Create app.py

app.py

@@ -0,0 +1,70 @@
+import gradio as gr
+from datasets import load_dataset
+import os
+
+# Set your HF token
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+# Load dataset (streaming mode for large datasets)
+dataset = load_dataset(
+    "Aowrow/behance_interiordesign",
+    split="train",
+    streaming=True,
+    token=HF_TOKEN
+)
+
+def get_images(num_images=50, start_idx=0):
+    """Get a batch of images from the dataset"""
+    images = []
+    for i, example in enumerate(dataset):
+        if i < start_idx:
+            continue
+        if len(images) >= num_images:
+            break
+        if 'image' in example:
+            images.append(example['image'])
+    return images
+
+def show_gallery(num_images, start_idx):
+    images = get_images(num_images, start_idx)
+    return images
+
+# Create Gradio interface
+with gr.Blocks(title="Behance Interior Design Dataset Viewer") as demo:
+    gr.Markdown("# 🏠 Behance Interior Design Dataset Viewer")
+    gr.Markdown("Browse 500K+ interior design images from Behance")
+    
+    with gr.Row():
+        num_images = gr.Slider(
+            minimum=10,
+            maximum=100,
+            value=50,
+            step=10,
+            label="Images to Display"
+        )
+        start_idx = gr.Number(
+            value=0,
+            label="Start from Image #",
+            precision=0
+        )
+    
+    btn = gr.Button("Load Images")
+    gallery = gr.Gallery(
+        label="Interior Design Images",
+        columns=5,
+        height="auto"
+    )
+    
+    btn.click(show_gallery, inputs=[num_images, start_idx], outputs=gallery)
+    
+    # Load initial batch
+    demo.load(show_gallery, inputs=[num_images, start_idx], outputs=gallery)
+
+demo.launch()
+```
+
+**File 2: `requirements.txt`**
+```
+gradio>=4.0.0
+datasets
+Pillow