Configure app visual appearance

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+sample_images/*.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,14 +1,47 @@
 ---
-title: Novic
-emoji: 🏆
-colorFrom: yellow
+title: "NOVIC: Unconstrained Open Vocabulary Image Classification"
+short_description: "Prompt-free open vocabulary classification of any image"
+license: gpl-3.0
+emoji: 🖼️
+colorFrom: green
 colorTo: yellow
 sdk: gradio
+python_version: 3.10
 sdk_version: 5.35.0
 app_file: app.py
-pinned: false
-license: gpl-3.0
-short_description: Prompt-free open vocabulary classification of any image
+fullWidth: true
+header: default
+models: []
+tags:
+  - prompt-free
+  - open vocabulary
+  - image classification
+  - computer vision
+  - identification
+  - generative
+pinned: true
+disable_embedding: false
+preload_from_hub: []
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+To run the Gradio space locally, you first create a sandboxed Python 3.10 environment:
+```bash
+python -m venv .venv  # <-- Python 3.10
+source .venv/bin/activate
+# OR...
+conda create -n novic_spaces python=3.10
+conda activate novic_spaces
+```
+Then install `gradio` and any further requirements:
+```bash
+pip install gradio==5.35.0 -r requirements.txt
+```
+Now run the Gradio application:
+```bash
+./app.py
+# OR...
+gradio app.py
+```
+Open in a browser the URL that the application is running on (as shown in the CLI output), e.g. [http://127.0.0.1:7860](http://127.0.0.1:7860).

app.py

@@ -0,0 +1,65 @@
+#!/usr/bin/env python3
+# NOVIC Gradio Space
+
+# Imports
+import os
+import glob
+import random
+from typing import Optional
+import PIL.Image
+import gradio as gr
+
+# Sample images
+IMAGE_EXTS = ('jpg', 'jpeg', 'png', 'webp')
+SAMPLE_IMAGES = sorted(image_path for image_ext in IMAGE_EXTS for image_path in glob.glob(os.path.join('sample_images', f'*.{image_ext}')))
+
+# Classify image
+def classify_image(image: Optional[PIL.Image.Image]) -> dict[str, float]:
+
+	if image is None:
+		return {}
+
+	labels = ('thing', 'item', 'stuff', 'object', 'entity')
+	probs = [random.random() for _ in labels]
+	prob_sum = sum(probs)
+	return {label: prob / prob_sum for label, prob in zip(labels, probs)}
+
+# Gradio UI
+demo = gr.Interface(
+	fn=classify_image,
+	inputs=gr.Image(
+		height=400,
+		image_mode='RGB',
+		type='pil',
+		label='Input image',
+		show_label=True,
+		interactive=True,
+		show_fullscreen_button=True,
+	),
+	outputs=gr.Label(
+		num_top_classes=3,
+		label='Predicted label',
+		show_label=True,
+		show_heading=True,
+	),
+	examples=SAMPLE_IMAGES,
+	examples_per_page=20,
+	live=True,
+	title="🖼️ NOVIC: Unconstrained Open Vocabulary Image Classification",
+	description="*Select* an image from the examples shown below **OR** *Upload* an image file from your computer **OR** *Capture* an image using an attached camera **OR** *Copy-paste* an image from your clipboard. The label predictions on the right will update automatically!",
+	article=None,
+	theme=None,
+	flagging_mode='never',
+	analytics_enabled=True,
+	api_name='classify',
+	allow_duplication=False,
+	clear_btn=gr.Button(value='Clear', variant='secondary'),
+	show_progress='full',
+	fill_width=False,
+	deep_link=True,
+)
+
+# Run demo
+if __name__ == '__main__':
+	demo.launch()
+# EOF

requirements.txt

@@ -0,0 +1 @@
+pillow