cleanup

app.py

@@ -50,12 +50,12 @@ def apply_classifier(img, context={}, detect=False):
     result = {}
     if detect:
         if not context.get("clf", None):
-            context["clf"] = pipeline("image-classification")
+            context["clf"] = pipeline(
+                "image-classification", model="google/vit-base-patch16-224")
         result = context["clf"](Image.fromarray((img*255).astype(np.uint8)))
     else:
         result = [{"score": 0., "label": "No classification"}]*5
     context["result"] = result
-    # return result
 
 
 def display_result(context={}):
@@ -64,10 +64,14 @@ def display_result(context={}):
         SingleCurve(
             x=[id, id, id+1, id+1],
             y=[0, r['score'], r['score'], 0],
-            label=r["label"], style="-"
+            label=r["label"], linestyle="-",
         )
         for id, r in enumerate(result_dict)]
-    result_curves = Curve(curves, ylim=[0, 1])
+    result_curves = Curve(
+        curves,
+        ylim=[0, 1],
+        title=f"{result_dict[0]['label']} ({result_dict[0]['score']:.2%})"
+    )
     return result_curves
 
 # -------------------
@@ -87,7 +91,7 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("-b", "--backend", type=str,
                         choices=BACKEND_OPTIONS, default=BACKEND_OPTIONS[0])
-    args = parser.parse_args() 
+    args = parser.parse_args()
     md_description = "# 🔍 EXAMPLE Interactive-pipe + machine learning \n"
     md_description += "```python\n"+open(__file__, 'r').read()+"```\n"
     interactive_pipeline(gui=args.backend, markdown_description=md_description)(