initial commit

app.py

@@ -0,0 +1,66 @@
+# pip install -U gradio transformers pillow matplotlib
+
+import io
+from typing import Optional
+
+import gradio as gr
+import matplotlib.pyplot as plt
+from PIL import Image
+
+from transformers.utils.processor_visualizer_utils import ImageVisualizer
+
+
+def _fig_to_pil(fig) -> Image.Image:
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", bbox_inches="tight", dpi=160)
+    buf.seek(0)
+    return Image.open(buf).convert("RGB")
+
+
+def _run(model_id: str, image: Optional[Image.Image], use_sample: bool, add_grid: bool):
+    viz = ImageVisualizer(model_id)
+
+    # Capture all matplotlib figures the visualizer produces without changing the utility.
+    captured = []
+    orig_show = plt.show
+
+    def _capture_show(*_, **__):
+        # collect the current figure then do not actually display
+        fig = plt.gcf()
+        captured.append(fig)
+
+    try:
+        plt.show = _capture_show
+        viz.visualize(images=None if use_sample else image, add_grid=add_grid)
+    finally:
+        plt.show = orig_show
+
+    # Convert figures to PIL for Gradio
+    imgs = [_fig_to_pil(fig) for fig in captured] if captured else []
+    prompt_preview = viz.default_message(full_output=False)
+    return imgs, prompt_preview
+
+
+with gr.Blocks(title="Transformers Processor Visualizer") as demo:
+    gr.Markdown("Switch models and see what the processor actually feeds them (uses the existing `ImageVisualizer`).")
+
+    with gr.Row():
+        model_id = gr.Textbox(
+            label="Model repo_id",
+            value="openai/clip-vit-base-patch32",
+            placeholder="owner/repo (e.g., llava-hf/llava-1.5-7b-hf)",
+        )
+        add_grid = gr.Checkbox(label="Show patch grid", value=True)
+        use_sample = gr.Checkbox(label="Use HF logo sample", value=True)
+
+    image = gr.Image(label="Or upload an image", type="pil")
+
+    run_btn = gr.Button("Render")
+
+    gallery = gr.Gallery(label="Processor output").style(grid=2, height=600)
+    prompt = gr.Code(label="Compact chat template preview", language="text")
+
+    run_btn.click(_run, inputs=[model_id, image, use_sample, add_grid], outputs=[gallery, prompt])
+
+if __name__ == "__main__":
+    demo.launch()