import cv2
import gradio as gr
from ultralytics import YOLO

# ── Config ─────────────────────────────────────────────
MODEL_PATH = "yolov8n.pt"     # modelo pre-entrenado (clase “person”)
CONF_THRES = 0.3              # confianza mínima detección
LINE_RATIO = 0.5              # posición de la línea virtual (50 % altura)
# ───────────────────────────────────────────────────────

model = YOLO(MODEL_PATH)

# estado global
memory = {}      # {track_id: previous_cy}
in_count = 0
out_count = 0


def count_people(frame):
    """
    Recibe un frame RGB (numpy) -> procesa -> devuelve frame RGB anotado y string.
    Se llama de forma continua porque el input tiene `streaming=True`.
    """
    global memory, in_count, out_count

    if frame is None:
        return None, ""

    # ── paso 1: RGB ➜ BGR para OpenCV/YOLO ─────────────
    frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
    h, w = frame_bgr.shape[:2]
    line_y = int(h * LINE_RATIO)

    # ── detección + tracking ───────────────────────────
    results = model.track(
        frame_bgr,
        classes=[0],                 # solo “person”
        conf=CONF_THRES,
        persist=True,
        verbose=False
    )

    annotated = frame_bgr.copy()
    cv2.line(annotated, (0, line_y), (w, line_y), (0, 255, 255), 2)

    if results:
        for box in results[0].boxes:
            x1, y1, x2, y2 = map(int, box.xyxy[0])
            cx, cy = int((x1 + x2) / 2), int((y1 + y2) / 2)
            tid = int(box.id[0]) if box.id is not None else -1

            # cruces entrada / salida
            prev_cy = memory.get(tid, cy)
            if prev_cy < line_y <= cy:        # cruzó de arriba → abajo (ENTRA)
                in_count += 1
            elif prev_cy > line_y >= cy:      # abajo → arriba (SALE)
                out_count += 1
            memory[tid] = cy

            # dibujitos
            cv2.rectangle(annotated, (x1, y1), (x2, y2), (0, 255, 0), 1)
            cv2.circle(annotated, (cx, cy), 3, (0, 0, 255), -1)
            cv2.putText(annotated, str(tid), (x1, y1 - 5),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)

    total = in_count - out_count
    label = f"In: {in_count} | Out: {out_count} | Ocupación: {total}"

    # ── paso 2: BGR ➜ RGB para mostrar en Gradio ───────
    annotated_rgb = cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB)
    return annotated_rgb, label


def reset_counts():
    """Callback para botón ‘Limpiar’."""
    global memory, in_count, out_count
    memory = {}
    in_count = 0
    out_count = 0
    return None, ""


with gr.Blocks(title="Contador de personas (entrada única)") as demo:
    gr.Markdown("# Contador de personas (entrada única)")

    with gr.Row():
        cam = gr.Image(sources=["webcam"], streaming=True, label="frame")
        out_img = gr.Image(label="Video")
    out_lbl = gr.Text(label="Contador")

    btn_clear = gr.Button("Limpiar")

    # wire-up
    cam.stream(fn=count_people, outputs=[out_img, out_lbl])
    btn_clear.click(fn=reset_counts, outputs=[out_img, out_lbl])

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