added barebones example for viewer with slider

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.nii.gz filter=lfs diff=lfs merge=lfs -text

NOTES.md

@@ -7,4 +7,6 @@ https://github.com/gradio-app/gradio/issues/4511
 
 https://www.gradio.app/guides/custom-components-in-five-minutes
 
-https://www.gradio.app/guides/pdf-component-example
+https://www.gradio.app/guides/pdf-component-example
+
+https://github.com/gradio-app/gradio/issues/6821

README.md

@@ -1,5 +1,5 @@
 ---
-title: Papaya Image Viewer
+title: Not-A-Papaya Image Viewer
 emoji: 🏢
 colorFrom: pink
 colorTo: purple

app.py

@@ -1,22 +1,124 @@
-import gradio as gr
 
-head = f"""
+"""
 <script src="https://cdn.jsdelivr.net/npm/[email protected]/release/current/standard/papaya.min.js"></script>
 <link href="https://cdn.jsdelivr.net/npm/[email protected]/release/current/standard/papaya.min.css" rel="stylesheet">
-
-"""
-papayadiv = f"""
 <div class="papaya" data-params="params"></div>
+
+TODO:
+https://www.gradio.app/guides/custom-components-in-five-minutes
+https://github.com/gradio-app/gradio/issues/6821
+
 """
 
+
+import os
+import gradio as gr
+
+import numpy as np
+import PIL
+import SimpleITK as sitk
+
+MINVAL,MAXVAL = -1000,1000
+
+THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+example_image_path = os.path.join(THIS_DIR,'files','promis12-test-Case00.nii.gz') # from promise12
+example_mask_path = os.path.join(THIS_DIR,'files','promis12-test-Case00-mask.nii.gz') # from promise12
+
+mydict = {}
+
+def load_data(input1,input2):
+    global mydict
+    mydict['image_path'] = input1.name
+    mydict['mask_path'] = input2.name
+    img = sitk.GetArrayFromImage(sitk.ReadImage(mydict['image_path']))
+    mask = sitk.GetArrayFromImage(sitk.ReadImage(mydict['mask_path']))
+    img = ((img-MINVAL)/(MAXVAL-MINVAL)).clip(0,1)*255
+    mydict['img'] = img.astype(np.uint8)
+    mydict['mask'] = mask.astype(np.uint8)
+    return f"Welcome to Gradio, {input1.name,input2.name}!"
+
+def render(x, state):
+    print(x,state)
+    if len(mydict)==4:
+        if x > mydict['img'].shape[0]-1:
+            x = mydict['img'].shape[0]-1
+        if x < 0:
+            x = 0
+        img = mydict['img'][x,:,:]
+        mask = mydict['mask'][x,:,:]
+        im = PIL.Image.fromarray(img)
+        value = (im,[(mask,"prostate")])
+    else:
+        im = np.zeros(10,10)
+        value = (im,[])
+
+    return value,f'z-value: {x}'
+
 with gr.Blocks() as demo:
-    input_mic = gr.HTML(papayadiv)
-    demo.load(None,None,None,_js=head)
+    with gr.Column():
+        gr.Markdown(
+        """
+        # Hello World!
+        Start typing below to see the output.
+        """)
+    with gr.Row():
+        image_file = gr.File()
+        mask_file = gr.File()
+    
+    with gr.Column():
+        btn = gr.Button("upload")
+        slider = gr.Slider(0, 100, step=1)
+        state = gr.State(value=0)
+        out = gr.Textbox(label='text-comment')
+    with gr.Row():
+        myimage = gr.AnnotatedImage(height=512)
+    gr.Examples(
+        [[example_image_path,example_mask_path]],
+        [image_file, mask_file],
+    )
+    btn.click(fn=load_data, 
+        inputs=[image_file,mask_file], outputs=out,
+    )
+    slider.change(
+        render,
+        inputs=[slider, state],
+        outputs=[myimage,out],
+        api_name="hohoho"
+    )
 
-demo.launch(server_name='0.0.0.0')
+if __name__ == "__main__":
+    #demo.launch()
+    demo.launch(server_name='0.0.0.0')
 
 '''
-docker run --runtime=nvidia -it -u $(id -u):$(id -g) -p 7860:7860     -w $PWD -v /mnt:/mnt -v ~/.cache:/.cache pangyuteng/ml:tf-hf-latest bash
+
+docker run --runtime=nvidia -it -u $(id -u):$(id -g) -p 7860:7860 \
+    -w $PWD -v /mnt:/mnt -v ~/.cache:/.cache pangyuteng/ml:tf-hf-latest bash
 
 python app.py
+
+'''
+
+'''
+import numpy as np
+import SimpleITK as sitk
+
+sys.path.append(os.path.dirname(utils_py_file))
+from utils import inference
+
+model = keras.models.load_model(model_file,compile=False)
+
+def mainfunc(file_obj):
+    image_path = file_obj.name
+    tmpdir = os.path.dirname(image_path)
+    mask_path = os.path.join(tmpdir,"mask.nii.gz")
+    img_obj = sitk.ReadImage(image_path)
+
+    mask_obj = inference(model,img_obj)
+    sitk.WriteImage(mask_obj,mask_path)
+    return [image_path,mask_path]
+
+if __name__ == "__main__":
+
+    demo.queue().launch(debug=True,show_api=True,share=False)
 '''

files/promis12-test-Case00-mask.nii.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:14b159df22eb0b404a7a8851315a0269b37ad9bff1517577d681abb3514fc742
+size 7774

files/promis12-test-Case00.nii.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:813dda4c7e60436017707cf7d371c311a1401750681e338fe29dac6bb087185c
+size 2832200