import os
import builtins
import math
import streamlit as st
import gdown

# from google_drive_downloader import GoogleDriveDownloader as gdd

from demo.src.models import load_trained_model
from demo.src.utils import render_predict_from_pose, predict_to_image

# from demo.src.config import MODEL_DIR, MODEL_NAME, FILE_ID

st.set_page_config(page_title="DietNeRF")


def select_model():
    obj_select = st.selectbox("Select a scene", ("Chair", "Lego", "Ship", "Hotdog"))
    if obj_select == "Chair":
        FILE_ID = "17dj0pQieo94TozFv-noSBkXebduij1aM"
        MODEL_DIR = "models"
        MODEL_NAME = "diet_nerf_chair"
    elif obj_select == "Lego":
        FILE_ID = "1D9I-qIVMPaxuCHfUWPWMHaoLYtAmCjwI"
        MODEL_DIR = "models"
        MODEL_NAME = "diet_nerf_lego"
    elif obj_select == "Ship":
        FILE_ID = "14ZeJ86ETQr8dtu6CFoxU-ifvniHKo_Dt"
        MODEL_DIR = "models"
        MODEL_NAME = "diet_nerf_ship"
    elif obj_select == "Hotdog":
        FILE_ID = "11vNlR4lMvV_AVFgVjZmKMrMWGVG7qhNu"
        MODEL_DIR = "models"
        MODEL_NAME = "diet_nerf_hotdog"
    return MODEL_DIR, MODEL_NAME, FILE_ID


st.title("DietNeRF")
caption = (
    "Diet-NeRF achieves SoTA few-shot learning capacity in 3D model reconstruction. "
    "Thanks to the 2D supervision by CLIP (aka semantic loss), "
    "it can render novel and challenging views with ONLY 8 training images, "
    "outperforming original NeRF!"
)
st.markdown(caption)
st.markdown("")
MODEL_DIR, MODEL_NAME, FILE_ID = select_model()


@st.cache
def download_model():
    os.makedirs(MODEL_DIR, exist_ok=True)
    _model_path = os.path.join(MODEL_DIR, MODEL_NAME)
    # gdd.download_file_from_google_drive(file_id=FILE_ID,
    #                                     dest_path=_model_path,
    #                                     unzip=True)
    url = f"https://drive.google.com/uc?id={FILE_ID}"
    gdown.download(url, _model_path, quiet=False)
    print(f"Model downloaded from google drive: {_model_path}")


@st.cache(show_spinner=False, allow_output_mutation=True)
def fetch_model():
    model, state = load_trained_model(MODEL_DIR, MODEL_NAME)
    return model, state


model_path = os.path.join(MODEL_DIR, MODEL_NAME)
if not os.path.isfile(model_path):
    download_model()

model, state = fetch_model()
pi = math.pi
st.sidebar.image("images/diet-nerf-logo.png", width=310)
st.sidebar.markdown(
    "<a href="https://github.com/codestella/putting-nerf-on-a-diet" target="_blank">GitHub</a> | <a href="https://www.notion.so/DietNeRF-Putting-NeRF-on-a-Diet-4aeddae95d054f1d91686f02bdb74745" target="_blank">Project Report</a>",
    unsafe_allow_html=True,
)
st.sidebar.header("SELECT YOUR VIEW DIRECTION")
theta = st.sidebar.slider(
    "Theta", min_value=-pi, max_value=pi, step=0.5, value=0.0, help="Rotational angle in Horizontal direction"
)
phi = st.sidebar.slider(
    "Phi", min_value=0.0, max_value=0.5 * pi, step=0.1, value=1.0, help="Rotational angle in Vertical direction"
)
radius = st.sidebar.slider(
    "Radius", min_value=2.0, max_value=6.0, step=1.0, value=3.0, help="Distance between object and the viewer"
)

st.markdown("")

with st.spinner("Rendering Image, it may take 2-3 mins. So, why don't you read our report in the meantime"):
    pred_color, _ = render_predict_from_pose(state, theta, phi, radius)
    im = predict_to_image(pred_color)
    w, _ = im.size
    new_w = int(2 * w)
    im = im.resize(size=(new_w, new_w))
    st.image(im, use_column_width=True)