Update app.py

app.py

@@ -1,7 +1,66 @@
 import gradio as gr
+import random
+import time
+from datasets import load_dataset, Dataset
 
-def greet(name):
-    return "Hello " + name + "!!"
+"""
+This code is designed to read in the ImageNet 1K ILSVRC dataset from the Hugging Face Hub, 
+then create a new version of this dataset with {percentage} lines with random labels between 0-9,
+then upload this new version of the Hugging Face Hub, in the Data Composition organization:
+https://huggingface.co/datasets/datacomp
+"""
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+# The number of examples/instances in this dataset is copied from the model card:
+# https://huggingface.co/datasets/ILSVRC/imagenet-1k
+NUM_EXAMPLES = 1281167
+# Arbitrary small number, only using in devv'ing (uncomment #.take(DEV_AMOUNT) below to use it).
+DEV_AMOUNT = 100
+
+
+def main(percentage=10):
+    global randomize_subset
+    # Just for timing how long this takes.
+    start = time.time()
+
+    print("Randomizing %d percent of the data." % percentage)
+    # Set the random seed, based on the percentage, so that our random changes are reproducible.
+    random.seed(percentage)
+
+    # Load the dataset from the HF hub. Use streaming so as not to load the entire dataset at once.
+    # Use the .take(DEV_AMOUNT) to only grab a small chunk of instances to develop with.
+    dataset = load_dataset("ILSVRC/imagenet-1k", split="train", streaming=True,
+                           trust_remote_code=True).take(DEV_AMOUNT)
+
+    # Create a set of indices that are randomly chosen, to change their labels.
+    # Specifically, randomly choose NUM_EXAMPLES/percentage indices.
+    randomize_subset = set(random.sample(range(0, NUM_EXAMPLES), round(
+        NUM_EXAMPLES / float(percentage))))
+
+    # Update the dataset so that the labels are randomized
+    updated_dataset = dataset.map(randomize_labels, with_indices=True,
+                                  features=dataset.features, batched=True)
+
+    # Upload the new version of the dataset (this will take awhile)
+    Dataset.from_generator(updated_dataset.__iter__).push_to_hub(
+        "datacomp/imagenet-1k-random" + str(percentage))
+
+    end = time.time()
+    print("That took %d seconds" % (end - start))
+
+
+def randomize_labels(examples, indices):
+    # What set of examples should be randomized in this batch?
+    # This is the intersection of the batch indices and the indices we randomly selected to change the labels of.
+    batch_subset = list(set(indices) & randomize_subset)
+    # If this batch has indices that we're changing the label of....
+    if batch_subset != []:
+        # Change the label to a random integer between 0 and 9
+        for n in range(len(indices)):
+            index = indices[n]
+            examples["label"][n] = random.randint(0,
+                                                  9) if index in batch_subset else \
+            examples["label"][n]
+    return examples
+
+demo = gr.Interface(fn=main, inputs="text", outputs="text")
 demo.launch()