# 📷 Object Detection Demo | CPU-only HF Space

import gradio as gr
from transformers import pipeline
from PIL import Image, ImageDraw

# Load DETR object‐detection pipeline (requires timm in requirements)
detector = pipeline("object-detection", model="facebook/detr-resnet-50", device=-1)

def detect_objects(image: Image.Image):
    outputs = detector(image)

    annotated = image.convert("RGB")
    draw = ImageDraw.Draw(annotated)
    table = []

    for obj in outputs:
        box = obj["box"]
        # DETR pipeline may return box as dict or list
        if isinstance(box, dict):
            xmin = int(box.get("xmin", box.get("x", 0)))
            ymin = int(box.get("ymin", box.get("y", 0)))
            xmax = int(box.get("xmax", xmin))
            ymax = int(box.get("ymax", ymin))
        else:
            # assume [x, y, w, h]
            x, y, w, h = box
            xmin, ymin = int(x), int(y)
            xmax, ymax = int(x + w), int(y + h)

        label = obj["label"]
        score = round(obj["score"], 3)

        # draw box & label
        draw.rectangle([xmin, ymin, xmax, ymax], outline="red", width=2)
        draw.text((xmin, max(ymin - 10, 0)), f"{label} ({score})", fill="red")

        table.append([label, score])

    return annotated, table

with gr.Blocks(title="📷✨ Object Detection Demo") as demo:
    gr.Markdown(
        """
        # 📷✨ Object Detection  
        Upload an image and let DETR identify objects on CPU.
        """
    )

    with gr.Row():
        img_in = gr.Image(type="pil", label="Upload Image")
        btn    = gr.Button("Detect Objects 🔍", variant="primary")

    img_out   = gr.Image(label="Annotated Image")
    table_out = gr.Dataframe(
        headers=["Label", "Score"],
        datatype=["str", "number"],
        wrap=True,
        interactive=False,
        label="Detections"
    )

    btn.click(detect_objects, inputs=img_in, outputs=[img_out, table_out])

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0")