Good first version

app.py

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+from click import progressbar
+import gradio as gr
+from PIL import Image
+
+from dataset import ImageDataset
+from labelizer import get_task_response
+
+
+def auto_label(image):
+    text = get_task_response("<MORE_DETAILED_CAPTION>", image)
+    return image, text
+
+
+def auto_label_and_update(dataset, im, image_id):
+    """Generate label and return updated dataset."""
+    text = get_task_response("<MORE_DETAILED_CAPTION>", im)
+    return dataset.update_label(image_id, text)
+
+
+def uploaded(files, current_dataset):
+    """Handle file upload - return new dataset instance."""
+    if current_dataset is None:
+        current_dataset = ImageDataset()
+    return current_dataset.add_images(files)
+
+
+def labelize_all_images(dataset, label, progress=gr.Progress(True)):
+    """Generate labels for all images and return new dataset instance."""
+
+    # Generate actual labels
+    labels_dict = {}
+    for imdata in progress.tqdm(dataset.images):
+        text = get_task_response("<MORE_DETAILED_CAPTION>", Image.open(imdata["path"]))
+        print(text)
+        labels_dict[imdata["id"]] = text
+
+    return dataset.update_all_labels(labels_dict), label
+
+
+def update_buttons_states(dataset):
+    count = len(dataset.images)
+    return (
+        gr.update(interactive=count > 0),  # remove all
+        gr.update(interactive=count > 0),  # label all
+    )
+
+
+CSS = """
+.label_image_box {
+    border-radius: 1rem;
+    background: var(--panel-background-fill);
+    .image-frame img {
+        height: 300px;
+        max-height: 300px;
+    }
+}
+"""
+
+with gr.Blocks(title="Labelizer") as demo:
+    dataset = gr.State()
+    with gr.Sidebar():
+        gr.Markdown("# 🖼️ Image Labeling Tool")
+        gr.Markdown("Upload images and add labels to build your dataset.")
+        upload_button = gr.UploadButton("Upload images", file_count="multiple")
+        label_all = gr.Button("Labelize all images", interactive=False)
+        is_labeling_in_progress = gr.State(False)
+        progressbar = gr.Label("", visible=False, label="Preparing...")
+        remove_all = gr.Button("Remove all", interactive=False)
+
+    @gr.render(inputs=[dataset, is_labeling_in_progress])
+    def render_grid(dataset, is_labeling_in_progress):
+        if dataset is None:
+            return
+        cols = 6
+        rows = len(dataset.images) // cols
+        if len(dataset.images) > cols * rows:
+            rows += 1
+        current = 0
+        for _ in range(rows):
+            with gr.Row(equal_height=True):
+                for _ in range(cols):
+                    with gr.Column(variant="compact", elem_classes=["label_image_box"]):
+                        if current >= len(dataset.images):
+                            break
+                        image = gr.Image(
+                            dataset.images[current]["path"],
+                            type="pil",
+                            container=False,
+                            sources=None,
+                            buttons=["fullscreen"],
+                        )
+                        label = gr.Text(
+                            dataset.images[current]["label"],
+                            placeholder="Description...",
+                            lines=5,
+                            container=False,
+                            interactive=not is_labeling_in_progress,
+                        )
+                        with gr.Row():
+                            button = gr.Button(
+                                "Generate label",
+                                interactive=not is_labeling_in_progress,
+                            )
+
+                        button.click(
+                            auto_label,
+                            inputs=[image],
+                            outputs=[image, label],
+                        )
+
+                        current += 1
+
+    remove_all.click(lambda: ImageDataset(), inputs=None, outputs=dataset).then(
+        update_buttons_states, inputs=dataset, outputs=[remove_all, label_all]
+    )
+
+    upload_button.upload(
+        uploaded, inputs=[upload_button, dataset], outputs=dataset
+    ).then(
+        update_buttons_states,
+        inputs=dataset,
+        outputs=[remove_all, label_all],
+    )
+    label_all.click(
+        fn=lambda: (
+            gr.update(interactive=False),
+            gr.update(interactive=False),
+            gr.update(visible=True),
+            True,
+        ),
+        inputs=None,
+        outputs=[
+            upload_button,
+            label_all,
+            progressbar,
+            is_labeling_in_progress,
+        ],
+    ).then(
+        fn=labelize_all_images,
+        inputs=[dataset, progressbar],
+        outputs=[dataset, progressbar],
+    ).then(
+        fn=lambda: (
+            gr.update(interactive=True),
+            gr.update(interactive=True),
+            gr.update(visible=False),
+            False,
+        ),
+        inputs=None,
+        outputs=[
+            upload_button,
+            label_all,
+            progressbar,
+            is_labeling_in_progress,
+        ],
+    )
+
+if __name__ == "__main__":
+    demo.launch(css=CSS)

dataset.py

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+class ImageDataset:
+    def __init__(self, images=None):
+        self.images = images if images is not None else []
+
+    def add_images(self, files):
+        """Return new instance with added images."""
+        new_images = self.images.copy()
+        for _, file in enumerate(files):
+            new_images.append(
+                {
+                    "id": len(new_images),
+                    "path": file.name,
+                    "label": "",
+                    "name": f"Image {len(new_images) + 1}",
+                }
+            )
+        return ImageDataset(new_images)
+
+    def remove_image(self, image_id):
+        """Return new instance with image removed by ID."""
+        new_images = [img for img in self.images if img["id"] != image_id]
+        # Reindex remaining images
+        for i, img in enumerate(new_images):
+            img["id"] = i
+            img["name"] = f"Image {i + 1}"
+        return ImageDataset(new_images)
+
+    def update_label(self, image_id, label):
+        """Return new instance with updated label."""
+        new_images = []
+        for img in self.images:
+            new_img = img.copy()
+            if new_img["id"] == image_id:
+                new_img["label"] = label
+            new_images.append(new_img)
+        return ImageDataset(new_images)
+
+    def update_all_labels(self, labels_dict):
+        """Return new instance with all labels updated."""
+        new_images = []
+        for img in self.images:
+            new_img = img.copy()
+            if img["id"] in labels_dict:
+                new_img["label"] = labels_dict[img["id"]]
+            new_images.append(new_img)
+        return ImageDataset(new_images)

labelizer/__init__.py

@@ -0,0 +1,74 @@
+import torch
+from PIL import Image
+from transformers import Florence2ForConditionalGeneration, Florence2Processor
+
+MODEL_ID = "ducviet00/Florence-2-large-hf"
+
+# Global variables for lazy loading
+_model = None
+_processor = None
+_device = None
+_torch_dtype = None
+
+
+def _load_model():
+    """Load model and processor lazily"""
+    global _model, _processor, _device, _torch_dtype
+
+    if _model is None:
+        _device = "cuda:0" if torch.cuda.is_available() else "cpu"
+        _torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+        print(f"Loading model {MODEL_ID} on {_device} with dtype {_torch_dtype}...")
+        _model = Florence2ForConditionalGeneration.from_pretrained(
+            MODEL_ID, torch_dtype=_torch_dtype, trust_remote_code=True
+        ).to(_device)  # type: ignore
+        _processor = Florence2Processor.from_pretrained(
+            MODEL_ID, trust_remote_code=True
+        )
+        print("Model loaded successfully!")
+
+    return _model, _processor, _device, _torch_dtype
+
+
+def get_task_response(task_prompt: str, image: Image.Image, text_input=None):
+    """Return associated task response
+
+    Task can be:
+        '<MORE_DETAILED_CAPTION>'
+        '<DETAILED_CAPTION>'
+        '<CAPTION>'
+
+    """
+    # Lazy load model only when needed
+    model, processor, device, torch_dtype = _load_model()
+    if text_input is None:
+        prompt = task_prompt
+    else:
+        prompt = task_prompt + text_input
+
+    # Ensure image is in RGB mode
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    if processor is None:
+        raise ValueError("processor is None")
+    inputs = processor(
+        text=prompt,
+        images=image,
+        return_tensors="pt",  # type: ignore
+    ).to(device, torch_dtype)
+
+    generated_ids = model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=1024,
+        num_beams=3,
+    )
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+
+    parsed_answer = processor.post_process_generation(
+        generated_text, task=task_prompt, image_size=(image.width, image.height)
+    )
+
+    return parsed_answer[task_prompt]

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pyproject.toml

@@ -0,0 +1,15 @@
+[project]
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+license = {text = "MIT"}
+
+
+[tool.pdm]
+distribution = false