app.py

import gradio as gr
from transformers import pipeline
from PIL import Image, ImageDraw, ImageFont
from collections import defaultdict

# 1) Zero-shot detector (works on CPU Spaces)
#    You can upgrade model to "google/owlv2-base-patch16-ensemble" for higher accuracy (slower).
detector = pipeline(
    task="zero-shot-object-detection",
    model="google/owlvit-base-patch32"  # fast & lightweight
)

# Keep labels explicit so the model can choose the right class.
# (You can add synonyms like "Bengal tiger", "African lion" if you want.)
LABELS = ["tiger", "lion"]

COLOR_BY_LABEL = {
    "tiger": "red",
    "lion": "blue",
}

def iou(box_a, box_b):
    xA = max(box_a["xmin"], box_b["xmin"])
    yA = max(box_a["ymin"], box_b["ymin"])
    xB = min(box_a["xmax"], box_b["xmax"])
    yB = min(box_a["ymax"], box_b["ymax"])
    inter_w = max(0.0, xB - xA)
    inter_h = max(0.0, yB - yA)
    inter = inter_w * inter_h
    area_a = (box_a["xmax"] - box_a["xmin"]) * (box_a["ymax"] - box_a["ymin"])
    area_b = (box_b["xmax"] - box_b["xmin"]) * (box_b["ymax"] - box_b["ymin"])
    denom = area_a + area_b - inter + 1e-9
    return inter / denom

def nms_single_class(dets, iou_thresh=0.5):
    # dets: list of dicts with keys {"box": {...}, "score": float, "label": str}
    dets = sorted(dets, key=lambda d: d["score"], reverse=True)
    kept = []
    while dets:
        best = dets.pop(0)
        kept.append(best)
        dets = [d for d in dets if iou(best["box"], d["box"]) < iou_thresh]
    return kept

def class_aware_nms(dets, iou_thresh=0.5):
    # Run NMS separately per class so lions don't suppress tigers (and vice versa)
    by_label = defaultdict(list)
    for d in dets:
        by_label[d["label"].lower()].append(d)
    merged = []
    for label, per_class in by_label.items():
        merged.extend(nms_single_class(per_class, iou_thresh=iou_thresh))
    return merged

def annotate(img, dets):
    draw = ImageDraw.Draw(img)
    try:
        font = ImageFont.truetype("DejaVuSans.ttf", 14)
    except:
        font = None
    for d in dets:
        b = d["box"]
        color = COLOR_BY_LABEL.get(d["label"].lower(), "red")
        draw.rectangle([(b["xmin"], b["ymin"]), (b["xmax"], b["ymax"])], outline=color, width=3)
        txt = f"{d['label']} {d['score']:.2f}"
        # Estimate text width
        try:
            txt_w = draw.textlength(txt, font=font)
        except AttributeError:
            txt_w = 8 * len(txt)
        pad = 3
        draw.rectangle(
            [(b["xmin"], b["ymin"] - 18), (b["xmin"] + txt_w + 2 * pad, b["ymin"])],
            fill=color
        )
        draw.text((b["xmin"] + pad, b["ymin"] - 16), txt, fill="white", font=font)
    return img

def count_big_cats(img, score_threshold, iou_threshold):
    # 2) Run zero-shot detection with both labels
    preds = detector(img, candidate_labels=LABELS)

    # 3) Keep only our labels and apply score filter
    preds = [p for p in preds if p["label"].lower() in LABELS and p["score"] >= score_threshold]

    # 4) Class-aware NMS
    preds = class_aware_nms(preds, iou_thresh=iou_threshold)

    # 5) Prepare counts
    tiger_count = sum(1 for p in preds if p["label"].lower() == "tiger")
    lion_count = sum(1 for p in preds if p["label"].lower() == "lion")
    total_count = tiger_count + lion_count

    # 6) Draw boxes
    img_annotated = annotate(img.copy(), preds)
    return tiger_count, lion_count, total_count, img_annotated

TEST_IMAGES = {
    "Tiger": "examples/tiger1.png",
    "Lion": "examples/tiger2.png",
    "Both": "examples/tiger3.png",
}

def load_test_image(choice):
    return Image.open(TEST_IMAGES[choice])


with gr.Blocks(title="Big Cat Counter") as demo:
    gr.Markdown("# 🐯🦁 Big Cat Counter\nUpload an image and I’ll count how many **tigers** and **lions** I see.")
    with gr.Row():
        with gr.Column():
            inp = gr.Image(type="pil", label="Input image")
            test_selector = gr.Dropdown(list(TEST_IMAGES.keys()), label="Pick a test image")
            score_th = gr.Slider(0.05, 0.95, value=0.20, step=0.05, label="Score threshold")
            iou_th = gr.Slider(0.1, 0.9, value=0.50, step=0.05, label="IOU (NMS) threshold")
            btn = gr.Button("Count Big Cats")
        with gr.Column():
            out_tiger = gr.Number(label="Tiger count", precision=0)
            out_lion = gr.Number(label="Lion count", precision=0)
            out_total = gr.Number(label="Total big cats", precision=0)
            out_img = gr.Image(label="Annotated output")
    test_selector.change(fn=load_test_image, inputs=test_selector, outputs=inp)
    btn.click(fn=count_big_cats, inputs=[inp, score_th, iou_th], outputs=[out_tiger, out_lion, out_total, out_img])

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