add app.py

app.py

@@ -59,8 +59,6 @@ def get_figure(in_pil_img, in_results):
 
 
 def infer(in_model, in_threshold, in_pil_img):
-    print(type(in_pil_img))
-    print(threshold)
     inputs = image_processor_tiny(images=in_pil_img, return_tensors="pt")
     outputs = model_tiny(**inputs)
 
@@ -81,23 +79,36 @@ def infer(in_model, in_threshold, in_pil_img):
     return output_pil_img
 
 
-#from transformers.models.flava import modeling_flava
-with gr.Blocks(css=".gradio-container {background:lightyellow;color:red;}", title="テスト"
+with gr.Blocks(title="YOLOS Object Detection - ClassCat",
+                    css=".gradio-container {background:lightyellow;}"
                ) as demo:
     #sample_index = gr.State([])
 
-    gr.HTML('<div style="font-size:12pt; text-align:center; color:yellow;">MNIST 分類器</div>')
+    gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">YOLOS Object Detection</div>""")
 
-    model = gr.Radio(["detr-resnet-50", "detr-resnet-101"], value="detr-resnet-50")
+    gr.HTML("""<h4 style="color:navy;">1. Select a model.</h4>""")
+
+    model = gr.Radio(["yolos-tiny", "yolos-small"], value="yolos-tiny")
+
+    gr.HTML("""<br/>""")
+    gr.HTML("""<h4 style="color:navy;">2-a. Select an example by clicking a thumbnail below.</h4>""")
+    gr.HTML("""<h4 style="color:navy;">2-b. Or upload an image by clicking on the canvas.</h4>""")
 
     with gr.Row():
-        input_image = gr.Image(label="", type="pil")
-        output_image = gr.Image(type="pil")
-    
+        input_image = gr.Image(label="Input image", type="pil")
+        output_image = gr.Image(label="Output image with predicted instances", type="pil")
+
+    gr.Examples(['samples/cats.jpg', 'samples/detectron2.png', 'samples/cat.jpg', 'samples/hotdog.jpg'], inputs=input_image)
+
+    gr.HTML("""<br/>""")
+    gr.HTML("""<h4 style="color:navy;">3. Set threshold value (default to 0.9)</h4>""")
 
     threshold = gr.Slider(0, 1.0, value=0.9, label='threshold')
 
-    send_btn = gr.Button("予測する")
+    gr.HTML("""<br/>""")
+    gr.HTML("""<h4 style="color:navy;">4. Then, click "Infer" button to predict object instances. It will take about 10 seconds (on cpu)</h4>""")
+
+    send_btn = gr.Button("Infer")
     send_btn.click(fn=infer, inputs=[model, threshold, input_image], outputs=[output_image])
 
 #demo.queue()