Medical-VQA / requirements.txt
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Deploy Gradio notebook-style Medical VQA app
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# Medical VQA β€” requirements.txt
# Python 3.10+ | CUDA 11.8+ | Tested on: RTX 4090, T4, A100
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# ── Deep Learning Core ───────────────────────────────────────────────────
torch>=2.1.0
torchvision>=0.16.0
torchaudio>=2.1.0 # cαΊ§n cho mα»™t sα»‘ HF pipeline
# ── Medical Imaging ──────────────────────────────────────────────────────
torchxrayvision>=1.1.0 # DenseNet-121 XRV pretrained weights
opencv-python-headless>=4.8.0 # CLAHE preprocessing (headless = khΓ΄ng cαΊ§n GUI)
Pillow>=10.0.0
# ── HuggingFace Ecosystem ────────────────────────────────────────────────
transformers>=4.38.0 # PhoBERT, LLaVA-Med, SFTTrainer
huggingface_hub>=0.20.0
datasets>=2.18.0
tokenizers>=0.15.0
accelerate>=0.27.0 # Mixed precision, device mapping
sentencepiece>=0.1.99 # PhoBERT tokenizer
peft>=0.9.0 # LoRA cho LLaVA-Med (B2)
trl>=0.8.1 # SFTTrainer + DPOTrainer
# ── Quantization (B2 / DPO 4-bit) ───────────────────────────────────────
bitsandbytes>=0.43.0 # 4-bit quantization cho LLaVA-Med
# ── Vietnamese NLP ───────────────────────────────────────────────────────
underthesea>=6.8.0 # Tokenization tiαΊΏng Việt
# ── NLP Metrics ──────────────────────────────────────────────────────────
nltk>=3.8.1
bert-score>=0.3.13
rouge-score>=0.1.2
evaluate>=0.4.1 # HuggingFace evaluate (BLEU, METEOR)
# ── Data & Scientific ────────────────────────────────────────────────────
numpy>=1.26.0
pandas>=2.1.0
scikit-learn>=1.4.0
scipy>=1.12.0
# ── Visualization ────────────────────────────────────────────────────────
matplotlib>=3.8.0
seaborn>=0.13.0
gradio>=4.44.0
# ── Experiment Tracking ──────────────────────────────────────────────────
wandb>=0.16.0
# ── Config & Utilities ───────────────────────────────────────────────────
pyyaml>=6.0.1
python-dotenv>=1.0.1
tqdm>=4.66.0
requests>=2.31.0
# ── Jupyter (local development) ──────────────────────────────────────────
jupyter>=1.0.0
ipython>=8.22.0
ipywidgets>=8.1.0