Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import spaces
 from huggingface_hub import hf_hub_download
 import os
+import cv2  # Import OpenCV
 
 # Function to download models from Hugging Face
 def download_models(model_id):
@@ -14,37 +15,38 @@ def download_models(model_id):
 
 
 @spaces.GPU
-def yolov9_inference(img_path, model_id, image_size, conf_threshold, iou_threshold):
+
+def yolov9_inference(img_path, model_id, conf_threshold, iou_threshold):
     """
     Load a YOLOv9 model, configure it, perform inference on an image, and optionally adjust 
     the input size and apply test time augmentation.
     
-    :param model_path: Path to the YOLOv9 model file.
+    :param model_id: Identifier of the YOLOv9 model.
     :param conf_threshold: Confidence threshold for NMS.
     :param iou_threshold: IoU threshold for NMS.
     :param img_path: Path to the image file.
-    :param size: Optional, input size for inference.
-    :return: A tuple containing the detections (boxes, scores, categories) and the results object for further actions like displaying.
+    :return: Output image with detections.
     """
-    # Import YOLOv9
-    import yolov9
+    # Load the image from the file path
+    image = cv2.imread(img_path)  # Use OpenCV to read the image
     
     # Load the model
     model_path = download_models(model_id)
-    model = yolov9.load(model_path, device="cpu")
+    model = yolov9.load(model_path, device="cpu")  # Change device if needed
     
     # Set model parameters
     model.conf = conf_threshold
     model.iou = iou_threshold
     
     # Perform inference
-    results = model(img_path)
+    results = model(image)  # Pass the loaded image instead of the file path
 
     # Optionally, show detection bounding boxes on image
     output = results.render()
     
     return output[0]
 
+