sync to remote

app.py

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+from datasets import Dataset, DatasetDict, load_dataset, Features, ClassLabel, Array3D
+from huggingface_hub import HfApi
+import os
+from PIL import Image
+import numpy as np
+import streamlit as st
+
+# Define the target image size
+IMG_SIZE = (128, 128)  # Change from (224, 224) to (128, 128)
+
+def load_images_from_folder(folder):
+    images = []
+    labels = []
+    label_names = sorted(os.listdir(folder))
+
+    for i, label in enumerate(label_names):
+        label_folder = os.path.join(folder, label)
+        if os.path.isdir(label_folder):
+            for img_file in os.listdir(label_folder):
+                img_path = os.path.join(label_folder, img_file)
+                if img_path.lower().endswith((".png", ".jpg", ".jpeg")):
+                    try:
+                        img = Image.open(img_path).convert("RGB")
+                        img = img.resize(IMG_SIZE)  # Resize to 128x128
+                        img_np = np.array(img, dtype=np.uint8)  # Convert to NumPy array
+
+                        images.append(img_np)
+                        labels.append(i)
+
+                    except Exception as e:
+                        print(f"Error processing image {img_path}: {e}")
+                        continue  # Skip problematic images
+
+    return Dataset.from_dict(
+        {"image": images, "label": labels},
+        features=Features({
+            "image": Array3D(shape=(128, 128, 3), dtype="uint8"),  # Update shape to (128, 128, 3)
+            "label": ClassLabel(names=label_names)
+        })
+    )
+
+def main():
+
+    st.title("Upload Cats and Dogs Dataset to Hugging Face Hub")
+    st.write("Download the archive of images from [this link](https://drive.google.com/uc?id=1Jq0tQqX5u3J8QfQqJ6f8v3v9XJ8h6Z1n) and extract it to the `dataset` folder.")
+    st.warning("This script will not work when run from the HuggingFace Space.")
+
+    #remove the stop once you have a copy of the dataset
+    #you will ned to run this script from your local machine
+    st.stop()
+
+    # Create dataset dictionary
+    dataset = DatasetDict({
+        "train": load_images_from_folder("dataset/train_set"),
+        "test": load_images_from_folder("dataset/test_set")
+    })
+
+    # Push dataset to Hugging Face Hub
+    repo_id = "cats_dogs_dataset"  # Choose a dataset name
+    HF_TOKEN = os.getenv("HF_TOKEN")
+
+    dataset.push_to_hub(repo_id, token=HF_TOKEN, commit_message="Initial dataset upload")
+
+    st.write(f"Dataset uploaded to {repo_id}")

requirements.txt

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+datasets
+huggingface_hub
+Pillow
+pyarrow
+numpy
+matplotlib