Update app.py

app.py

@@ -1,43 +1,43 @@
 import gradio as gr
-from ultralytics import YOLO
 import torch
-import numpy as np
+from ultralytics import YOLO
 import cv2
+import numpy as np
 
-# Load the trained YOLO model from your Hugging Face Model Hub
-MODEL_REPO = "Alaaeldin/yolo-demo"  # Replace with your actual model repo
-model = YOLO("yolov8n.pt")  # Change to your trained model file if needed
+# Load the trained YOLO model
+model = YOLO("yolo11n.pt")  # Ensure this matches the model file in your repo
 
-# Define the function for image prediction
+# Define the function for making predictions
 def predict(image):
-    results = model(image)
+    # Convert to OpenCV format
+    img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
 
-    # Extract bounding boxes, labels, and confidence scores
-    detections = results[0].boxes.xyxy.cpu().numpy()
-    labels = results[0].boxes.cls.cpu().numpy()
-    confidences = results[0].boxes.conf.cpu().numpy()
-
-    # Load class names
-    class_names = ["apples", "banana", "cherry", "grapes", "oranges"]
+    # Run YOLOv8 inference
+    results = model(img)
 
     # Draw bounding boxes on the image
-    for i, (box, label, conf) in enumerate(zip(detections, labels, confidences)):
-        x1, y1, x2, y2 = map(int, box)
-        label_text = f"{class_names[int(label)]}: {conf:.2f}"
-        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
-        cv2.putText(image, label_text, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
-
-    return image
-
-# Create Gradio Interface
-demo = gr.Interface(
+    for r in results:
+        for box in r.boxes:
+            x1, y1, x2, y2 = map(int, box.xyxy[0])  # Get bounding box coordinates
+            conf = float(box.conf[0])  # Confidence score
+            cls = int(box.cls[0])  # Class index
+            
+            label = f"{model.names[cls]} {conf:.2f}"
+            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(img, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+    # Convert back to PIL format
+    return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+# Create the Gradio interface
+iface = gr.Interface(
     fn=predict,
-    inputs=gr.Image(type="numpy"),
-    outputs=gr.Image(type="numpy"),
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
     title="YOLOv8 Fruit Detection",
-    description="Upload an image to detect fruits using YOLOv8.",
+    description="Upload an image and the model will detect objects."
 )
 
-# Run the app
+# Launch the Gradio app
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()