Add app.py

app.py

@@ -0,0 +1,43 @@
+import gradio as gr
+import numpy as np
+from ultralytics import YOLO
+
+# Load YOLO model
+model = YOLO('TrashDetection/trash_detection.pt')  # Ganti dengan path model YOLO Anda
+
+def predict(image):
+    """
+    Function to make predictions using YOLO model.
+    Args:
+        image (PIL.Image): Input image.
+    Returns:
+        List[List]: Predictions with labels, confidence, and bounding boxes.
+    """
+    # Convert PIL image to numpy array
+    img = np.array(image)
+
+    # Get predictions from the model
+    results = model(img)
+
+    # Extract predictions
+    predictions = results.pandas().xyxy[0]  # Pandas DataFrame of predictions
+    # Select necessary columns and convert to list
+    output = predictions[['name', 'confidence', 'xmin', 'ymin', 'xmax', 'ymax']].values.tolist()
+
+    return output
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),  # Input image as PIL
+    outputs=gr.Dataframe(
+        headers=["Label", "Confidence", "Xmin", "Ymin", "Xmax", "Ymax"],
+        label="Predictions"
+    ),
+    title="YOLO Object Detection",
+    description="Upload an image to detect objects using YOLO."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch(share=True)