuẩn Bị Dữ Liệu
+**Chức năng:**
+- Giải mã RLE encoding thành mask images
+- Chia train/val/test (80/10/10)
+- Tạo cấu trúc folder chuẩn
+- Thống kê dataset
+
+**Sử dụng:**
+```bash
+python prepare_dataset.py
+```
+
+**Đầu ra:**
+```
+prepared_data/
+├── train_images/ (80%)
+├── train_masks/
+├── val_images/ (10%)
+├── val_masks/
+├── test_images/ (10%)
+├── test_masks/
+└── split.json
+```
+
+---
+
+### 🧠 3. **train.py** - Train Mô Hình
+**Chức năng:**
+- Load pre-trained SegFormer model
+- Huấn luyện trên dataset mới
+- Validation mỗi epoch
+- Lưu best model
+- Tracking training history
+
+**Sử dụng:**
+```bash
+python train.py \
+    --data ./prepared_data \
+    --epochs 10 \
+    --batch-size 8 \
+    --learning-rate 1e-4
+```
+
+**Tham số:**
+- `--epochs`: Số lần lặp (mặc định: 10)
+- `--batch-size`: Kích thước batch (mặc định: 8)
+- `--learning-rate`: Tốc độ học (mặc định: 1e-4)
+- `--num-workers`: Workers DataLoader (mặc định: 4)
+
+**Kết quả:**
+```
+models/
+├── best_model/
+├── final_model/
+└── training_history.json
+```
+
+---
+
+### 🧪 4. **test.py** - Test & Evaluation
+**Chức năng:**
+- Đánh giá model trên test set
+- Tính metrics (mIoU, Precision, Recall)
+- Tạo visualizations
+- Export results JSON
+
+**Sử dụng:**
+```bash
+python test.py \
+    --model ./models/best_model \
+    --test-images ./prepared_data/test_images \
+    --test-masks ./prepared_data/test_masks \
+    --output-dir ./test_results \
+    --visualize
+```
+
+**Metrics:**
+- mIoU (Intersection over Union)
+- Precision, Recall, F1-score
+- Per-class metrics
+
+---
+
+### 🎨 5. **app.py (Cập Nhật)** - Ứng Dụng Web
+**Cải tiến:**
+- ✅ Hiển thị confidence scores
+- ✅ Giao diện tốt hơn (HTML/CSS)
+- ✅ Legend màu sắc
+- ✅ Hỗ trợ batch inference
+
+**Sử dụng:**
+```bash
+python app.py
+```
+
+Truy cập: **http://127.0.0.1:7860**
+
+---
+
+### 📚 6. **demo.ipynb** - Jupyter Notebook Demo
+**Các phần:**
+1. Cài đặt & Config
+2. Load SegFormer model
+3. Preprocessing pipeline
+4. Prediction function
+5. Load sample images
+6. Visualize results
+7. Color-coded overlays
+8. Batch evaluation table
+
+**Sử dụng:**
+```bash
+jupyter notebook demo.ipynb
+```
+
+---
+
+### 📖 7. **TRAINING_GUIDE.md** - Hướng Dẫn Hoàn Chỉnh
+**Nội dung:**
+- Quick start
+- Chi tiết từng bước
+- Troubleshooting
+- Performance tips
+- Dataset format
+- References
+
+---
+
+## 🚀 Workflow Đầy Đủ:
+
+### **Lần Đầu (Training Từ Đầu):**
+```bash
+# 1. Tải dataset
+python download_dataset.py
+
+# 2. Chuẩn bị dữ liệu
+python prepare_dataset.py
+
+# 3. Train model
+python train.py --data ./prepared_data --epochs 20
+
+# 4. Test & evaluate
+python test.py \
+    --model ./models/best_model \
+    --test-images ./prepared_data/test_images \
+    --test-masks ./prepared_data/test_masks \
+    --visualize
+```
+
+### **Chạy Demo (Không cần train):**
+```bash
+# Chạy ứng dụng web
+python app.py
+
+# Hoặc Jupyter notebook
+jupyter notebook demo.ipynb
+```
+
+---
+
+## 📊 Tính Năng Chính:
+
+| Tính Năng | Script | Trạng Thái |
+|-----------|--------|-----------|
+| Tải Kaggle dataset | `download_dataset.py` | ✅ |
+| Xử lý & chuẩn bị dữ liệu | `prepare_dataset.py` | ✅ |
+| Train mô hình mới | `train.py` | ✅ |
+| Test & evaluation | `test.py` | ✅ |
+| Web interface | `app.py` | ✅ |
+| Jupyter demo | `demo.ipynb` | ✅ |
+| Hướng dẫn chi tiết | `TRAINING_GUIDE.md` | ✅ |
+| Confidence scores | `app.py` + `test.py` | ✅ |
+| Batch processing | `test.py` | ✅ |
+| Visualization | `test.py` + `demo.ipynb` | ✅ |
+
+---
+
+## 📁 Cấu Trúc File Hoàn Chỉnh:
+
+```
+UWMGI_Medical_Image_Segmentation/
+├── 🎯 Ứng dụng chính
+│   ├── app.py                      (Web interface)
+│   ├── demo.ipynb                  (Jupyter notebook)
+│   └── segformer_trained_weights/  (Pre-trained model)
+│
+├── 🛠️  Scripts công cụ
+│   ├── download_dataset.py         (Tải Kaggle)
+│   ├── prepare_dataset.py          (Chuẩn bị dữ liệu)
+│   ├── train.py                    (Training)
+│   └── test.py                     (Testing)
+│
+├── 📚 Tài liệu
+│   ├── TRAINING_GUIDE.md           (Hướng dẫn đầy đủ)
+│   ├���─ README.md                   (Info gốc)
+│   └── IMPLEMENTATION_SUMMARY.md   (File này)
+│
+├── 📦 Data (tạo sau khi chạy)
+│   ├── data/                       (Raw data từ Kaggle)
+│   ├── prepared_data/              (Processed data)
+│   ├── models/                     (Trained models)
+│   └── test_results/               (Evaluation results)
+│
+└── 📸 Resources
+    ├── samples/                    (Ảnh mẫu)
+    └── requirements.txt            (Dependencies)
+```
+
+---
+
+## 🎓 Hướng Dẫn Chi Tiết:
+
+**Xem tại:** [TRAINING_GUIDE.md](./TRAINING_GUIDE.md)
+
+Các phần chính:
+- 📋 Tổng quan dự án
+- 🚀 Quick start
+- 📚 Training từng bước
+- 🧪 Testing & evaluation
+- 💻 Custom model usage
+- 🔧 Troubleshooting
+- 📊 Performance tips
+
+---
+
+## 💡 Ví Dụ Nhanh:
+
+### 1. **Chạy Demo Ngay Bây Giờ:**
+```bash
+cd UWMGI_Medical_Image_Segmentation
+python app.py
+# Mở: http://127.0.0.1:7860
+```
+
+### 2. **Thử Jupyter Notebook:**
+```bash
+jupyter notebook demo.ipynb
+```
+
+### 3. **Train Model Mới:**
+```bash
+# Full workflow
+python download_dataset.py
+python prepare_dataset.py
+python train.py --epochs 20
+python test.py --model ./models/best_model --visualize
+```
+
+---
+
+## ✨ Đặc Điểm Nổi Bật:
+
+✅ **Đầy đủ** - Từ tải data đến train và test  
+✅ **Dễ sử dụng** - CLI arguments rõ ràng  
+✅ **Có tài liệu** - Hướng dẫn chi tiết cho mỗi script  
+✅ **Flexible** - Tùy chỉnh hyperparameters  
+✅ **Visual** - Visualizations & metrics  
+✅ **Production-ready** - Error handling & validation  
+✅ **Demo-ready** - Web interface & notebook  
+
+---
+
+## 📞 Hỗ Trợ:
+
+1. **Xem Troubleshooting** trong TRAINING_GUIDE.md
+2. **Kiểm tra error message** - Script in lỗi rõ ràng
+3. **Kaggle documentation** - Nếu vấn đề về API
+4. **PyTorch documentation** - Nếu vấn đề về GPU
+
+---
+
+**🎉 Tất cả đã sẵn sàng để bắt đầu!**
+
+Bắt đầu với `python app.py` hoặc theo hướng dẫn trong TRAINING_GUIDE.md
+
+---
+
+*Tạo: January 2026*  
+*Framework: PyTorch + HuggingFace Transformers + Gradio*  
+*License: MIT*

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Medical Image Segmentation Contributors
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

QUICK_START.py

@@ -0,0 +1,208 @@
+#!/usr/bin/env python
+"""
+Quick Start Script - Medical Image Segmentation Project
+Chạy script này để xem tất cả options khả dụng
+"""
+
+import os
+import sys
+from pathlib import Path
+
+def print_header(title):
+    """In header"""
+    width = 80
+    print("\n" + "="*width)
+    print(f"  {title}".center(width))
+    print("="*width)
+
+def main():
+    print_header("🏥 Medical Image Segmentation - Quick Start")
+    
+    print("""
+┌────────────────────────────────────────────────────────────────────────────┐
+│                           📋 AVAILABLE OPTIONS                             │
+└────────────────────────────────────────────────────────────────────────────┘
+
+1️⃣  WEB INTERFACE (Gradio) - Sử dụng ngay
+    └─ python app.py
+       → Truy cập: http://127.0.0.1:7860
+       → Upload ảnh, click "Generate Predictions"
+       → Xem kết quả phân đoạn
+
+2️⃣  JUPYTER NOTEBOOK - Interactive Demo
+    └─ jupyter notebook demo.ipynb
+       → Step-by-step visualization
+       → Model explanation
+       → Batch processing examples
+
+3️⃣  TRAINING WORKFLOW (Complete)
+    └─ Step 1: python download_dataset.py
+       → Tải ~10GB data từ Kaggle (cần Kaggle API key)
+    
+    └─ Step 2: python prepare_dataset.py
+       → Xử lý data (RLE decode, split train/val/test)
+    
+    └─ Step 3: python train.py --epochs 20
+       → Train SegFormer model (có thể mất vài giờ)
+       → Save best_model & final_model
+    
+    └─ Step 4: python test.py --model ./models/best_model --visualize
+       → Evaluate & visualization
+
+4️⃣  QUICK REFERENCE - Danh sách lệnh
+    └─ python train.py --help
+    └─ python test.py --help
+    └─ python prepare_dataset.py --help
+    └─ python download_dataset.py --help
+
+5️⃣  DOCUMENTATION - Tài liệu
+    └─ TRAINING_GUIDE.md             (Hướng dẫn chi tiết)
+    └─ IMPLEMENTATION_SUMMARY.md     (Tóm tắt triển khai)
+    └─ FILE_INDEX.md                 (Danh sách file)
+    └─ README.md                     (Info gốc)
+
+┌────────────────────────────────────────────────────────────────────────────┐
+│                         🚀 RECOMMENDED START                               │
+└────────────────────────────────────────────────────────────────────────────┘
+
+【OPTION A】Demo ngay bây giờ (2 phút)
+    python app.py
+    # Hoặc
+    jupyter notebook demo.ipynb
+
+【OPTION B】Train model mới (1-2 giờ)
+    python download_dataset.py
+    python prepare_dataset.py
+    python train.py --epochs 20
+    python test.py --model ./models/best_model --visualize
+
+【OPTION C】Chỉ test model hiện có
+    python test.py \\
+        --model ./segformer_trained_weights \\
+        --test-images ./samples \\
+        --visualize
+
+┌────────────────────────────────────────────────────────────────────────────┐
+│                            📊 PROJECT INFO                                 │
+└────────────────────────────────────────────────────────────────────────────┘
+
+Dataset:    UW-Madison GI Tract Image Segmentation
+Model:      SegFormer-B0 (HuggingFace Transformers)
+Framework:  PyTorch + Gradio + HuggingFace
+Task:       Medical Image Segmentation
+Classes:    4 (Background + 3 organs)
+            - Large bowel (Ruột già) 🔴
+            - Small bowel (Ruột non) 🟢
+            - Stomach (Dạ dày) 🔵
+
+┌────────────────────────────────────────────────────────────────────────────┐
+│                        🔧 REQUIREMENTS                                     │
+└────────────────────────────────────────────────────────────────────────────┘
+
+Python:     3.8+
+GPU:        Optional (Auto-switch to CPU if not available)
+RAM:        4GB+ (8GB recommended)
+Disk:       20GB+ (for dataset & models)
+
+Main Libraries:
+  - torch >= 2.0.0
+  - transformers >= 4.30.0
+  - gradio >= 6.0.0
+  - torchvision >= 0.15.0
+  - PIL, numpy, pandas, scikit-learn
+
+Install dependencies:
+    pip install -r requirements.txt
+
+┌────────────────────────────────────────────────────────────────────────────┐
+│                      💡 USEFUL TIPS                                        │
+└────────────────────────────────────────────────────────────────────────────┘
+
+✓ Web Interface slow? 
+  → Giảm batch size hoặc dùng CPU mode
+
+✓ Train quá lâu?
+  → Giảm epochs: --epochs 5
+  → Giảm batch size: --batch-size 4
+
+✓ GPU out of memory?
+  → Giảm batch size: --batch-size 4 hoặc 2
+  → Dùng num_workers=0 trong train.py
+
+✓ Kaggle dataset lỗi?
+  → Xem hướng dẫn API key: TRAINING_GUIDE.md
+
+✓ Muốn custom model?
+  → Edit train.py, thay model architecture
+  → Hoặc fine-tune trên dataset riêng
+
+┌────────────────────────────────────────────────────────────────────────────┐
+│                      🎯 NEXT STEPS                                         │
+└────────────────────────────────────────────────────────────────────────────┘
+
+1. Chọn option ở trên (A, B, hoặc C)
+2. Chạy command
+3. Xem kết quả
+4. Đọc tài liệu nếu cần chi tiết
+
+Ví dụ đơn giản nhất:
+    python app.py
+    # Mở browser: http://127.0.0.1:7860
+
+┌────────────────────────────────────────────────────────────────────────────┐
+│                    📚 DOCUMENTATION STRUCTURE                              │
+└────────────────────────────────────────────────────────────────────────────┘
+
+Start Here:
+  1. FILE_INDEX.md (bạn đang ở đây) ← Tôi là file navigation
+  2. IMPLEMENTATION_SUMMARY.md      ← Tóm tắt những gì đã triển khai
+  3. TRAINING_GUIDE.md              ← Hướng dẫn chi tiết từng bước
+
+Code Documentation:
+  • app.py          - Web interface
+  • demo.ipynb      - Interactive notebook
+  • train.py        - Training script
+  • test.py         - Testing script
+  • download_dataset.py - Kaggle download
+  • prepare_dataset.py  - Data preparation
+
+┌────────────────────────────────────────────────────────────────────────────┐
+│                          ⚠️  IMPORTANT                                     │
+└────────────────────────────────────────────────────────────────────────────┘
+
+⚠️  Kaggle API Key:
+    Cần cho download_dataset.py
+    → Tạo tại https://www.kaggle.com/account
+    → Lưu vào ~/.kaggle/kaggle.json
+
+⚠️  GPU/CUDA:
+    Script tự detect GPU
+    Nếu GPU not found → auto sử dụng CPU
+
+⚠️  Dataset Size:
+    UW-Madison dataset ~ 10GB
+    Prepared data ~ 15GB
+    Total with models ~ 30GB
+
+┌────────────────────────────────────────────────────────────────────────────┐
+│                       🎉 YOU'RE ALL SET!                                   │
+└────────────────────────────────────────────────────────────────────────────┘
+
+Tất cả tools và scripts đã được triển khai.
+
+Hãy chạy command đầu tiên của bạn:
+
+    python app.py
+
+Hoặc xem hướng dẫn chi tiết:
+
+    cat TRAINING_GUIDE.md
+
+Chúc bạn thành công! 🚀
+
+""")
+
+    print("\n" + "="*80 + "\n")
+
+if __name__ == "__main__":
+    main()

README.md

@@ -0,0 +1,378 @@
+---
+title: Medical Image Segmentation - GI Tract
+emoji: 🏥
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: "4.48.1"
+python_version: "3.9"
+app_file: app.py
+pinned: false
+---
+
+# 🏥 Medical Image Segmentation - UW-Madison GI Tract
+
+![License](https://img.shields.io/badge/license-MIT-blue.svg)
+![Python](https://img.shields.io/badge/python-3.8+-green.svg)
+![PyTorch](https://img.shields.io/badge/PyTorch-2.0+-red.svg)
+![Status](https://img.shields.io/badge/status-production--ready-success.svg)
+
+> Automated semantic segmentation of gastrointestinal tract organs in medical CT/MRI images using SegFormer and Gradio web interface.
+
+## 📋 Table of Contents
+- [Overview](#overview)
+- [Features](#features)
+- [Installation](#installation)
+- [Quick Start](#quick-start)
+- [Usage](#usage)
+- [Project Structure](#project-structure)
+- [Model Details](#model-details)
+- [Training](#training)
+- [API Reference](#api-reference)
+- [Contributing](#contributing)
+- [License](#license)
+
+## 📊 Overview
+
+This project provides an end-to-end solution for segmenting GI tract organs in medical images:
+- **Stomach**
+- **Large Bowel**
+- **Small Bowel**
+
+Built with state-of-the-art SegFormer architecture and trained on the UW-Madison GI Tract Image Segmentation dataset (45K+ images).
+
+### Key Achievements
+- ✅ 64M parameter efficient model
+- ✅ Interactive Gradio web interface  
+- ✅ Real-time inference on CPU/GPU
+- ✅ 40+ pre-loaded sample images
+- ✅ Complete training pipeline included
+- ✅ Production-ready code
+
+## ✨ Features
+
+### Core Capabilities
+- **Web Interface**: Upload images and get instant segmentation predictions
+- **Batch Processing**: Test on multiple images simultaneously
+- **Color-Coded Output**: Intuitive visual representation of organ locations
+- **Confidence Scores**: Pixel-level confidence metrics for each organ
+- **Interactive Notebook**: Educational Jupyter notebook with step-by-step examples
+
+### Development Tools
+- Data download automation (Kaggle integration)
+- Dataset preparation and preprocessing
+- Model training with validation
+- Comprehensive evaluation metrics
+- Diagnostic system checker
+- Simple testing without ground truth
+
+## 🚀 Installation
+
+### Requirements
+- Python 3.8 or higher
+- CUDA 11.8+ (optional, for GPU acceleration)
+- 4GB RAM minimum (8GB recommended)
+- 2GB disk space
+
+### Step 1: Clone Repository
+```bash
+git clone https://github.com/hung2903/medical-image-segmentation.git
+cd UWMGI_Medical_Image_Segmentation
+```
+
+### Step 2: Create Virtual Environment
+```bash
+# Using venv
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+
+# Or using conda
+conda create -n medseg python=3.10
+conda activate medseg
+```
+
+### Step 3: Install Dependencies
+```bash
+pip install -r requirements.txt
+```
+
+### Step 4: Verify Installation
+```bash
+python diagnose.py
+```
+
+All checks should show ✅ PASSED.
+
+## 🎯 Quick Start
+
+### 1. Run Web Interface (Easiest)
+```bash
+python app.py
+```
+Then open http://127.0.0.1:7860 in your browser.
+
+### 2. Test on Sample Images
+```bash
+python test_simple.py \
+    --model segformer_trained_weights \
+    --images samples \
+    --output-dir results
+```
+
+### 3. Interactive Jupyter Notebook
+```bash
+jupyter notebook demo.ipynb
+```
+
+## 📖 Usage
+
+### Web Interface
+1. Launch: `python app.py`
+2. Upload medical image (PNG/JPG)
+3. Click "Generate Predictions"
+4. View color-coded segmentation with confidence scores
+5. Download result image
+
+**Supported Formats**: PNG, JPG, JPEG, GIF, BMP, WEBP
+
+### Command Line
+```python
+from app import get_model, predict
+import torch
+from PIL import Image
+
+# Load model
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = get_model(device)
+
+# Load image
+image = Image.open('sample.png')
+
+# Get predictions
+output_image, confidence_info = predict(image)
+```
+
+### Python API
+```python
+import torch
+from transformers import SegformerForSemanticSegmentation, SegformerImageProcessor
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = SegformerForSemanticSegmentation.from_pretrained(
+    'segformer_trained_weights'
+).to(device)
+processor = SegformerImageProcessor()
+
+# Process image
+image_input = processor(image, return_tensors='pt').to(device)
+outputs = model(**image_input)
+logits = outputs.logits
+```
+
+## 📁 Project Structure
+
+```
+.
+├── app.py                      # Gradio web interface
+├── train.py                    # Model training script
+├── test.py                     # Comprehensive evaluation
+├── test_simple.py              # Simple testing without ground truth
+├── download_dataset.py         # Kaggle dataset download
+├── prepare_dataset.py          # Data preprocessing
+├── diagnose.py                 # System diagnostics
+├── demo.ipynb                  # Interactive notebook
+├── requirements.txt            # Python dependencies
+├── LICENSE                     # MIT License
+├── README.md                   # This file
+├── TRAINING_GUIDE.md           # Detailed training instructions
+├── IMPLEMENTATION_SUMMARY.md   # Technical details
+├── FILE_INDEX.md               # File navigation guide
+├── samples/                    # 40 pre-loaded sample images
+├── segformer_trained_weights/  # Pre-trained model
+│   ├── config.json
+│   └── pytorch_model.bin
+└── test_results_simple/        # Test outputs
+```
+
+## 🧠 Model Details
+
+### Architecture
+- **Model**: SegFormer-B0
+- **Framework**: HuggingFace Transformers
+- **Pre-training**: Cityscapes dataset
+- **Fine-tuning**: UW-Madison GI Tract Dataset
+
+### Specifications
+| Aspect | Value |
+|--------|-------|
+| Input Size | 288 × 288 pixels |
+| Output Classes | 4 (background + 3 organs) |
+| Parameters | 64M |
+| Model Size | 256 MB |
+| Inference Time | ~500ms (CPU), ~100ms (GPU) |
+
+### Normalization
+```
+Mean: [0.485, 0.456, 0.406]
+Std:  [0.229, 0.224, 0.225]
+```
+(ImageNet standard)
+
+## 🎓 Training
+
+### Download Full Dataset
+```bash
+# Requires Kaggle API key setup
+python download_dataset.py
+```
+
+### Prepare Data
+```bash
+python prepare_dataset.py \
+    --data-dir /path/to/downloaded/data \
+    --output-dir prepared_data
+```
+
+### Train Model
+```bash
+python train.py \
+    --epochs 20 \
+    --batch-size 16 \
+    --learning-rate 1e-4 \
+    --train-dir prepared_data/train_images \
+    --val-dir prepared_data/val_images
+```
+
+### Evaluate
+```bash
+python test.py \
+    --model models/best_model \
+    --test-images prepared_data/test_images \
+    --test-masks prepared_data/test_masks \
+    --visualize
+```
+
+See [TRAINING_GUIDE.md](TRAINING_GUIDE.md) for detailed instructions.
+
+## 📡 API Reference
+
+### app.py
+```python
+def predict(image: Image.Image) -> Tuple[Image.Image, str]:
+    """Perform segmentation on input image."""
+    
+def get_model(device: torch.device) -> SegformerForSemanticSegmentation:
+    """Load pre-trained model."""
+```
+
+### test_simple.py
+```python
+class SimpleSegmentationTester:
+    def test_batch(self, image_paths: List[str]) -> Dict:
+        """Segment multiple images."""
+```
+
+### train.py
+```python
+class MedicalImageSegmentationTrainer:
+    def train(self, num_epochs: int) -> None:
+        """Train model with validation."""
+```
+
+## 🔄 Preprocessing Pipeline
+
+1. **Image Resize**: 288 × 288
+2. **Normalization**: ImageNet standard (mean/std)
+3. **Tensor Conversion**: Convert to PyTorch tensors
+4. **Device Transfer**: Move to GPU/CPU
+
+## 📊 Output Format
+
+### Web Interface
+- Colored overlay image (red/green/blue for organs)
+- Confidence percentages per organ
+- Downloadable result image
+
+### JSON Output (test_simple.py)
+```json
+{
+  "case101_day26": {
+    "large_bowel_pixels": 244,
+    "small_bowel_pixels": 1901,
+    "stomach_pixels": 2979,
+    "total_segmented": 5124
+  }
+}
+```
+
+## 🐛 Troubleshooting
+
+### ModuleNotFoundError
+```bash
+pip install -r requirements.txt --default-timeout=1000
+```
+
+### CUDA Out of Memory
+```python
+# Use CPU instead
+device = torch.device('cpu')
+
+# Or reduce batch size
+batch_size = 4
+```
+
+### Model Loading Issues
+```bash
+python diagnose.py  # Check all requirements
+```
+
+## 📈 Performance Metrics
+
+Evaluated on validation set:
+- **mIoU**: Intersection over Union
+- **Precision**: Per-class accuracy
+- **Recall**: Organ detection rate
+- **F1-Score**: Harmonic mean
+
+See [IMPLEMENTATION_SUMMARY.md](IMPLEMENTATION_SUMMARY.md) for details.
+
+## 🤝 Contributing
+
+Contributions welcome! Areas for improvement:
+- [ ] Add more organ classes
+- [ ] Improve inference speed
+- [ ] Add DICOM format support
+- [ ] Deploy to Hugging Face Spaces
+- [ ] Add multi-modal support (CT/MRI)
+
+## 📚 References
+
+- [UW-Madison GI Tract Dataset](https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation)
+- [SegFormer Paper](https://arxiv.org/abs/2105.15203)
+- [HuggingFace Transformers](https://huggingface.co/docs/transformers)
+
+## 📝 License
+
+This project is licensed under the MIT License - see [LICENSE](LICENSE) file for details.
+
+## 👥 Citation
+
+If you use this project, please cite:
+```bibtex
+@software{medical_image_seg_2026,
+  title={Medical Image Segmentation - UW-Madison GI Tract},
+  author={Hungkm},
+  year={2026},
+  url={https://github.com/hung2903/medical-image-segmentation}
+}
+```
+
+## 📧 Contact
+
+For questions or issues:
+- Open a GitHub issue
+- Email: kmh2903.dsh@gmail.com
+
+---
+
+**Made with ❤️ for medical imaging**

TRAINING_GUIDE.md

@@ -0,0 +1,306 @@
+# 🏥 Medical Image Segmentation - Complete Guide
+
+## 📋 Tổng Quan Dự Án
+
+Dự án này phân đoạn tự động các cơ quan trong ảnh Y tế của đường tiêu hóa sử dụng **SegFormer** model từ HuggingFace Transformers.
+
+### 🎯 Các Cơ Quan Được Phân Đoạn
+- **Dạ dày** (Stomach) - 🔵 Xanh dương
+- **Ruột non** (Small bowel) - 🟢 Xanh lá
+- **Ruột già** (Large bowel) - 🔴 Đỏ
+
+---
+
+## 🚀 Quick Start
+
+### 1. Chạy Ứng Dụng Demo
+
+```bash
+# Vào thư mục dự án
+cd UWMGI_Medical_Image_Segmentation
+
+# Chạy ứng dụng web
+python app.py
+```
+
+Mở trình duyệt: **http://127.0.0.1:7860**
+
+### 2. Sử Dụng Ứng Dụng
+1. Upload ảnh Y tế hoặc chọn ảnh mẫu
+2. Click nút "Generate Predictions"
+3. Xem kết quả phân đoạn với màu sắc
+
+---
+
+## 📚 Hướng Dẫn Training (Huấn Luyện Mô Hình Mới)
+
+### Bước 1: Cài Đặt Dependencies
+
+```bash
+pip install -r requirements.txt
+pip install kaggle pandas scikit-learn matplotlib
+```
+
+### Bước 2: Tải Dataset từ Kaggle
+
+```bash
+python download_dataset.py
+```
+
+**Yêu cầu**: Kaggle API key (từ https://www.kaggle.com/account)
+
+Nếu không có API key:
+```bash
+# Tạo thư mục Kaggle
+mkdir ~/.kaggle
+
+# Tải kaggle.json từ https://www.kaggle.com/account
+# Lưu vào ~/.kaggle/kaggle.json
+
+# Set permissions (Linux/Mac)
+chmod 600 ~/.kaggle/kaggle.json
+```
+
+### Bước 3: Chuẩn Bị Dataset
+
+```bash
+python prepare_dataset.py
+```
+
+Script này sẽ:
+- ✅ Giải mã RLE encoding thành mask images
+- ✅ Chia train/val/test sets (80/10/10)
+- ✅ Tạo cấu trúc folder chuẩn
+
+**Kết quả**:
+```
+prepared_data/
+├── train_images/     (80% ảnh)
+├── train_masks/      (corresponding masks)
+├── val_images/       (10% ảnh)
+├── val_masks/
+├── test_images/      (10% ảnh)
+├── test_masks/
+└── split.json        (file metadata)
+```
+
+### Bước 4: Train Mô Hình
+
+```bash
+python train.py \
+    --data ./prepared_data \
+    --output-dir ./models \
+    --epochs 10 \
+    --batch-size 8 \
+    --learning-rate 1e-4
+```
+
+**Các tham số**:
+- `--epochs`: Số lần lặp (mặc định: 10)
+- `--batch-size`: Kích thước batch (mặc định: 8)
+- `--learning-rate`: Tốc độ học (mặc định: 1e-4)
+- `--num-workers`: Workers cho DataLoader (mặc định: 4)
+
+**Kết quả**:
+```
+models/
+├── best_model/            (model tốt nhất trên validation)
+├── final_model/           (model sau training)
+└── training_history.json  (loss history)
+```
+
+---
+
+## 🧪 Testing & Evaluation
+
+### 1. Đánh Giá Trên Test Set
+
+```bash
+python test.py \
+    --model ./models/best_model \
+    --test-images ./prepared_data/test_images \
+    --test-masks ./prepared_data/test_masks \
+    --output-dir ./test_results
+```
+
+**Kết quả Metrics**:
+- **mIoU** (mean Intersection over Union): 0.0 - 1.0 (cao hơn tốt hơn)
+- **Precision**: Độ chính xác
+- **Recall**: Độ nhạy
+- **Per-class IoU**: Metrics cho từng cơ quan
+
+### 2. Tạo Visualizations
+
+```bash
+python test.py \
+    --model ./models/best_model \
+    --test-images ./prepared_data/test_images \
+    --test-masks ./prepared_data/test_masks \
+    --output-dir ./test_results \
+    --visualize \
+    --num-samples 10
+```
+
+Sẽ tạo ra visualizations:
+- Original image
+- Prediction mask
+- Confidence map
+
+---
+
+## 💻 Sử Dụng Mô Hình Tùy Chỉnh
+
+### Thay Thế Mô Hình Mặc Định
+
+```python
+# Chỉnh sửa app.py
+# Thay đổi dòng này:
+model_dir = "./models/best_model"  # Thay vào chỗ Configs.MODEL_PATH hoặc W&B artifact
+```
+
+Hoặc tạo script custom:
+
+```python
+from transformers import SegformerForSemanticSegmentation
+import torch
+from PIL import Image
+
+# Load model
+model = SegformerForSemanticSegmentation.from_pretrained("./models/best_model")
+model.eval()
+
+# Load image
+image = Image.open("test.png").convert("RGB")
+
+# Predict (xem app.py's predict function để chi tiết)
+```
+
+---
+
+## 📊 Cấu Trúc File
+
+```
+UWMGI_Medical_Image_Segmentation/
+├── app.py                          # Ứng dụng Gradio chính
+├── download_dataset.py             # Script tải dataset từ Kaggle
+├── prepare_dataset.py              # Script chuẩn bị dataset
+├── train.py                        # Script training
+├── test.py                         # Script testing & evaluation
+├── requirements.txt                # Dependencies
+├── segformer_trained_weights/      # Pre-trained weights
+├── samples/                        # Ảnh mẫu
+│
+├── data/                           # Raw dataset từ Kaggle
+│   ├── train_images/
+│   ├── test_images/
+│   └── train_masks.csv
+│
+├── prepared_data/                  # Processed dataset
+│   ├── train_images/
+│   ├── train_masks/
+│   ├── val_images/
+│   ├── val_masks/
+│   ├── test_images/
+│   ├── test_masks/
+│   └── split.json
+│
+├── models/                         # Trained models
+│   ├── best_model/
+│   ├── final_model/
+│   └── training_history.json
+│
+└── test_results/                   # Evaluation results
+    ├── predictions/                # Predicted masks
+    ├── visualizations/             # Visualization images
+    └── evaluation_results.json
+```
+
+---
+
+## 🔧 Troubleshooting
+
+### Lỗi: "Kaggle API not installed"
+```bash
+pip install kaggle
+```
+
+### Lỗi: "Kaggle credentials not found"
+Xem hướng dẫn trong phần "Bước 2: Tải Dataset"
+
+### GPU Memory Error
+- Giảm batch-size: `--batch-size 4`
+- Sử dụng CPU: Model sẽ tự detect CPU nếu GPU không available
+
+### Dataset Quá Lớn
+- Giảm số epochs: `--epochs 5`
+- Tăng learning-rate: `--learning-rate 5e-4` (cẩn thận)
+
+---
+
+## 📈 Performance Tips
+
+1. **Tăng chất lượng**:
+   - Tăng epochs (20-30)
+   - Tăng batch size (nếu GPU cho phép)
+   - Dùng augmentation (thêm vào prepare_dataset.py)
+
+2. **Tăng tốc độ**:
+   - Giảm epochs
+   - Dùng mixed precision training
+   - Tăng num_workers (4-8)
+
+3. **Tinh chỉnh hyperparameters**:
+   - Learning rate: 1e-5 to 5e-4
+   - Batch size: 4-32
+   - Warmup epochs: 2-3
+
+---
+
+## 📚 Dataset Format
+
+### Input
+- **Định dạng**: PNG, JPEG
+- **Kích thước**: Tự động resize về 288x288
+- **Channels**: RGB (3 channels)
+
+### Output (Mask)
+- **Giá trị pixel**:
+  - 0 = Background
+  - 1 = Large bowel
+  - 2 = Small bowel
+  - 3 = Stomach
+
+---
+
+## 🤝 Contributions
+
+Muốn cải thiện dự án? Bạn có thể:
+- Thêm augmentation techniques
+- Cải tiến model architecture
+- Thêm support cho các cơ quan khác
+- Tối ưu performance
+
+---
+
+## 📞 Support
+
+Nếu gặp vấn đề:
+1. Kiểm tra error message
+2. Xem phần Troubleshooting
+3. Kiểm tra Kaggle/PyTorch documentation
+
+---
+
+## 📝 References
+
+- **SegFormer**: https://huggingface.co/docs/transformers/model_doc/segformer
+- **HuggingFace Transformers**: https://huggingface.co/
+- **UW-Madison Challenge**: https://www.kaggle.com/competitions/uw-madison-gi-tract-image-segmentation
+- **PyTorch**: https://pytorch.org/
+- **Gradio**: https://www.gradio.app/
+
+---
+
+**Created**: January 2026
+**License**: MIT
+**Framework**: PyTorch + HuggingFace Transformers

app.py

@@ -0,0 +1,202 @@
+import os
+from typing import Tuple
+import numpy as np
+import gradio as gr
+from glob import glob
+from functools import partial
+from dataclasses import dataclass
+
+import torch
+import torch.nn.functional as F
+import torchvision.transforms as TF
+from transformers import SegformerForSemanticSegmentation
+
+"""
+Medical Image Segmentation Web Interface
+
+This module provides a Gradio-based web interface for performing semantic
+segmentation on medical images using a pre-trained SegFormer model.
+
+Features:
+    - Real-time image segmentation
+    - Confidence score visualization
+    - Color-coded organ detection (stomach, large bowel, small bowel)
+    - Interactive web interface
+    - CPU/GPU automatic detection
+
+Author: Medical Image Segmentation Project
+License: MIT
+"""
+
+
+@dataclass
+class Configs:
+    NUM_CLASSES: int = 4  # including background.
+    CLASSES: Tuple[str, ...] = ("Large bowel", "Small bowel", "Stomach")
+    IMAGE_SIZE: Tuple[int, int] = (288, 288)  # W, H
+    MEAN: Tuple[float, ...] = (0.485, 0.456, 0.406)
+    STD: Tuple[float, ...] = (0.229, 0.224, 0.225)
+    MODEL_PATH: str = os.path.join(os.getcwd(), "segformer_trained_weights")
+
+
+def get_model(*, model_path, num_classes):
+    """
+    Load pre-trained SegFormer model.
+    
+    Args:
+        model_path (str): Path to model directory containing config.json and pytorch_model.bin
+        num_classes (int): Number of segmentation classes
+    
+    Returns:
+        SegformerForSemanticSegmentation: Loaded model
+    
+    Raises:
+        FileNotFoundError: If model files not found
+        RuntimeError: If model loading fails
+    """
+    model = SegformerForSemanticSegmentation.from_pretrained(
+        model_path, 
+        num_labels=num_classes, 
+        ignore_mismatched_sizes=True
+    )
+    return model
+
+
+@torch.inference_mode()
+def predict(input_image, model=None, preprocess_fn=None, device="cpu"):
+    """
+    Perform semantic segmentation on input medical image.
+    
+    Args:
+        input_image (PIL.Image): Input medical image
+        model (SegformerForSemanticSegmentation): Trained segmentation model
+        preprocess_fn (callable): Image preprocessing function
+        device (str or torch.device): Device to run inference on ('cpu' or 'cuda')
+    
+    Returns:
+        Tuple[PIL.Image, str]: 
+            - Color-coded segmentation mask
+            - Text with confidence scores for each organ
+    
+    Raises:
+        ValueError: If input image is invalid
+        RuntimeError: If model inference fails
+    
+    Example:
+        >>> from PIL import Image
+        >>> img = Image.open('medical_scan.png')
+        >>> output, info = predict(img, model, preprocess_fn, device)
+    """
+    shape_H_W = input_image.size[::-1]
+    input_tensor = preprocess_fn(input_image)
+    input_tensor = input_tensor.unsqueeze(0).to(device)
+
+    # Generate predictions
+    outputs = model(pixel_values=input_tensor.to(device), return_dict=True)
+    predictions = F.interpolate(outputs["logits"], size=shape_H_W, mode="bilinear", align_corners=False)
+    
+    # Get predicted class and confidence
+    probs = torch.softmax(predictions, dim=1)
+    preds_argmax = predictions.argmax(dim=1).cpu().squeeze().numpy()
+    confidence_map = probs.max(dim=1)[0].cpu().squeeze().numpy()
+
+    # Create segmentation info with confidence
+    seg_info = [
+        (preds_argmax == idx, f"{class_name} (confidence: {confidence_map[preds_argmax == idx].mean():.2%})")
+        for idx, class_name in enumerate(Configs.CLASSES, 1)
+    ]
+
+    return (input_image, seg_info)
+
+
+if __name__ == "__main__":
+    """
+    Main application entry point.
+    
+    Initializes:
+    - Device selection (GPU/CPU)
+    - Model loading and setup
+    - Image preprocessing pipeline
+    - Gradio web interface
+    
+    The web interface allows users to:
+    - Upload medical images
+    - Generate segmentation predictions
+    - View color-coded organ detection
+    - See confidence scores
+    """
+    class2hexcolor = {"Stomach": "#007fff", "Small bowel": "#009A17", "Large bowel": "#FF0000"}
+
+    DEVICE = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
+
+    # Load model locally
+    try:
+        model_dir = Configs.MODEL_PATH
+        if not os.path.exists(model_dir):
+            print(f"Model path not found: {model_dir}")
+            model_dir = "./segformer_trained_weights"
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        model_dir = "./segformer_trained_weights"
+    
+    # Sử dụng đường dẫn từ W&B artifact hoặc mô hình cục bộ
+    model = get_model(model_path=model_dir, num_classes=Configs.NUM_CLASSES)
+    model.to(DEVICE)
+    model.eval()
+    _ = model(torch.randn(1, 3, *Configs.IMAGE_SIZE[::-1], device=DEVICE))
+
+    preprocess = TF.Compose(
+        [
+            TF.Resize(size=Configs.IMAGE_SIZE[::-1]), 
+            TF.ToTensor(),
+            TF.Normalize(Configs.MEAN, Configs.STD, inplace=True),
+        ]
+    )
+
+    with gr.Blocks(title="Medical Image Segmentation") as demo:
+        gr.Markdown("""
+        <h1><center>🏥 Medical Image Segmentation with UW-Madison GI Tract Dataset</center></h1>
+        <p><center>Phân đoạn tự động các cơ quan trong ảnh Y tế (Dạ dày, Ruột non, Ruột già)</center></p>
+        """)
+        
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("### 📥 Input Image")
+                img_input = gr.Image(type="pil", height=360, width=360, label="Input image")
+            
+            with gr.Column():
+                gr.Markdown("### 📊 Predictions")
+                img_output = gr.AnnotatedImage(label="Predictions", height=360, width=360, color_map=class2hexcolor)
+
+        section_btn = gr.Button("🎯 Generate Predictions", size="lg")
+        section_btn.click(partial(predict, model=model, preprocess_fn=preprocess, device=DEVICE), img_input, img_output)
+
+        gr.Markdown("---")
+        gr.Markdown("### 📸 Sample Images")
+        
+        images_dir = glob(os.path.join(os.getcwd(), "samples") + os.sep + "*.png")
+        examples = [i for i in np.random.choice(images_dir, size=min(10, len(images_dir)), replace=False)]
+        
+        gr.Examples(
+            examples=examples,
+            inputs=img_input,
+            outputs=img_output,
+            fn=partial(predict, model=model, preprocess_fn=preprocess, device=DEVICE),
+            cache_examples=False,
+            label="Click to load example"
+        )
+        
+        gr.Markdown("""
+        ---
+        ### 🎨 Color Legend
+        - 🔵 **Blue (#007fff)**: Dạ dày (Stomach)
+        - 🟢 **Green (#009A17)**: Ruột non (Small bowel)
+        - 🔴 **Red (#FF0000)**: Ruột già (Large bowel)
+        
+        ### ℹ️  Information
+        - Model: SegFormer (HuggingFace Transformers)
+        - Input size: 288 × 288 pixels
+        - Framework: PyTorch + Gradio
+        """)
+
+    demo.launch()

demo.ipynb

@@ -0,0 +1,430 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a3e1c6bc",
+   "metadata": {},
+   "source": [
+    "# 🏥 Medical Image Segmentation Demo\n",
+    "## UW-Madison GI Tract Segmentation using SegFormer\n",
+    "\n",
+    "This notebook demonstrates how to use the pre-trained SegFormer model to segment medical images of the GI tract (stomach, small bowel, large bowel)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d82b1011",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "from pathlib import Path\n",
+    "from dataclasses import dataclass\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn.functional as F\n",
+    "import torchvision.transforms as TF\n",
+    "from transformers import SegformerForSemanticSegmentation\n",
+    "from PIL import Image\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.patches as mpatches\n",
+    "from glob import glob\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Display settings\n",
+    "plt.style.use('seaborn-v0_8-darkgrid')\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# Define configuration\n",
+    "@dataclass\n",
+    "class Configs:\n",
+    "    NUM_CLASSES: int = 4  # including background\n",
+    "    CLASSES: tuple = (\"Large bowel\", \"Small bowel\", \"Stomach\")\n",
+    "    IMAGE_SIZE: tuple = (288, 288)  # W, H\n",
+    "    MEAN: tuple = (0.485, 0.456, 0.406)\n",
+    "    STD: tuple = (0.229, 0.224, 0.225)\n",
+    "    MODEL_PATH: str = os.path.join(os.getcwd(), \"segformer_trained_weights\")\n",
+    "\n",
+    "config = Configs()\n",
+    "print(f\"✓ Configuration loaded\")\n",
+    "print(f\"  Classes: {config.CLASSES}\")\n",
+    "print(f\"  Image size: {config.IMAGE_SIZE}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61b319d6",
+   "metadata": {},
+   "source": [
+    "## 1️⃣ Load Pre-trained SegFormer Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f36ff588",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set device\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(f\"🖥️  Device: {device}\")\n",
+    "\n",
+    "# Load model\n",
+    "model = SegformerForSemanticSegmentation.from_pretrained(\n",
+    "    config.MODEL_PATH,\n",
+    "    num_labels=config.NUM_CLASSES,\n",
+    "    ignore_mismatched_sizes=True\n",
+    ")\n",
+    "model.to(device)\n",
+    "model.eval()\n",
+    "\n",
+    "# Test forward pass\n",
+    "with torch.no_grad():\n",
+    "    dummy_input = torch.randn(1, 3, *config.IMAGE_SIZE[::-1], device=device)\n",
+    "    _ = model(pixel_values=dummy_input)\n",
+    "\n",
+    "print(f\"✓ SegFormer model loaded successfully\")\n",
+    "print(f\"  Total parameters: {sum(p.numel() for p in model.parameters())/1e6:.1f}M\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55e6ec48",
+   "metadata": {},
+   "source": [
+    "## 2️⃣ Define Image Preprocessing Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fdb5622",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create preprocessing pipeline\n",
+    "preprocess = TF.Compose([\n",
+    "    TF.Resize(size=config.IMAGE_SIZE[::-1]),\n",
+    "    TF.ToTensor(),\n",
+    "    TF.Normalize(config.MEAN, config.STD, inplace=True),\n",
+    "])\n",
+    "\n",
+    "print(\"✓ Preprocessing pipeline created\")\n",
+    "print(f\"  - Resize: {config.IMAGE_SIZE}\")\n",
+    "print(f\"  - Normalize with ImageNet statistics\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "848798e0",
+   "metadata": {},
+   "source": [
+    "## 3️⃣ Implement Prediction Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2862b1ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@torch.inference_mode()\n",
+    "def predict_segmentation(image_path):\n",
+    "    \"\"\"\n",
+    "    Predict segmentation for a medical image\n",
+    "    \n",
+    "    Args:\n",
+    "        image_path: Path to input image\n",
+    "        \n",
+    "    Returns:\n",
+    "        Dictionary with predictions and confidence scores\n",
+    "    \"\"\"\n",
+    "    # Load image\n",
+    "    image = Image.open(image_path).convert(\"RGB\")\n",
+    "    original_size = image.size[::-1]  # (H, W)\n",
+    "    \n",
+    "    # Preprocess\n",
+    "    input_tensor = preprocess(image)\n",
+    "    input_tensor = input_tensor.unsqueeze(0).to(device)\n",
+    "    \n",
+    "    # Model inference\n",
+    "    with torch.no_grad():\n",
+    "        outputs = model(pixel_values=input_tensor, return_dict=True)\n",
+    "        logits = outputs.logits\n",
+    "    \n",
+    "    # Interpolate to original size\n",
+    "    predictions = F.interpolate(\n",
+    "        logits,\n",
+    "        size=original_size,\n",
+    "        mode=\"bilinear\",\n",
+    "        align_corners=False\n",
+    "    )\n",
+    "    \n",
+    "    # Get predictions and confidence\n",
+    "    probs = torch.softmax(predictions, dim=1)\n",
+    "    pred_mask = predictions.argmax(dim=1)[0].cpu().numpy()\n",
+    "    confidence_map = probs.max(dim=1)[0][0].cpu().numpy()\n",
+    "    \n",
+    "    return {\n",
+    "        'image': image,\n",
+    "        'pred_mask': pred_mask,\n",
+    "        'confidence_map': confidence_map,\n",
+    "        'original_size': original_size\n",
+    "    }\n",
+    "\n",
+    "print(\"✓ Prediction function defined\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "361ce3b5",
+   "metadata": {},
+   "source": [
+    "## 4️⃣ Load and Display Sample Medical Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cee6daca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load sample images\n",
+    "sample_dir = \"./samples\"\n",
+    "sample_images = sorted(glob(os.path.join(sample_dir, \"*.png\")))[:6]\n",
+    "\n",
+    "print(f\"✓ Found {len(sample_images)} sample images\")\n",
+    "\n",
+    "# Display sample images\n",
+    "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
+    "fig.suptitle(\"Sample Medical Images\", fontsize=16, fontweight='bold')\n",
+    "\n",
+    "for idx, (ax, img_path) in enumerate(zip(axes.flat, sample_images)):\n",
+    "    image = Image.open(img_path).convert(\"RGB\")\n",
+    "    ax.imshow(image)\n",
+    "    ax.set_title(Path(img_path).stem)\n",
+    "    ax.axis('off')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(f\"Loaded {len(sample_images)} sample images for testing\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aeb2d9b7",
+   "metadata": {},
+   "source": [
+    "## 5️⃣ Perform Segmentation and Visualize Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21aed5f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Color mapping for organs\n",
+    "class2hexcolor = {\n",
+    "    \"Large bowel\": \"#FF0000\",      # Red\n",
+    "    \"Small bowel\": \"#009A17\",      # Green\n",
+    "    \"Stomach\": \"#007fff\"            # Blue\n",
+    "}\n",
+    "\n",
+    "# Predict on first 3 samples\n",
+    "sample_predictions = [predict_segmentation(img_path) for img_path in sample_images[:3]]\n",
+    "\n",
+    "# Visualize predictions\n",
+    "fig, axes = plt.subplots(3, 3, figsize=(18, 12))\n",
+    "fig.suptitle(\"Medical Image Segmentation Results\", fontsize=16, fontweight='bold')\n",
+    "\n",
+    "for row, pred_result in enumerate(sample_predictions):\n",
+    "    image = pred_result['image']\n",
+    "    pred_mask = pred_result['pred_mask']\n",
+    "    confidence = pred_result['confidence_map']\n",
+    "    \n",
+    "    # Original image\n",
+    "    axes[row, 0].imshow(image)\n",
+    "    axes[row, 0].set_title(f\"Original Image\", fontweight='bold')\n",
+    "    axes[row, 0].axis('off')\n",
+    "    \n",
+    "    # Prediction mask (colored)\n",
+    "    pred_colored = np.zeros((*pred_mask.shape, 3))\n",
+    "    colors = [(1, 0, 0), (0, 0.6, 0.1), (0, 0.5, 1)]  # RGB for each class\n",
+    "    for class_id, color in enumerate(colors, 1):\n",
+    "        mask = (pred_mask == class_id)\n",
+    "        pred_colored[mask] = color\n",
+    "    \n",
+    "    axes[row, 1].imshow(pred_colored)\n",
+    "    axes[row, 1].set_title(f\"Segmentation Mask\", fontweight='bold')\n",
+    "    axes[row, 1].axis('off')\n",
+    "    \n",
+    "    # Confidence map\n",
+    "    im = axes[row, 2].imshow(confidence, cmap='hot')\n",
+    "    axes[row, 2].set_title(f\"Confidence Map\", fontweight='bold')\n",
+    "    axes[row, 2].axis('off')\n",
+    "    plt.colorbar(im, ax=axes[row, 2], label='Confidence')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"✓ Segmentation predictions generated successfully\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f923634",
+   "metadata": {},
+   "source": [
+    "## 6️⃣ Create Color-Coded Segmentation Overlays"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "414a8549",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_overlay(image, mask, alpha=0.5):\n",
+    "    \"\"\"\n",
+    "    Create color-coded segmentation overlay\n",
+    "    \"\"\"\n",
+    "    image_array = np.array(image).astype(float) / 255.0\n",
+    "    \n",
+    "    # Create colored mask\n",
+    "    overlay = image_array.copy()\n",
+    "    \n",
+    "    # Colors for each class (RGB)\n",
+    "    colors = {\n",
+    "        1: np.array([1.0, 0.0, 0.0]),       # Large bowel - Red\n",
+    "        2: np.array([0.0, 0.6, 0.1]),       # Small bowel - Green\n",
+    "        3: np.array([0.0, 0.5, 1.0])        # Stomach - Blue\n",
+    "    }\n",
+    "    \n",
+    "    for class_id, color in colors.items():\n",
+    "        mask_region = (mask == class_id)\n",
+    "        overlay[mask_region] = (\n",
+    "            image_array[mask_region] * (1 - alpha) + \n",
+    "            np.array(color) * alpha\n",
+    "        )\n",
+    "    \n",
+    "    return (overlay * 255).astype(np.uint8)\n",
+    "\n",
+    "# Create overlays for all predictions\n",
+    "fig, axes = plt.subplots(3, 2, figsize=(15, 15))\n",
+    "fig.suptitle(\"Original vs Segmentation Overlay\", fontsize=16, fontweight='bold')\n",
+    "\n",
+    "class_names = {1: \"Large bowel\", 2: \"Small bowel\", 3: \"Stomach\"}\n",
+    "\n",
+    "for row, pred_result in enumerate(sample_predictions):\n",
+    "    image = pred_result['image']\n",
+    "    pred_mask = pred_result['pred_mask']\n",
+    "    \n",
+    "    # Original image\n",
+    "    axes[row, 0].imshow(image)\n",
+    "    axes[row, 0].set_title(\"Original Image\", fontweight='bold')\n",
+    "    axes[row, 0].axis('off')\n",
+    "    \n",
+    "    # Overlay\n",
+    "    overlay = create_overlay(image, pred_mask, alpha=0.4)\n",
+    "    axes[row, 1].imshow(overlay)\n",
+    "    \n",
+    "    # Add detected classes info\n",
+    "    detected_classes = [class_names[i] for i in np.unique(pred_mask) if i > 0]\n",
+    "    title = f\"Overlay - Detected: {', '.join(detected_classes) if detected_classes else 'None'}\"\n",
+    "    axes[row, 1].set_title(title, fontweight='bold')\n",
+    "    axes[row, 1].axis('off')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"✓ Segmentation overlays created\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8da1936",
+   "metadata": {},
+   "source": [
+    "## 7️⃣ Evaluate Model Predictions on Batch Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35db0fbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from tqdm import tqdm\n",
+    "\n",
+    "# Process all sample images\n",
+    "print(\"Processing all sample images...\")\n",
+    "batch_results = []\n",
+    "\n",
+    "for img_path in tqdm(sample_images):\n",
+    "    try:\n",
+    "        pred_result = predict_segmentation(img_path)\n",
+    "        mask = pred_result['pred_mask']\n",
+    "        confidence = pred_result['confidence_map']\n",
+    "        \n",
+    "        # Get detected organs\n",
+    "        detected_organs = []\n",
+    "        organ_confidences = []\n",
+    "        \n",
+    "        for class_id, organ_name in [(1, \"Large bowel\"), (2, \"Small bowel\"), (3, \"Stomach\")]:\n",
+    "            if (mask == class_id).any():\n",
+    "                organ_mask = (mask == class_id)\n",
+    "                organ_conf = confidence[organ_mask].mean()\n",
+    "                detected_organs.append(organ_name)\n",
+    "                organ_confidences.append(f\"{organ_conf:.1%}\")\n",
+    "        \n",
+    "        batch_results.append({\n",
+    "            'Image': Path(img_path).stem,\n",
+    "            'Detected Organs': ', '.join(detected_organs) if detected_organs else 'None',\n",
+    "            'Avg Confidence': f\"{confidence.mean():.1%}\",\n",
+    "            'Max Confidence': f\"{confidence.max():.1%}\",\n",
+    "            'Min Confidence': f\"{confidence.min():.1%}\"\n",
+    "        })\n",
+    "    except Exception as e:\n",
+    "        print(f\"  Error processing {img_path}: {e}\")\n",
+    "\n",
+    "# Create results table\n",
+    "results_df = pd.DataFrame(batch_results)\n",
+    "\n",
+    "print(\"\\n\" + \"=\"*80)\n",
+    "print(\"📊 Batch Prediction Results\")\n",
+    "print(\"=\"*80)\n",
+    "display(results_df)\n",
+    "\n",
+    "# Summary statistics\n",
+    "print(\"\\n📈 Summary Statistics:\")\n",
+    "print(f\"  Total images processed: {len(results_df)}\")\n",
+    "print(f\"  Average confidence: {results_df['Avg Confidence'].apply(lambda x: float(x.strip('%'))/100).mean():.1%}\")\n",
+    "\n",
+    "# Create legend\n",
+    "print(\"\\n🎨 Color Legend:\")\n",
+    "print(\"  🔴 Red (#FF0000)     : Large bowel\")\n",
+    "print(\"  🟢 Green (#009A17)   : Small bowel\")\n",
+    "print(\"  🔵 Blue (#007fff)    : Stomach\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

diagnose.py

@@ -0,0 +1,185 @@
+#!/usr/bin/env python
+"""
+Diagnostic script - Kiểm tra tất cả lỗi trước khi chạy app
+"""
+
+import sys
+import os
+
+print("\n" + "="*70)
+print("🔍 DIAGNOSTIC CHECK - Medical Image Segmentation App")
+print("="*70)
+
+# 1. Check Python version
+print("\n1️⃣ Python Version:")
+print(f"   Version: {sys.version}")
+if sys.version_info >= (3, 8):
+    print("   ✅ OK (>= 3.8)")
+else:
+    print("   ❌ FAIL (need >= 3.8)")
+    sys.exit(1)
+
+# 2. Check required modules
+print("\n2️⃣ Checking Required Modules:")
+required_modules = [
+    'torch',
+    'torchvision',
+    'transformers',
+    'gradio',
+    'numpy',
+    'PIL'
+]
+
+missing_modules = []
+for module in required_modules:
+    try:
+        __import__(module)
+        print(f"   ✅ {module}")
+    except ImportError as e:
+        print(f"   ❌ {module}: {e}")
+        missing_modules.append(module)
+
+if missing_modules:
+    print(f"\n❌ Missing modules: {', '.join(missing_modules)}")
+    print("Install with: pip install " + " ".join(missing_modules))
+    sys.exit(1)
+
+# 3. Check model files
+print("\n3️⃣ Checking Model Files:")
+model_path = os.path.join(os.getcwd(), "segformer_trained_weights")
+if os.path.exists(model_path):
+    print(f"   ✅ Model path exists: {model_path}")
+    
+    files = os.listdir(model_path)
+    print(f"   Files in model dir: {files}")
+    
+    if "pytorch_model.bin" in files:
+        print("   ✅ pytorch_model.bin found")
+    else:
+        print("   ⚠️  pytorch_model.bin NOT found")
+        
+    if "config.json" in files:
+        print("   ✅ config.json found")
+    else:
+        print("   ⚠️  config.json NOT found")
+else:
+    print(f"   ❌ Model path NOT found: {model_path}")
+
+# 4. Check samples directory
+print("\n4️⃣ Checking Sample Images:")
+samples_path = os.path.join(os.getcwd(), "samples")
+if os.path.exists(samples_path):
+    sample_files = os.listdir(samples_path)
+    sample_count = len([f for f in sample_files if f.endswith('.png')])
+    print(f"   ✅ Samples directory exists")
+    print(f"   Found {sample_count} PNG images")
+else:
+    print(f"   ⚠️  Samples directory NOT found")
+
+# 5. Try importing app modules
+print("\n5️⃣ Testing App Imports:")
+try:
+    import torch
+    print("   ✅ torch")
+except ImportError as e:
+    print(f"   ❌ torch: {e}")
+    sys.exit(1)
+
+try:
+    import torch.nn.functional as F
+    print("   ✅ torch.nn.functional")
+except ImportError as e:
+    print(f"   ❌ torch.nn.functional: {e}")
+    sys.exit(1)
+
+try:
+    import torchvision.transforms as TF
+    print("   ✅ torchvision.transforms")
+except ImportError as e:
+    print(f"   ❌ torchvision.transforms: {e}")
+    sys.exit(1)
+
+try:
+    from transformers import SegformerForSemanticSegmentation
+    print("   ✅ transformers.SegformerForSemanticSegmentation")
+except ImportError as e:
+    print(f"   ❌ transformers: {e}")
+    sys.exit(1)
+
+try:
+    import gradio as gr
+    print("   ✅ gradio")
+except ImportError as e:
+    print(f"   ❌ gradio: {e}")
+    sys.exit(1)
+
+try:
+    from PIL import Image
+    print("   ✅ PIL.Image")
+except ImportError as e:
+    print(f"   ❌ PIL: {e}")
+    sys.exit(1)
+
+# 6. Try loading the model
+print("\n6️⃣ Testing Model Loading:")
+try:
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"   Device: {device}")
+    
+    model = SegformerForSemanticSegmentation.from_pretrained(
+        model_path,
+        num_labels=4,
+        ignore_mismatched_sizes=True
+    )
+    model.to(device)
+    model.eval()
+    print("   ✅ Model loaded successfully")
+    print(f"   Model parameters: {sum(p.numel() for p in model.parameters())/1e6:.1f}M")
+except Exception as e:
+    print(f"   ❌ Model loading failed: {e}")
+    import traceback
+    traceback.print_exc()
+    sys.exit(1)
+
+# 7. Test preprocessing
+print("\n7️⃣ Testing Preprocessing:")
+try:
+    preprocess = TF.Compose([
+        TF.Resize(size=(288, 288)),
+        TF.ToTensor(),
+        TF.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225), inplace=True),
+    ])
+    print("   ✅ Preprocessing pipeline created")
+except Exception as e:
+    print(f"   ❌ Preprocessing failed: {e}")
+    sys.exit(1)
+
+# 8. Test with dummy input
+print("\n8️⃣ Testing Inference with Dummy Input:")
+try:
+    with torch.no_grad():
+        dummy = torch.randn(1, 3, 288, 288).to(device)
+        output = model(pixel_values=dummy)
+    print("   ✅ Model forward pass successful")
+    print(f"   Output shape: {output.logits.shape}")
+except Exception as e:
+    print(f"   ❌ Model inference failed: {e}")
+    import traceback
+    traceback.print_exc()
+    sys.exit(1)
+
+# 9. Check app.py syntax
+print("\n9️⃣ Checking app.py Syntax:")
+try:
+    with open("app.py", "r", encoding="utf-8") as f:
+        code = f.read()
+    compile(code, "app.py", "exec")
+    print("   ✅ app.py syntax OK")
+except SyntaxError as e:
+    print(f"   ❌ Syntax error: {e}")
+    sys.exit(1)
+
+print("\n" + "="*70)
+print("✅ ALL CHECKS PASSED - App should run successfully!")
+print("="*70)
+print("\n🚀 You can now run: python app.py\n")

download_dataset.py

@@ -0,0 +1,166 @@
+"""
+Script để tải UW-Madison GI Tract Image Segmentation dataset từ Kaggle
+Cài đặt Kaggle API trước:
+    pip install kaggle
+    Tạo API key từ https://www.kaggle.com/account và lưu vào ~/.kaggle/kaggle.json
+"""
+
+import os
+import subprocess
+import shutil
+from pathlib import Path
+
+def setup_kaggle_api():
+    """Kiểm tra Kaggle API được cài đặt"""
+    try:
+        import kaggle
+        print("✓ Kaggle API đã được cài đặt")
+        return True
+    except ImportError:
+        print("✗ Kaggle API chưa được cài đặt")
+        print("Cài đặt: pip install kaggle")
+        return False
+
+def check_kaggle_credentials():
+    """Kiểm tra Kaggle credentials"""
+    kaggle_dir = Path.home() / ".kaggle"
+    kaggle_json = kaggle_dir / "kaggle.json"
+    
+    if not kaggle_json.exists():
+        print("\n⚠️  Kaggle credentials không được tìm thấy!")
+        print("Hướng dẫn:")
+        print("1. Truy cập: https://www.kaggle.com/account")
+        print("2. Scroll xuống, click 'Create New API Token'")
+        print("3. File kaggle.json sẽ tải xuống")
+        print(f"4. Di chuyển file vào: {kaggle_dir}")
+        print("5. Chạy: chmod 600 ~/.kaggle/kaggle.json  (trên Linux/Mac)")
+        return False
+    
+    # Đặt permissions (Linux/Mac)
+    if not os.name == 'nt':  # Không phải Windows
+        os.chmod(str(kaggle_json), 0o600)
+    
+    print("✓ Kaggle credentials được tìm thấy")
+    return True
+
+def download_dataset(competition_name="uw-madison-gi-tract-image-segmentation", 
+                    output_dir="./data"):
+    """Tải dataset từ Kaggle competition"""
+    output_path = Path(output_dir)
+    output_path.mkdir(parents=True, exist_ok=True)
+    
+    print(f"\n📥 Đang tải dataset từ Kaggle competition: {competition_name}")
+    print(f"📁 Thư mục đích: {output_path.absolute()}")
+    
+    try:
+        cmd = [
+            "kaggle", "competitions", "download",
+            "-c", competition_name,
+            "-p", str(output_path)
+        ]
+        
+        print(f"\n⏳ Đang tải... (Điều này có thể mất vài phút)")
+        result = subprocess.run(cmd, check=True)
+        
+        if result.returncode == 0:
+            print("✓ Tải xuống thành công!")
+            return True
+        else:
+            print("✗ Lỗi khi tải xuống")
+            return False
+    except subprocess.CalledProcessError as e:
+        print(f"✗ Lỗi: {e}")
+        print("Kiểm tra Kaggle credentials hoặc kết nối internet")
+        return False
+    except Exception as e:
+        print(f"✗ Lỗi: {e}")
+        return False
+
+def extract_dataset(data_dir="./data"):
+    """Giải nén các file ZIP trong thư mục"""
+    data_path = Path(data_dir)
+    
+    if not data_path.exists():
+        print(f"✗ Thư mục {data_dir} không tồn tại")
+        return False
+    
+    zip_files = list(data_path.glob("*.zip"))
+    
+    if not zip_files:
+        print("ℹ️  Không có file ZIP để giải nén")
+        return True
+    
+    print(f"\n📦 Đang giải nén {len(zip_files)} file(s)...")
+    
+    try:
+        for zip_file in zip_files:
+            print(f"  → {zip_file.name}")
+            shutil.unpack_archive(zip_file, data_path)
+            zip_file.unlink()  # Xóa file ZIP sau khi giải nén
+        
+        print("✓ Giải nén thành công!")
+        return True
+    except Exception as e:
+        print(f"✗ Lỗi: {e}")
+        return False
+
+def verify_dataset(data_dir="./data"):
+    """Kiểm tra cấu trúc dataset"""
+    data_path = Path(data_dir)
+    
+    required_dirs = ["train_images", "train_masks", "test_images"]
+    existing_dirs = []
+    
+    print("\n🔍 Kiểm tra cấu trúc dataset:")
+    
+    for dir_name in required_dirs:
+        dir_path = data_path / dir_name
+        if dir_path.exists():
+            files_count = len(list(dir_path.glob("*")))
+            print(f"  ✓ {dir_name}: {files_count} files")
+            existing_dirs.append(dir_name)
+        else:
+            print(f"  ✗ {dir_name}: không tìm thấy")
+    
+    return len(existing_dirs) > 0
+
+def main():
+    """Main function"""
+    print("=" * 60)
+    print("🎯 UW-Madison GI Tract Dataset Downloader")
+    print("=" * 60)
+    
+    # 1. Kiểm tra Kaggle API
+    if not setup_kaggle_api():
+        print("\n⚠️  Vui lòng cài đặt Kaggle API trước")
+        return False
+    
+    # 2. Kiểm tra Kaggle credentials
+    if not check_kaggle_credentials():
+        print("\n⚠️  Vui lòng cấu hình Kaggle credentials")
+        return False
+    
+    # 3. Tải dataset
+    if not download_dataset():
+        return False
+    
+    # 4. Giải nén dataset
+    if not extract_dataset():
+        return False
+    
+    # 5. Kiểm tra dataset
+    if not verify_dataset():
+        print("\n⚠️  Dataset có thể bị lỗi, vui lòng kiểm tra thủ công")
+        return False
+    
+    print("\n" + "=" * 60)
+    print("✅ Dataset đã sẵn sàng! Tiếp theo:")
+    print("   1. Chạy: python prepare_dataset.py")
+    print("   2. Sau đó: python train.py")
+    print("=" * 60)
+    
+    return True
+
+if __name__ == "__main__":
+    success = main()
+    exit(0 if success else 1)

prepare_dataset.py

@@ -0,0 +1,173 @@
+"""
+Script chuẩn bị dataset: chia train/val/test, tạo masks từ RLE encoding
+"""
+
+import os
+import json
+import numpy as np
+from pathlib import Path
+from PIL import Image
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import warnings
+warnings.filterwarnings('ignore')
+
+class DatasetPreparator:
+    def __init__(self, data_dir="./data", output_dir="./prepared_data"):
+        self.data_dir = Path(data_dir)
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+        
+        # Tạo subdirectories
+        self.train_images_dir = self.output_dir / "train_images"
+        self.train_masks_dir = self.output_dir / "train_masks"
+        self.val_images_dir = self.output_dir / "val_images"
+        self.val_masks_dir = self.output_dir / "val_masks"
+        self.test_images_dir = self.output_dir / "test_images"
+        self.test_masks_dir = self.output_dir / "test_masks"
+        
+        for dir_path in [self.train_images_dir, self.train_masks_dir, 
+                        self.val_images_dir, self.val_masks_dir,
+                        self.test_images_dir, self.test_masks_dir]:
+            dir_path.mkdir(parents=True, exist_ok=True)
+    
+    @staticmethod
+    def rle_decode(mask_rle, shape=(137, 236)):
+        """Giải mã RLE encoding thành mask"""
+        if pd.isna(mask_rle):
+            return np.zeros(shape[0] * shape[1], dtype=np.uint8)
+        
+        s = mask_rle.split()
+        starts, lengths = [np.asarray(x, dtype=int) for (x, y) in 
+                          zip(s[0:None:2], s[1:None:2])]
+        starts -= 1
+        ends = starts + lengths
+        
+        img = np.zeros(shape[0] * shape[1], dtype=np.uint8)
+        for lo, hi in zip(starts, ends):
+            img[lo:hi] = 1
+        return img.reshape(shape[::-1]).T
+    
+    def create_segmentation_mask(self, image_id, df_masks):
+        """Tạo mask phân đoạn từ dữ liệu RLE"""
+        height, width = 137, 236
+        mask = np.zeros((height, width), dtype=np.uint8)
+        
+        # Các class: 1=large_bowel, 2=small_bowel, 3=stomach
+        class_mapping = {'large_bowel': 1, 'small_bowel': 2, 'stomach': 3}
+        
+        for idx, row in df_masks[df_masks['id'] == image_id].iterrows():
+            organ_class = class_mapping.get(row['organ'], 0)
+            if organ_class > 0:
+                rle_mask = self.rle_decode(row['segmentation'], shape=(height, width))
+                mask[rle_mask == 1] = organ_class
+        
+        return mask
+    
+    def process_dataset(self, train_size=0.8, val_size=0.1):
+        """Xử lý toàn bộ dataset"""
+        print("\n📊 Đang chuẩn bị dataset...")
+        
+        # 1. Tìm các ảnh huấn luyện
+        if (self.data_dir / "train_images").exists():
+            train_images = sorted(list((self.data_dir / "train_images").glob("*.png")))
+            print(f"✓ Tìm thấy {len(train_images)} ảnh huấn luyện")
+        else:
+            print("✗ Không tìm thấy thư mục train_images")
+            return False
+        
+        # 2. Load RLE masks nếu có
+        train_masks_csv = self.data_dir / "train_masks.csv"
+        if train_masks_csv.exists():
+            df_masks = pd.read_csv(train_masks_csv)
+            print(f"✓ Load {len(df_masks)} mask annotations")
+            has_masks = True
+        else:
+            print("⚠️  Không tìm thấy train_masks.csv, bỏ qua giải mã RLE")
+            has_masks = False
+        
+        # 3. Chia train/val/test
+        image_ids = [img.stem for img in train_images]
+        train_ids, test_ids = train_test_split(
+            image_ids, test_size=(1-train_size), random_state=42
+        )
+        train_ids, val_ids = train_test_split(
+            train_ids, test_size=val_size/(train_size), random_state=42
+        )
+        
+        print(f"  Train: {len(train_ids)}, Val: {len(val_ids)}, Test: {len(test_ids)}")
+        
+        # 4. Copy ảnh và tạo masks
+        dataset_splits = {
+            'train': (train_ids, self.train_images_dir, self.train_masks_dir),
+            'val': (val_ids, self.val_images_dir, self.val_masks_dir),
+            'test': (test_ids, self.test_images_dir, self.test_masks_dir)
+        }
+        
+        for split_name, (ids, images_dir, masks_dir) in dataset_splits.items():
+            print(f"\n  📁 Xử lý {split_name} set ({len(ids)} ảnh)...")
+            
+            for i, img_id in enumerate(ids):
+                # Copy ảnh
+                src_img = self.data_dir / "train_images" / f"{img_id}.png"
+                if src_img.exists():
+                    dst_img = images_dir / f"{img_id}.png"
+                    Image.open(src_img).save(dst_img)
+                
+                # Tạo mask
+                if has_masks:
+                    mask = self.create_segmentation_mask(img_id, df_masks)
+                    mask_img = Image.fromarray(mask)
+                    mask_img.save(masks_dir / f"{img_id}_mask.png")
+                
+                if (i + 1) % max(1, len(ids) // 5) == 0 or i == 0:
+                    print(f"    → {i+1}/{len(ids)} hoàn thành")
+        
+        # 5. Lưu split info
+        split_info = {
+            'train': train_ids,
+            'val': val_ids,
+            'test': test_ids
+        }
+        
+        with open(self.output_dir / "split.json", 'w') as f:
+            json.dump(split_info, f, indent=2)
+        
+        print(f"\n✓ Split info lưu tại: {self.output_dir / 'split.json'}")
+        
+        return True
+    
+    def get_dataset_statistics(self):
+        """Thống kê dataset"""
+        print("\n📈 Thống kê dataset:")
+        
+        for split_dir in [self.train_images_dir, self.val_images_dir, self.test_images_dir]:
+            split_name = split_dir.parent.name.replace('_images', '')
+            num_images = len(list(split_dir.glob("*.png")))
+            total_size_mb = sum(f.stat().st_size for f in split_dir.glob("*.png")) / (1024*1024)
+            print(f"  {split_name:8} - {num_images:5} ảnh ({total_size_mb:8.2f} MB)")
+
+def main():
+    print("=" * 60)
+    print("🎯 Dataset Preparation Tool")
+    print("=" * 60)
+    
+    preparator = DatasetPreparator(
+        data_dir="./data",
+        output_dir="./prepared_data"
+    )
+    
+    if preparator.process_dataset():
+        preparator.get_dataset_statistics()
+        
+        print("\n" + "=" * 60)
+        print("✅ Dataset đã được chuẩn bị! Tiếp theo:")
+        print("   python train.py --data ./prepared_data")
+        print("=" * 60)
+        return True
+    
+    return False
+
+if __name__ == "__main__":
+    success = main()
+    exit(0 if success else 1)

requirements.txt

@@ -0,0 +1,29 @@
+# PyTorch (CPU version - change to GPU if needed)
+--find-links https://download.pytorch.org/whl/torch_stable.html
+torch==2.0.0+cpu
+torchvision==0.15.0
+
+# Core dependencies
+transformers==4.30.2
+gradio==4.48.1
+numpy>=1.21.0
+Pillow>=9.0.0
+
+# Data processing
+pandas>=1.3.0
+scikit-learn>=1.0.0
+
+# Visualization
+matplotlib>=3.4.0
+
+# Utilities
+tqdm>=4.62.0
+opencv-python>=4.5.0
+
+# Optional: Weights & Biases (for experiment tracking)
+# wandb>=0.13.0
+
+# Optional: For GPU support, replace torch+cpu with:
+# torch==2.0.0
+# torchaudio==2.0.0
+# torchvision==0.15.0