run_yolo_interactive.py

"""
Interactive runner for the YOLO object-detection inventory check.

Walks you through:
  1. Uploading the BASELINE (pickup) photo via a native file-picker dialog
  2. Uploading the RETURN photo the same way
  3. Detecting objects in both, with a running total per item type
  4. Comparing the two counts and reporting exactly what's missing

Run: python run_yolo_interactive.py
"""

import os
import json

from yolo_detection import detect_objects, draw_detections, compare_with_asymmetric_confidence
from inventory_check import compare_inventory


def pick_file(window_title):
    """Open a native file-picker dialog. Falls back to typed path entry if
    tkinter isn't available (e.g. headless environments)."""
    try:
        import tkinter as tk
        from tkinter import filedialog

        root = tk.Tk()
        root.withdraw()
        root.attributes("-topmost", True)
        path = filedialog.askopenfilename(
            title=window_title,
            filetypes=[("Image files", "*.jpg *.jpeg *.png *.bmp *.webp"), ("All files", "*.*")],
        )
        root.destroy()
        if path:
            return path
        print("  No file selected, please try again.")
        return pick_file(window_title)
    except Exception:
        while True:
            path = input(f"{window_title}\n  Enter the full file path: ").strip().strip('"')
            if os.path.isfile(path):
                return path
            print(f"  File not found: {path}")


def print_item_counts(label, counts):
    print(f"\n{label}:")
    if not counts:
        print("  (no objects detected with enough confidence)")
        return
    total = sum(counts.values())
    for name, count in sorted(counts.items(), key=lambda x: -x[1]):
        print(f"  {name}: {count}")
    print(f"  --- total items: {total} ---")


def main():
    print("=== YOLO Inventory Check ===")
    item_id = input("Item/kit ID (press Enter for a default): ").strip() or "kit_test_001"

    print("\nSelect the BASELINE (pickup) photo...")
    baseline_path = pick_file("Baseline photo (pickup)")
    print(f"  Baseline: {baseline_path}")

    print("Select the RETURN photo...")
    return_path = pick_file("Return photo")
    print(f"  Return:   {return_path}")

    print("\nDetecting objects (this may take a few seconds the first time, while the model downloads)...")
    baseline_detections = detect_objects(baseline_path)
    return_detections = detect_objects(return_path)

    baseline_counts, return_counts = compare_with_asymmetric_confidence(baseline_detections, return_detections)
    print("\n(Note: baseline uses a strict confidence bar to establish the expected inventory;")
    print(" the return photo uses a more lenient bar to CONFIRM presence, since a physically")
    print(" unchanged item can naturally score lower confidence from a different angle/lighting -")
    print(" only items with no detection at all in the return photo count as missing.)")

    print_item_counts("BASELINE - items detected", baseline_counts)
    print_item_counts("RETURN - items detected", return_counts)

    comparison = compare_inventory(baseline_counts, return_counts)

    print("\n" + "=" * 50)
    print("RESULT")
    print("=" * 50)
    if comparison.unchanged:
        print("Nothing missing - all item counts match.")
    else:
        print("MISSING ITEMS DETECTED:")
        for name, count in comparison.missing.items():
            print(f"  - {name}: {count} missing "
                  f"(expected {baseline_counts.get(name, 0)}, found {return_counts.get(name, 0)})")

    if comparison.extra:
        print("\nExtra items noticed in return photo (unusual, worth a glance):")
        for name, count in comparison.extra.items():
            print(f"  - {name}: +{count}")

    # save annotated images for visual review
    out_dir = "yolo_crops"
    os.makedirs(out_dir, exist_ok=True)
    baseline_out = os.path.join(out_dir, f"{item_id}_baseline_detected.jpg")
    return_out = os.path.join(out_dir, f"{item_id}_return_detected.jpg")
    draw_detections(baseline_path, baseline_detections, baseline_out)
    draw_detections(return_path, return_detections, return_out)
    print(f"\nAnnotated images saved:\n  {baseline_out}\n  {return_out}")

    result = {
        "item_id": item_id,
        "baseline_inventory": baseline_counts,
        "return_inventory": return_counts,
        "missing_items": comparison.missing,
        "extra_items": comparison.extra,
        "overall_change_detected": not comparison.unchanged,
        "routing_hint": "agent_4" if not comparison.unchanged else "none",
    }
    print("\nFull JSON output:")
    print(json.dumps(result, indent=2))


if __name__ == "__main__":
    main()