Upload 4 files

.gitignore

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+flaggedd/
+*.py 
+*.png
+*.mp4
+*.mkv
+graadio_cached_examples/
+
+

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+from ultralytics import YOLO
+
+# Load the YOLOv8 model
+model = YOLO("yolov8n.pt")  # Ensure this file is in the same directory as app.py on Hugging Face
+
+# Define the inference function
+def predict(image):
+    # Convert the input image from RGB to BGR (OpenCV format)
+    image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    
+    # Run the model on the input image
+    results = model(image_bgr)
+    
+    # Extract the result image with detections
+    annotated_image = results[0].plot()  # Returns a BGR image with annotations
+    
+    # Convert the image back to RGB for displaying in Gradio
+    annotated_image_rgb = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
+    
+    return annotated_image_rgb
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="numpy", label="Upload an Image"),
+    outputs=gr.Image(type="numpy", label="Detected Objects"),
+    title="YOLOv8 Object Detection",
+    description="Upload an image to detect objects with YOLOv8 model."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()

requirements.txt

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+torch==1.13.1+cu118  # Ensure compatibility with your GPU and cu118
+torchvision==0.14.1+cu118  # Ensure compatibility with the specified torch version
+ultralytics  # For YOLOv8 models
+transformers  # For Hugging Face model interfacing
+huggingface_hub  # For Hugging Face model uploading and management
+pandas  # For data manipulation
+numpy  # For numerical operations
+opencv-python-headless  # For YOLOv8 image processing
+scipy  # For scientific computations
+requests  # For handling HTTP requests
+gradio
+Pillow

yolov8n.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f59b3d833e2ff32e194b5bb8e08d211dc7c5bdf144b90d2c8412c47ccfc83b36
+size 6549796