UPDATED app.py and requirements

app.py

@@ -1,26 +1,16 @@
 import gradio as gr
 from ultralytics import YOLO
-from PIL import Image
 
-# Load the YOLO model
-model = YOLO("best_int8_openvino_model", task="detect")  # Update with your model's path
+# Load your YOLO model (ensure correct path to your model)
+model = YOLO("best_int8_openvino_model.pt", task="detect")
 
-# Function to process the uploaded image
-def detect_objects(image):
-    # Convert the uploaded image to YOLO-compatible format
-    results = model(image)  # Perform inference
-    annotated_image = results[0].plot()  # Get annotated image as a NumPy array
-    return Image.fromarray(annotated_image[..., ::-1])  # Convert BGR to RGB for PIL
+# Define the prediction function
+def predict_image(image):
+    results = model(image)
+    return results.plot()  # Returns the image with detected objects
 
 # Create the Gradio interface
-demo = gr.Interface(
-    fn=detect_objects,
-    inputs=gr.inputs.Image(type="pil"),  # Accept image uploads
-    outputs="image",  # Return the processed image
-    title="Object Detection App",
-    description="Upload an image to detect objects using a YOLO model."
-)
-
-# Launch the app
-if __name__ == "__main__":
-    demo.launch()
+demo = gr.Interface(fn=predict_image, 
+                    inputs=gr.Image(type="pil"),  # Updated syntax
+                    outputs=gr.Image())  # Outputs an image
+demo.launch()

requirements.txt

@@ -1,2 +1,2 @@
 ultralytics
-gradio
+huggingface_hub