import os
import clip
import torch
import logging
import json
import pickle
from PIL import Image
import gradio as gr
from scipy.special import softmax


logger = logging.getLogger("basebody")
CLIP_MODEL_NAME = "ViT-B/16"

TEXT_PROMPTS_FILE_NAME = "text_prompts.json"
LOGISTIC_REGRESSION_MODEL_FILE_NAME = "logistic_regression_l1_oct_2.pkl"

HF_TOKEN = os.getenv('HF_TOKEN')
hf_writer = gr.HuggingFaceDatasetSaver(
    HF_TOKEN, "Roblox/basebody_feedback"
)

clip_model, preprocess = clip.load(
    CLIP_MODEL_NAME, device="cpu"
)

with open(
    os.path.join(os.path.dirname(__file__), TEXT_PROMPTS_FILE_NAME), "r"
) as f:
    text_prompts = json.load(f)
with open(
    os.path.join(
        os.path.dirname(__file__), LOGISTIC_REGRESSION_MODEL_FILE_NAME
    ),
    "rb",
) as f:
    lr_model = pickle.load(f)

logger.info("Logistic regression model loaded, coefficients: ")


all_text_features = []
with torch.no_grad():
    for k, prompts in text_prompts.items():
        assert len(prompts) == 2
        inputs = clip.tokenize(prompts)
        outputs = clip_model.encode_text(inputs)
        all_text_features.append(outputs)
    all_text_features = torch.cat(all_text_features, dim=0)
    all_text_features = all_text_features.cpu()


def predict_fn(input_img):
    input_img = Image.fromarray(input_img.astype("uint8"), "RGB")
    image = preprocess(
        input_img
    ).unsqueeze(0)
    with torch.no_grad():
        image_features = clip_model.encode_image(image)
        cosine_simlarities = softmax(
            (all_text_features @ image_features.cpu().T)
            .squeeze()
            .reshape(len(text_prompts), 2, -1),
            axis=1,
        )[:, 0, :]
        # logger.info(f"cosine_simlarities shape: {cosine_simlarities.shape}")
        logger.info(f"cosine_simlarities: {cosine_simlarities}")
    probabilities = lr_model.predict_proba(
        cosine_simlarities.reshape(1, -1)
    )
    logger.info(f"probabilities: {probabilities}")
    result_probabilty = float(probabilities[0][1].round(3))
    # get decision string
    if result_probabilty > 0.95:
        decision = "AUTO ACCEPT"
    elif result_probabilty < 0.4:
        decision = "AUTO REJECT"
    else:
        decision = "MODERATION"
    logger.info(f"decision: {decision}")
    decision_json = json.dumps(
        {"is_base_body": result_probabilty, "decision": decision}
    ).encode("utf-8")
    logger.info(f"decision_json: {decision_json}")
    return decision_json


iface = gr.Interface(
    fn=predict_fn,
    inputs="image",
    outputs="text",
    description="""
    The model returns the probability of the image being a base body. If
    probability > 0.95, the image can be automatically tagged as a base body. If
    probability < 0.4, the image can be automatically REJECTED as NOT as base
    body. All other cases will be submitted for moderation.

    Please flag if you think the decision is wrong.
    """,

    allow_flagging="manual",
    flagging_options=[
        ": decision hould be accept",
        ": decision hould be reject",
        ": decision hould be moderation"
    ],
    flagging_callback=hf_writer
)
iface.launch()