company label model

app.py

@@ -6,7 +6,6 @@ from sahi import AutoDetectionModel
 from PIL import Image
 import plotly.graph_objects as go
 import torch
-import spaces
 
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 
@@ -15,7 +14,7 @@ class Detection:
 
     def __init__(self):
         # Set the model path and confidence threshold
-        yolov8_model_path = "./model/best.pt"  # Update to your model path
+        yolov8_model_path = "./model/company_model.pt"  # Update to your model path
 
         # Initialize the AutoDetectionModel
         self.model = AutoDetectionModel.from_pretrained(
@@ -113,7 +112,7 @@ class Detection:
                     name=category,
                     marker_color=self.get_color(category),  # Use associated color
                     opacity=1,
-                    nbinsx=10  # Number of bins
+                    nbinsx=50  # Number of bins
                 )
                 fig.add_trace(histogram_data)
 
@@ -209,7 +208,6 @@ def upload_image(image):
     """Process the uploaded image (if needed) and display it.""" 
     return image
 
-@spaces.GPU
 def apply_detection(image):
     """Run object detection on the uploaded image and return the annotated image.""" 
     # Convert image from PIL to NumPy array