Update app.py

app.py

@@ -5,22 +5,22 @@ from ultralytics.utils.plotting import Annotator
 
 import gradio as gr
 
-cell_detector = YOLO("./weights/yolo_uninfected_cells.pt")
-redetr_detector = YOLO("./weights/redetr_infected_cells.pt")
+cell_detector = YOLO("./weights/yolo_uninfected_cell.pt")
 yolo_detector = YOLO("./weights/yolo_infected_cells.pt")
+# redetr_detector = YOLO("./weights/yolo_infected_cells.pt")
 
-models = {"Yolo V11": yolo_detector, "Real Time Detection Transformer": redetr_detector}
-classes = {"Yolo V11": [0], "Real Time Detection Transformer": [1]}
+models = {"Yolo V11": yolo_detector, "Real Time Detection Transformer": yolo_detector}
+# classes = {"Yolo V11": [0], "Real Time Detection Transformer": [1]}
 
 
-def inference(image, model) -> Tuple[str, str, str]:
+def inference(image, model, conf) -> Tuple[str, str, str]:
     bboxes = []
     labels = []
     healthy_cell_count = 0
     unhealthy_cell_count = 0
-    cells_results = cell_detector.predict(image, conf=0.5)
+    cells_results = cell_detector.predict(image, conf=0.4)
     selected_model_results = models[model].predict(
-        image, conf=0.05, classes=classes[model], imgsz=1024
+        image, conf=conf
     )
 
     for cell_result in cells_results:
@@ -53,6 +53,7 @@ ifer = gr.Interface(
         gr.Dropdown(
             choices=["Yolo V11", "Real Time Detection Transformer"], multiselect=False
         ),
+        gr.Slider(minimum=0.01, maximum=1)
     ],
     outputs=[
         gr.Image(label="Output Image", type="numpy"),
@@ -62,4 +63,4 @@ ifer = gr.Interface(
     title="Blood Cancer Cell Detection and Counting"
 )
 
-ifer.launch(share=True)
+ifer.launch(share=True)