Initial Changes

.gitattributes

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README.md

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+---
+title: Open AI Zero Shot Image Classification
+emoji: 👁
+colorFrom: red
+colorTo: yellow
+sdk: gradio
+sdk_version: 6.5.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+short_description: Image classification tasks in a zero-shot manner
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import torch
+from transformers import CLIPProcessor, CLIPModel
+import gradio as gr
+from PIL import Image
+import requests
+from typing import Iterable
+
+from gradio.themes import Soft
+from gradio.themes.utils import colors, fonts, sizes
+
+import warnings
+warnings.filterwarnings(action="ignore")
+
+from pathlib import Path
+
+BASE_DIR = Path(__file__).parent
+ASSETS_DIR = BASE_DIR / "images"
+
+colors.orange_red = colors.Color(
+    name="orange_red",
+    c50="#FFF0E5",
+    c100="#FFE0CC",
+    c200="#FFC299",
+    c300="#FFA366",
+    c400="#FF8533",
+    c500="#FF4500",
+    c600="#E63E00",
+    c700="#CC3700",
+    c800="#B33000",
+    c900="#992900",
+    c950="#802200",
+)
+
+class OrangeRedTheme(Soft):
+    def __init__(
+        self,
+        *,
+        primary_hue: colors.Color | str = colors.gray,
+        secondary_hue: colors.Color | str = colors.orange_red,
+        neutral_hue: colors.Color | str = colors.slate,
+        text_size: sizes.Size | str = sizes.text_lg,
+        font: fonts.Font | str | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("Outfit"), "Arial", "sans-serif",
+        ),
+        font_mono: fonts.Font | str | Iterable[fonts.Font | str] = (
+            fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace",
+        ),
+    ):
+        super().__init__(
+            primary_hue=primary_hue,
+            secondary_hue=secondary_hue,
+            neutral_hue=neutral_hue,
+            text_size=text_size,
+            font=font,
+            font_mono=font_mono,
+        )
+        super().set(
+            background_fill_primary="*primary_50",
+            background_fill_primary_dark="*primary_900",
+            body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)",
+            body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)",
+            button_primary_text_color="white",
+            button_primary_text_color_hover="white",
+            button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)",
+            button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)",
+            block_title_text_weight="600",
+            block_shadow="*shadow_drop_lg",
+        )
+
+orange_red_theme = OrangeRedTheme()
+
+MODEL_ID = "openai/clip-vit-base-patch32"
+model = CLIPModel.from_pretrained(MODEL_ID)
+processor = CLIPProcessor.from_pretrained(MODEL_ID)
+
+def postprocess_metaclip(probs, labels):
+    return {labels[i]: probs[0][i].item() for i in range(len(labels))}
+
+def metaclip_detector(image, texts):
+    inputs = processor(text=texts, images=image, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = outputs.logits_per_image.softmax(dim=1)
+    return probs
+
+def infer(image, candidate_labels):
+    candidate_labels = [l.strip() for l in candidate_labels.split(",")]
+    probs = metaclip_detector(image, candidate_labels)
+    return postprocess_metaclip(probs, labels=candidate_labels)
+
+css = """
+#root, body, html {
+    margin: 0;
+    padding: 0;
+    height: 100%;
+}
+
+.center-container {
+    max-width: 1000px;
+    margin: 0 auto !important;
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+}
+
+#main-title h1 {
+    text-align: center !important;
+    width: 100%;
+}
+"""
+
+with gr.Blocks(css=css, theme=orange_red_theme) as demo:
+    with gr.Column(elem_classes="center-container"):
+        
+        gr.Markdown("# **MetaCLIP 2 Zero-Shot Classification**", elem_id="main-title")
+        gr.Markdown("This is the demo of MetaCLIP 2 for zero-shot classification.")
+
+        with gr.Row():
+            with gr.Column():
+                image_input = gr.Image(type="filepath", label="Upload Image", height=310)
+                text_input = gr.Textbox(label="Input labels (comma separated)")
+                run_button = gr.Button("Run", variant="primary")
+            with gr.Column():
+                metaclip_output = gr.Label(
+                    label="MetaCLIP 2 Output",
+                    num_top_classes=3
+                )
+
+        run_button.click(
+            fn=infer, 
+            inputs=[image_input, text_input], 
+            outputs=[metaclip_output]
+        )
+
+demo.launch()

requirements.txt

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+huggingface-hub
+sentencepiece
+transformers
+accelerate
+protobuf
+pillow
+gradio
+torch