Update app.py

app.py

@@ -1,3 +1,14 @@
+def setup_cpu_environment():
+    os.environ['CUDA_VISIBLE_DEVICES'] = ''
+    
+    torch.set_num_threads(4)  # 根据你的 CPU 核心数调整
+    
+    os.environ['TOKENIZERS_PARALLELISM'] = 'false'
+    
+    os.environ['TRANSFORMERS_CACHE'] = './cache'
+
+setup_cpu_environment()
+
 import gradio as gr
 import sys
 import os
@@ -146,7 +157,7 @@ with gr.Blocks(title="RadEval: A framework for radiology text evaluation", theme
     gr.Markdown(
         """
         # 🩺 RadEval:  A framework for radiology text evaluation
-        [Github](https://pypi.org/project/RadEval/) | [PyPI]() | [Video](https://justin13601.github.io/files/radeval.mp4) |[arXiv]() | [RadEvalModernBERT Model](https://huggingface.co/IAMJB/RadEvalModernBERT) | [Expert Dataset]()
+        [Github](https://github.com/jbdel/RadEval) | [PyPI](https://pypi.org/project/RadEval/) | [Video](https://justin13601.github.io/files/radeval.mp4) |[arXiv]() | [RadEvalModernBERT Model](https://huggingface.co/IAMJB/RadEvalModernBERT) | [Expert Dataset]()
 
         **RadEval** is a lightweight, extensible framework for **evaluating radiology reports** using both standard NLP metrics (e.g. BLEU, ROUGE, BERTScore) and **radiology-specific measures** (e.g. RadGraph, CheXbert, GREEN). Whether you're benchmarking generation systems or validating clinical correctness, RadEval offers **comprehensive and interpretable** metrics out of the box.