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# 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")
prompt = gr.Textbox(label="Compact chat template preview")
run_btn.click(_run, inputs=[model_id, image, use_sample, add_grid], outputs=[gallery, prompt])
if __name__ == "__main__":
demo.launch()
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