Update app.py

app.py

@@ -1,43 +1,43 @@
-import gradio as gr
-from ultralytics import YOLO
-import cv2
-import numpy as np
-
-# Load the trained model
-model_path = 'runs\\detect\\train6\\weights\\best.pt'  # Replace with the path to your trained .pt file
-model = YOLO(model_path)
-
-# Function to perform inference on an image
-def infer_image(image):
-    # Convert the image from BGR to RGB
-    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    
-    # Perform inference
-    results = model(image_rgb)
-    
-    # Extract results and annotate image
-    for result in results:
-        for box in result.boxes:
-            x1, y1, x2, y2 = box.xyxy[0]
-            cls = int(box.cls[0])
-            conf = float(box.conf[0])
-            
-            # Draw bounding box
-            cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
-            # Draw label
-            label = f'{model.names[cls]} {conf:.2f}'
-            cv2.putText(image, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
-    
-    return image
-
-# Create Gradio interface
-iface = gr.Interface(
-    fn=infer_image, 
-    inputs=gr.Image(type="numpy", label="Upload an Image"),
-    outputs=gr.Image(type="numpy", label="Annotated Image"),
-    title="YOLOv8 Inference",
-    description="Upload an image to get object detection results using YOLOv8."
-)
-
-# Launch the app
-iface.launch()
+import gradio as gr
+from ultralytics import YOLO
+import cv2
+import numpy as np
+
+# Load the trained model
+model_path = 'best.pt'  # Replace with the path to your trained .pt file
+model = YOLO(model_path)
+
+# Function to perform inference on an image
+def infer_image(image):
+    # Convert the image from BGR to RGB
+    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    
+    # Perform inference
+    results = model(image_rgb)
+    
+    # Extract results and annotate image
+    for result in results:
+        for box in result.boxes:
+            x1, y1, x2, y2 = box.xyxy[0]
+            cls = int(box.cls[0])
+            conf = float(box.conf[0])
+            
+            # Draw bounding box
+            cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
+            # Draw label
+            label = f'{model.names[cls]} {conf:.2f}'
+            cv2.putText(image, label, (int(x1), int(y1) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+    
+    return image
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=infer_image, 
+    inputs=gr.Image(type="numpy", label="Upload an Image"),
+    outputs=gr.Image(type="numpy", label="Annotated Image"),
+    title="YOLOv8 Inference",
+    description="Upload an image to get object detection results using YOLOv8."
+)
+
+# Launch the app
+iface.launch()