Upload 2 files (#2)

- Upload 2 files (44a233748483f224365f510127a1d945c972a50c)


Co-authored-by: John Smith <[email protected]>

README.md

@@ -1,13 +1,13 @@
----
-title: SigLIP Tagger
-emoji: 🧷
-colorFrom: green
-colorTo: blue
-sdk: gradio
-sdk_version: 4.16.0
-app_file: app.py
-pinned: true
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: SigLIP Tagger
+emoji: 🧷
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 4.43.0
+app_file: app.py
+pinned: true
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,164 +1,164 @@
-import os
-from PIL import Image
-
-import numpy as np
-import torch
-
-from transformers import (
-    AutoImageProcessor,
-)
-
-import gradio as gr
-
-from modeling_siglip import SiglipForImageClassification
-
-
-HF_TOKEN = os.environ["HF_READ_TOKEN"]
-
-EXAMPLES = [["./images/sample.jpg"], ["./images/sample2.webp"]]
-
-model_maps: dict[str, dict] = {
-    "test2": {
-        "repo": "p1atdev/siglip-tagger-test-2",
-    },
-    "test3": {
-        "repo": "p1atdev/siglip-tagger-test-3",
-    },
-    # "test4": {
-    #     "repo": "p1atdev/siglip-tagger-test-4",
-    # },
-}
-
-for key in model_maps.keys():
-    model_maps[key]["model"] = SiglipForImageClassification.from_pretrained(
-        model_maps[key]["repo"], torch_dtype=torch.bfloat16, token=HF_TOKEN
-    )
-    model_maps[key]["processor"] = AutoImageProcessor.from_pretrained(
-        model_maps[key]["repo"], token=HF_TOKEN
-    )
-
-README_MD = (
-    f"""\
-## SigLIP Tagger Test 3
-An experimental model for tagging danbooru tags of images using SigLIP.
-
-Model(s):
-"""
-    + "\n".join(
-        f"- [{value['repo']}](https://huggingface.co/{value['repo']})"
-        for value in model_maps.values()
-    )
-    + "\n"
-    + """
-Example images by NovelAI and niji・journey.
-"""
-)
-
-
-def compose_text(results: dict[str, float], threshold: float = 0.3):
-    return ", ".join(
-        [
-            key
-            for key, value in sorted(results.items(), key=lambda x: x[1], reverse=True)
-            if value > threshold
-        ]
-    )
-
-
-@torch.no_grad()
-def predict_tags(image: Image.Image, model_name: str, threshold: float):
-    if image is None:
-        return None, None
-
-    inputs = model_maps[model_name]["processor"](image, return_tensors="pt")
-
-    logits = (
-        model_maps[model_name]["model"](
-            **inputs.to(
-                model_maps[model_name]["model"].device,
-                model_maps[model_name]["model"].dtype,
-            )
-        )
-        .logits.detach()
-        .cpu()
-        .float()
-    )
-
-    logits = np.clip(logits, 0.0, 1.0)
-
-    results = {}
-
-    for prediction in logits:
-        for i, prob in enumerate(prediction):
-            if prob.item() > 0:
-                results[model_maps[model_name]["model"].config.id2label[i]] = (
-                    prob.item()
-                )
-
-    return compose_text(results, threshold), results
-
-
-css = """\
-.sticky {
-  position: sticky;
-  top: 16px;
-}
-
-.gradio-container {
-  overflow: clip;
-}
-"""
-
-
-def demo():
-    with gr.Blocks(css=css) as ui:
-        gr.Markdown(README_MD)
-
-        with gr.Row():
-            with gr.Column():
-                with gr.Row(elem_classes="sticky"):
-                    with gr.Column():
-                        input_img = gr.Image(
-                            label="Input image", type="pil", height=480
-                        )
-
-                        with gr.Group():
-                            model_name_radio = gr.Radio(
-                                label="Model",
-                                choices=list(model_maps.keys()),
-                                value="test3",
-                            )
-                            tag_threshold_slider = gr.Slider(
-                                label="Tags threshold",
-                                minimum=0.0,
-                                maximum=1.0,
-                                value=0.3,
-                                step=0.01,
-                            )
-
-                        start_btn = gr.Button(value="Start", variant="primary")
-
-                        gr.Examples(
-                            examples=EXAMPLES,
-                            inputs=[input_img],
-                            cache_examples=False,
-                        )
-
-            with gr.Column():
-                output_tags = gr.Text(label="Output text", interactive=False)
-                output_label = gr.Label(label="Output tags")
-
-        start_btn.click(
-            fn=predict_tags,
-            inputs=[input_img, model_name_radio, tag_threshold_slider],
-            outputs=[output_tags, output_label],
-        )
-
-    ui.launch(
-        debug=True,
-        # share=True
-    )
-
-
-if __name__ == "__main__":
-    demo()
+import os
+from PIL import Image
+
+import numpy as np
+import torch
+
+from transformers import (
+    AutoImageProcessor,
+)
+
+import gradio as gr
+
+from modeling_siglip import SiglipForImageClassification
+
+
+HF_TOKEN = os.environ.get("HF_READ_TOKEN")
+
+EXAMPLES = [["./images/sample.jpg"], ["./images/sample2.webp"]]
+
+model_maps: dict[str, dict] = {
+    "test2": {
+        "repo": "p1atdev/siglip-tagger-test-2",
+    },
+    "test3": {
+        "repo": "p1atdev/siglip-tagger-test-3",
+    },
+    # "test4": {
+    #     "repo": "p1atdev/siglip-tagger-test-4",
+    # },
+}
+
+for key in model_maps.keys():
+    model_maps[key]["model"] = SiglipForImageClassification.from_pretrained(
+        model_maps[key]["repo"], torch_dtype=torch.bfloat16, token=HF_TOKEN
+    )
+    model_maps[key]["processor"] = AutoImageProcessor.from_pretrained(
+        model_maps[key]["repo"], token=HF_TOKEN
+    )
+
+README_MD = (
+    f"""\
+## SigLIP Tagger Test 3
+An experimental model for tagging danbooru tags of images using SigLIP.
+
+Model(s):
+"""
+    + "\n".join(
+        f"- [{value['repo']}](https://huggingface.co/{value['repo']})"
+        for value in model_maps.values()
+    )
+    + "\n"
+    + """
+Example images by NovelAI and niji・journey.
+"""
+)
+
+
+def compose_text(results: dict[str, float], threshold: float = 0.3):
+    return ", ".join(
+        [
+            key
+            for key, value in sorted(results.items(), key=lambda x: x[1], reverse=True)
+            if value > threshold
+        ]
+    )
+
+
+@torch.no_grad()
+def predict_tags(image: Image.Image, model_name: str, threshold: float):
+    if image is None:
+        return None, None
+
+    inputs = model_maps[model_name]["processor"](image, return_tensors="pt")
+
+    logits = (
+        model_maps[model_name]["model"](
+            **inputs.to(
+                model_maps[model_name]["model"].device,
+                model_maps[model_name]["model"].dtype,
+            )
+        )
+        .logits.detach()
+        .cpu()
+        .float()
+    )
+
+    logits = np.clip(logits, 0.0, 1.0)
+
+    results = {}
+
+    for prediction in logits:
+        for i, prob in enumerate(prediction):
+            if prob.item() > 0:
+                results[model_maps[model_name]["model"].config.id2label[i]] = (
+                    prob.item()
+                )
+
+    return compose_text(results, threshold), results
+
+
+css = """\
+.sticky {
+  position: sticky;
+  top: 16px;
+}
+
+.gradio-container {
+  overflow: clip;
+}
+"""
+
+
+def demo():
+    with gr.Blocks(css=css) as ui:
+        gr.Markdown(README_MD)
+
+        with gr.Row():
+            with gr.Column():
+                with gr.Row(elem_classes="sticky"):
+                    with gr.Column():
+                        input_img = gr.Image(
+                            label="Input image", type="pil", height=480
+                        )
+
+                        with gr.Group():
+                            model_name_radio = gr.Radio(
+                                label="Model",
+                                choices=list(model_maps.keys()),
+                                value="test3",
+                            )
+                            tag_threshold_slider = gr.Slider(
+                                label="Tags threshold",
+                                minimum=0.0,
+                                maximum=1.0,
+                                value=0.3,
+                                step=0.01,
+                            )
+
+                        start_btn = gr.Button(value="Start", variant="primary")
+
+                        gr.Examples(
+                            examples=EXAMPLES,
+                            inputs=[input_img],
+                            cache_examples=False,
+                        )
+
+            with gr.Column():
+                output_tags = gr.Text(label="Output text", interactive=False)
+                output_label = gr.Label(label="Output tags")
+
+        start_btn.click(
+            fn=predict_tags,
+            inputs=[input_img, model_name_radio, tag_threshold_slider],
+            outputs=[output_tags, output_label],
+        )
+
+    ui.launch(
+        debug=True,
+        # share=True
+    )
+
+
+if __name__ == "__main__":
+    demo()