Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import torch
+from transformers import pipeline, AutoModel, AutoTokenizer
+from monai.transforms import Compose, LoadImage, ScaleIntensity, EnsureChannelFirst
+import SimpleITK as sitk
+
+# 初始化组件
+llm = pipeline("text-generation", model="microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract")
+seg_model = AutoModel.from_pretrained("Project-MONAI/model-zoo/hnfnet_brats21").eval() 
+
+# 医学图像预处理
+preprocess = Compose([
+    LoadImage(image_only=True),
+    EnsureChannelFirst(channel_dim='no_channel'),
+    ScaleIntensity(minv=0.0, maxv=1.0)
+])
+
+def analyze_image(image_path, clinical_note):
+    # 生成分割提示
+    prompt = f"根据临床报告生成分割提示：{clinical_note}"
+    guidance = llm(prompt, max_length=200)[0]['generated_text']
+    
+    # 图像处理
+    img = preprocess(image_path)
+    
+    # 分割推理
+    with torch.no_grad():
+        seg = seg_model(img.unsqueeze(0))[0]
+    
+    # 后处理
+    result = sitk.GetArrayFromImage(seg.squeeze().numpy())
+    return (result > 0.5).astype(np.uint8), guidance
+
+# 创建交互界面
+demo = gr.Interface(
+    fn=analyze_image,
+    inputs=[
+        gr.File(label="上传DICOM/NIfTI文件"),
+        gr.Textbox(label="临床描述", placeholder="输入影像学检查报告...")
+    ],
+    outputs=[
+        gr.Image(label="分割结果", colormap="viridis"),
+        gr.Textbox(label="生成的分割提示")
+    ],
+    examples=[
+        ["assets/sample1.nii.gz", "左侧基底节区可见直径2cm占位，T1低信号，T2高信号"],
+        ["assets/sample2.dcm", "右肺上叶结节，边缘毛刺，考虑恶性肿瘤可能"]
+    ]
+)
+
+demo.launch()