Update app.py

Updated model selection code.

app.py

@@ -1,3 +1,53 @@
+# import gradio as gr
+# import cv2
+# from ultralytics import YOLO
+
+# model0 = YOLO('yolov8.pt')
+# model1 = YOLO('yolov8.pt')
+# model2 = YOLO('yolov8.pt')
+# model3 = YOLO('yolov8.pt')
+# model4 = YOLO('yolov8.pt')
+# model5 = YOLO('yolov8.pt')
+
+# models = [model0, model1, model2, model3, model4, model5]
+# model_names = ["Model 0", "Model 1", "Model 2", "Model 3", "Model 4", "Model 5"]
+
+# def show_preds_image(image, model_selection=0):
+#     img = image.read()
+#     outputs = models[model_selection].predict(source=img)
+#     results = outputs[0].cpu().numpy()
+#     for i, det in enumerate(results.boxes.xyxy):
+#         cv2.rectangle(
+#             img,
+#             (int(det[0]), int(det[1])),
+#             (int(det[2]), int(det[3])),
+#             color=(0, 0, 255),
+#             thickness=2,
+#             lineType=cv2.LINE_AA
+#         )
+#     return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+# interface_image = gr.Interface(
+#     fn=show_preds_image,
+#     inputs=[
+#         gr.inputs.Image(type="file", label="Input Image"),
+#         gr.inputs.Dropdown(
+#             choices=[(name, idx) for idx, name in enumerate(model_names)],
+#             label="Select Model",
+#             default=0
+#         )
+#     ],
+#     outputs=gr.outputs.Image(type="numpy", label="Output Image"),
+#     title="Panicle detector app",
+# )
+
+# interface_image.launch()
+
+
+
+
+
+
 import gradio as gr
 import cv2
 import requests
@@ -5,6 +55,8 @@ import os
 
 from ultralytics import YOLO
 
+
+
 model0 = YOLO('yolov8.pt')
 model1 = YOLO('yolov8.pt')
 model2 = YOLO('yolov8.pt')
@@ -12,13 +64,23 @@ model3 = YOLO('yolov8.pt')
 model4 = YOLO('yolov8.pt')
 model5 = YOLO('yolov8.pt')
 
-models = [model0, model1, model2, model3, model4, model5]
-model_names = ["Model 0", "Model 1", "Model 2", "Model 3", "Model 4", "Model 5"]
+models = []
+models.append(model0)
+models.append(model1)
+models.append(model2)
+models.append(model3)
+models.append(model4)
+models.append(model5)
+
+
+path  = [['flowering.png']]
 
 
-def show_preds_image(image_path, model_selection=0):
+
+
+def show_preds_image(image_path, selection):
     image = cv2.imread(image_path)
-    outputs = models[model_selection].predict(source=image_path)
+    outputs = models[selection].predict(source=image_path)
     results = outputs[0].cpu().numpy()
     for i, det in enumerate(results.boxes.xyxy):
         cv2.rectangle(
@@ -31,23 +93,26 @@ def show_preds_image(image_path, model_selection=0):
         )
     return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
 
-
-inputs_image = [
-    gr.inputs.Image(type="file", label="Input Image"),
-    gr.inputs.Dropdown(
-        choices=[(name, idx) for idx, name in enumerate(model_names)],
-        label="Select Model",
-        default=0
-    )
+inputs = [
+    gr.components.Image(type="filepath", label="Input Image"),
+    gr.components.Dropdown(choices=[str(i) for i in range(len(models))], label="Select Model", type="index"),
 ]
 outputs_image = [
-    gr.outputs.Image(type="numpy", label="Output Image"),
+    gr.components.Image(type="numpy", label="Output Image"),
 ]
+model_select = []
 interface_image = gr.Interface(
     fn=show_preds_image,
-    inputs=inputs_image,
+    inputs=inputs,
     outputs=outputs_image,
     title="Panicle detector app",
+    examples=path,
+    cache_examples=False,
 )
 
-interface_image.launch()
+
+
+gr.TabbedInterface(
+    [interface_image],
+    tab_names=['Image inference']
+).queue().launch()