import os  # For reading environment variables
import shutil  # For directory cleanup
import zipfile  # For extracting model archives
import pathlib  # For path manipulations
import tempfile  # For creating temporary files/directories

import gradio  # For interactive UI
import pandas  # For tabular data handling
import PIL.Image # For image I/O

import huggingface_hub # For downloading model assets
import autogluon.multimodal  # For loading AutoGluon image classifier

# Hardcoded Hub model (native zip)
MODEL_REPO_ID = "yusenthebot/sign-identification-autogluon" # Updated model ID
ZIP_FILENAME  = "autogluon_sign_predictor_dir.zip"
HF_TOKEN = os.getenv("HF_TOKEN", None)

# Local cache/extract dirs
CACHE_DIR   = pathlib.Path("hf_assets")
EXTRACT_DIR = CACHE_DIR / "predictor_native"

# Download & load the native predictor
def _prepare_predictor_dir() -> str:
    CACHE_DIR.mkdir(parents=True, exist_ok=True)
    local_zip = huggingface_hub.hf_hub_download(
        repo_id=MODEL_REPO_ID,
        filename=ZIP_FILENAME,
        repo_type="model",
        token=HF_TOKEN,
        local_dir=str(CACHE_DIR),
        local_dir_use_symlinks=False,
    )
    if EXTRACT_DIR.exists():
        shutil.rmtree(EXTRACT_DIR)
    EXTRACT_DIR.mkdir(parents=True, exist_ok=True)
    with zipfile.ZipFile(local_zip, "r") as zf:
        zf.extractall(str(EXTRACT_DIR))
    contents = list(EXTRACT_DIR.iterdir())
    predictor_root = contents[0] if (len(contents) == 1 and contents[0].is_dir()) else EXTRACT_DIR
    return str(predictor_root)

PREDICTOR_DIR = _prepare_predictor_dir()
PREDICTOR = autogluon.multimodal.MultiModalPredictor.load(PREDICTOR_DIR)

# Explicit class labels (edit copy as desired)
CLASS_LABELS = {0: "⛔ No Stop Sign", 1: "🛑 Stop Sign"} # Updated class labels

# Helper to map model class -> human label
def _human_label(c):
    try:
        ci = int(c)
        return CLASS_LABELS.get(ci, str(c))
    except Exception:
        return CLASS_LABELS.get(c, str(c))

# Do the prediction!
def do_predict(pil_img: PIL.Image.Image):
    # Make sure there's actually an image to work with
    if pil_img is None:
        return "No image provided.", {}, pandas.DataFrame(columns=["Predicted label", "Confidence (%)"])

    # IF we have something to work with, save it and prepare the input
    tmpdir = pathlib.Path(tempfile.mkdtemp())
    img_path = tmpdir / "input.png"
    pil_img.save(img_path)

    df = pandas.DataFrame({"image": [str(img_path)]})  # For AutoGluon expected input format

    # For class probabilities
    proba_df = PREDICTOR.predict_proba(df)

    # For user-friendly column names
    proba_df = proba_df.rename(columns={0: "⛔ No Stop Sign (0)", 1: "🛑 Stop Sign (1)"}) # Updated column names
    row = proba_df.iloc[0]

    # For pretty ranked dict expected by gr.Label
    pretty_dict = {
        "⛔ No Stop Sign": float(row.get("⛔ No Stop Sign (0)", 0.0)), # Updated dictionary keys
        "🛑 Stop Sign": float(row.get("🛑 Stop Sign (1)", 0.0)), # Updated dictionary keys
    }

    return pretty_dict

# Representative example images! These can be local or links.
EXAMPLES = [
    ["https://datasets-server.huggingface.co/assets/ecopus/sign_identification/--/a1506696eb48233ed9cd1afa1bea8e7002a7ad85/--/default/original/4/image/image.jpg?Expires=1760832682&Signature=m1wwlKKZ5Lkn2kN2Ty~zRgpJdXWwdXypqcrztCO001yw-2hYtU5ZfFHfmcp0miuoUGZDVdjEF4~3OAdBiACGotB5K5wjMQH~37EcHy~NYYsoFek2Zwg8rY7Syv4t9PnCpaScC4yN8tnRKQmHyIyq4Zj6Zr5JHNjMD5T6mgL9nH6Ul0PUojo~vEYXwc4FkCU1MPfuNWA3j4lIPiOwW2Daqfb-kmbeZcTFJ2tCk5CP8AbyWO5wncs~6JSiDCrj-1SQDgnlsarA95XLt6egDxbo0RBe2XWkV3jHHeQcNMd9aosD5LGPuAwa7LJnzcztwIcpx5iqsD0b5uHUcuh779qETw__&Key-Pair-Id=K3EI6M078Z3AC3"], # Example from the new dataset
    ["https://datasets-server.huggingface.co/assets/ecopus/sign_identification/--/a1506696eb48233ed9cd1afa1bea8e7002a7ad85/--/default/original/5/image/image.jpg?Expires=1760832682&Signature=ZZrth953~hKHf30qk9IfV-IW3xoZKmhYHdu-bZfWe0BbFHoRgMFNHkZG5zfb4npHxK6iPD5RXoFpdIipzpGZNRWCFRYy7rYXbm60fXFLFozwGBYhuGgV5-RIL~A-fvziB0-5KVL8BsC9cpy9YNeOv6PRKX36wEW6z8B7p8TTFV3A6u6KpfAuiyxl7GHISzLePXJlJtmmY6JeKAq4TUd99FxWitELwD4qtCcntP05FdldDycuQaqan5pDKcj6phCfa-p2vICEWyRyr8BPv3HnOcgdzcX1pTARLzf9lWiVz~TD5e4bJKPmhD2N~4k544OzQzZu9BvlGnE1YU1VIC2iGg__&Key-Pair-Id=K3EI6M078Z3AC3"],
    ["https://datasets-server.huggingface.co/assets/ecopus/sign_identification/--/a1506696eb48233ed9cd1afa1bea8e7002a7ad85/--/default/original/10/image/image.jpg?Expires=1760832682&Signature=Od6vvEE2Oh3UeagI5aWd6U60AXj9PymrbJhIQ2z6iS0y5DAw4xh2lPH3409TfJ5e9L~2FcxP1sl3wbkyV8GMEY~q8RYfxwpVkwMswcV3dK1ddvnWYFvJwtWnNExZxsDtgPO8h~KOsUcm5GLYnG7Wd21ibQ8r22-kVuRQb5jRpJLLBFAA0l9XMn-vLjHJAEwUrUUy15VKUL1G2oxFxhZQMxDXwS0QEaeNDzu9iMyy1YOvhN-jF4EFAf7pwPZggRfNIGuRYhemoB2tor-YU~KQAURiAD0mZ80ojbeihMcbFq6TB6~watBFegrYqCfp~~kTmLA0exqAkXmsHReqe26XPA__&Key-Pair-Id=K3EI6M078Z3AC3"],# Example from the new dataset
    ["https://datasets-server.huggingface.co/assets/ecopus/sign_identification/--/a1506696eb48233ed9cd1afa1bea8e7002a7ad85/--/default/original/11/image/image.jpg?Expires=1760832682&Signature=ObjUmUy3TxAdYQWbjo7d5n9LkDRc0ue-wt75eteR~tiQkCNn9yLqTRMUdefIvzKOoeUHTCzMQi7TZeHo7ik4LDNjdPaFdNmGx2TJLkGYogKLMIZNLKa1uSNYBifOq8w9WlJyaA8qc03C7m0hogbbrLtGsXyPk4ET9XmCsR3yU5w1DBXvsV4FPohiAuRqcMAFYOKDwqNZyeuRkNUIEXCsma68jESnDDJP0rXQruOZkd~Tm1GFnh8uB3zmwmby1m2bBjmNrAPPhnVDELD1OkpQUOrnAMuhHbcsg4S~Wo0sWPdnWgfONLw7j5xkp~GdJ3Nw~a57X-A~~Bo15ZhTgIHPhg__&Key-Pair-Id=K3EI6M078Z3AC3"] # Example from the new dataset

]

# Gradio UI
with gradio.Blocks() as demo:

    # Provide an introduction
    gradio.Markdown("# Stop Sign Detection?") # Updated title
    gradio.Markdown("""
    This is a simple app that demonstrates how to use an autogluon multimodal
    predictor in a gradio space to predict if a picture contains a stop sign. To use,
    just upload a photo. The result will be generated automatically if there is or is not a stop sign.
    """) # Updated description

    # Interface for the incoming image
    image_in = gradio.Image(type="pil", label="Input image", sources=["upload", "webcam"])

    # Interface elements to show htte result and probabilities
    proba_pretty = gradio.Label(num_top_classes=2, label="Class probabilities")

    # Whenever a new image is uploaded, update the result
    image_in.change(fn=do_predict, inputs=[image_in], outputs=[proba_pretty])

      # For clickable example images
    gradio.Examples(
        examples=EXAMPLES,
        inputs=[image_in],
        label="Representative examples",
        examples_per_page=8,
        cache_examples=False,
    )

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