added labels

app.py

@@ -1,24 +1,47 @@
-import fiftyone as fo
+import os
 from io import BytesIO
-from PIL import Image
+from multiprocessing import Pool, cpu_count
+
+import fiftyone as fo
 from datasets import load_dataset
-import os
+from PIL import Image
 
 # Load the dataset
 imagenet_hard_dataset = load_dataset("taesiri/imagenet-hard", split="validation")
-
 os.makedirs("dataset", exist_ok=True)
 
-list_of_images = []
 
-for i in range(len(imagenet_hard_dataset)):
+def process_image(i):
     image = imagenet_hard_dataset[i]["image"].convert("RGB")
-    image.save(f"dataset/{i}.JPEG", "JPEG", quality=100)
-    list_of_images.append(f"imagenet_hard_images/{i}.jpg")
+    image_path = f"dataset/{i}.JPEG"
+    image.save(image_path, "JPEG", quality=80)
+    return {
+        "file_path": image_path,
+        "labels": imagenet_hard_dataset[i]["english_label"],
+    }
+
+
+def create_fiftyone_sample(sample):
+    classifications = [
+        fo.Classification(label=str(label)) for label in sample["labels"]
+    ]
+    return fo.Sample(
+        filepath=sample["file_path"],
+        labels=fo.Classifications(classifications=classifications),
+    )
 
 
 if __name__ == "__main__":
-    # Ensures that the App processes are safely launched on Windows
-    dataset = fo.Dataset.from_images_dir("./dataset/")
+    # Process images in parallel and get the list of images with their labels
+    with Pool(cpu_count()) as pool:
+        samples_data = pool.map(process_image, range(len(imagenet_hard_dataset)))
+
+    # Create a FiftyOne dataset
+    dataset = fo.Dataset(name="imagenet-hard")
+
+    # Add images and labels to the FiftyOne dataset
+    samples = [create_fiftyone_sample(sample_data) for sample_data in samples_data]
+    dataset.add_samples(samples)
+
     session = fo.launch_app(dataset, port=7860, remote=True, address="0.0.0.0")
     session.wait()