#!/usr/bin/env python3
import warnings
warnings.filterwarnings("ignore", message="Using a slow image processor as `use_fast` is unset.*")
warnings.filterwarnings("ignore", category=FutureWarning, message=".*AutoModelForVision2Seq.*")

import gradio as gr
from pathlib import Path
from app.captioner import get_captioner

cap = get_captioner()

SAMPLES_DIR =    Path("./samples") #/.resolve().parents[1]
EXAMPLES = [
    str(SAMPLES_DIR / "test1.jpg"),
    str(SAMPLES_DIR / "test2.jpg"),
]

def caption_fn(image):
    # image is a PIL.Image from gradio
    return cap.generate_caption(image)

with gr.Blocks(title="Image Captioner (CPU-friendly)") as demo:
    gr.Markdown("# 🖼️ Image Captioner\nUpload an image to get a concise caption.\n")
    with gr.Row():
        inp = gr.Image(type="pil", label="Input image")
        out = gr.Textbox(label="Caption", lines=3)
    btn = gr.Button("Generate caption", variant="primary")
    btn.click(caption_fn, inputs=inp, outputs=out)
    gr.Examples(EXAMPLES, inputs=[inp], outputs=[out], examples_per_page=2)

    gr.Markdown(
        "Tip: This demo uses a lightweight model by default (CPU). "
        "You can switch models/devices via `.env` without code changes."
    )

if __name__ == "__main__":
    demo.queue().launch()  # add share=True if you want a public link