Create app.py

app.py

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+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image
+import requests
+
+# Periksa apakah GPU tersedia dan pilih perangkat GPU jika ada
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# Step 1: Download the YOLO model from Hugging Face
+url = 'https://huggingface.co/Yudsky/pest-detection-yolo11/resolve/main/best.pt'
+model_path = 'best.pt'
+
+# Check if the model file already exists, if not, download it
+if not os.path.exists(model_path):
+    print("Downloading the model...")
+    response = requests.get(url)
+    with open(model_path, 'wb') as f:
+        f.write(response.content)
+    print("Download completed.")
+
+# Step 2: Load the model using YOLO from ultralytics
+# print("Loading the model...")
+model = YOLO(model_path)
+# print("Model loaded successfully.")
+
+# Step 3: Define the prediction functions for images and videos
+def predict_image(image):
+    # Run inference
+    results = model(image, device=device)
+    # Plot results on the image
+    annotated_image = results[0].plot()  # Get the annotated image with bounding boxes
+    return Image.fromarray(annotated_image)
+
+inputs_image = [
+    gr.Image(type='filepath', label='input image')
+]
+outputs_image = [
+    gr.Image(type='numpy', label='output image')
+]
+
+# Step 4: Define the Gradio Interface
+interface_image = gr.Interface(
+    fn=predict_image,
+    inputs=inputs_image,
+    outputs=outputs_image,
+    title="Pest Detection",
+    description="Upload an image and the model will detect pests."
+)
+
+# Step 5: Launch the interface
+interface_image.launch()