Update app.py

app.py

@@ -6,9 +6,8 @@ def inference(path:str, threshold:float=0.6):
     print("trying inference with path", path)
     if path is None:
         return None,0
-    model = YOLO('yolov8m.pt') # Caution new API since Ultralytics 8.0.43
-    # [0]  # only considering class 'person' and not the 79 other classes...
-    outputs = model.predict(source=path, classes= [0], show=False, conf=threshold) # new API with Ultralytics 8.0.43. Accepts 'show', 'classes', 'stream', 'conf' (default is 0.25)
+    model = YOLO('yolo8n_small.pt') # Caution new API since Ultralytics 8.0.43
+    outputs = model.predict(source=path, show=False, conf=threshold) # new API with Ultralytics 8.0.43. Accepts 'show', 'classes', 'stream', 'conf' (default is 0.25)
     image = cv2.imread(path)
     counter = 0
     for output in outputs:  # mono item batch
@@ -33,9 +32,9 @@ def inference(path:str, threshold:float=0.6):
 gr.Interface(
     fn = inference,
     inputs = [ gr.components.Image(type="filepath", label="Input"), gr.Slider(minimum=0.5, maximum=0.9, step=0.05, value=0.7, label="Confidence threshold") ],
-    outputs = [ gr.components.Image(type="numpy", label="Output"), gr.Label(label="nb of persons detected for given confidence threshold") ],
+    outputs = [ gr.components.Image(type="numpy", label="Output"), gr.Label(label="Number of legos detected for given confidence threshold") ],
     title="Person detection with YOLO v8",
     description="Person detection, you can tweak the corresponding confidence threshold. Good results even when face not visible. New API since Ultralytics 8.0.43.",
-    examples=[ ['data/businessmen-612.jpg'], ['data/businessmen-back.jpg']],
+    examples=[ ['sample1.jpg'], ['sample2.jpg']],
     allow_flagging="never"
 ).launch(debug=True, enable_queue=True)