Update app.py

app.py

@@ -8,18 +8,18 @@ m_raw_model = YOLO("M-Raw.pt")
 n_raw_model = YOLO("N-Raw.pt")
 s_raw_model = YOLO("S-Raw.pt")
 
-def snap(image, model, conf):
+def snap(image, model, conf, iou):
     # Convert the image to a numpy array
     image = np.array(image)
 
     # Run the selected model
     results = None
     if model == "M-Raw":
-        results = m_raw_model(image, conf=conf)
+        results = m_raw_model(image, conf=conf, iou=iou)
     elif model == "N-Raw":
-        results = n_raw_model(image, conf=conf)
+        results = n_raw_model(image, conf=conf, iou=iou)
     elif model == "S-Raw":
-        results = s_raw_model(image, conf=conf)
+        results = s_raw_model(image, conf=conf, iou=iou)
 
     # Draw the bounding boxes
     resulting_image = results.render()
@@ -31,15 +31,15 @@ def snap(image, model, conf):
     labels = results.pandas().xyxy[0]["name"].values
 
     # Sort the labels by their x-value first and then by their y-value
-    print(labels)
+    # print(labels)
 
-    return [resulting_image, labels]
+    return [resulting_image]
 
 
 demo = gr.Interface(
     snap,
-    [gr.Image(source="webcam", tool=None, streaming=True), gr.inputs.Radio(["M-Raw", "N-Raw", "S-Raw"]), gr.inputs.Slider(0.0, 1.0, 0.5, 0.1, "Confidence")],
-    ["image", "text"],
+    [gr.Image(source="webcam", tool=None, streaming=True), gr.inputs.Radio(["M-Raw", "N-Raw", "S-Raw"]), gr.inputs.Slider(0.3, 1.0, "Classifier Confidence Threshold", value=0.6), gr.inputs.Slider(0.3, 1.0, "IoU Confidence Threshold", value=0.7)],
+    ["image"],
     title="Baybayin Instance Detection"
 )