from pathlib import Path
from fastai.vision.all import *
from bs4 import BeautifulSoup
import requests
import re
import nltk
from pprint import pprint
import random
import streamlit as st
import pathlib

plt =platform.system()

if plt == "Windows": pathlib.WindowsPath = pathlib.PosixPath



# Add random seed for answer location #
random.seed(42)

# app layout #
st.set_page_config(
    page_title="Star Wars Character App"
)

#st.sidebar.success("Select a page above.")

# set session_state for character name prediction #
if "char_label" not in st.session_state:
    st.session_state["char_label"] = ""

# session_state for quiz tests #
if "quiz_corpus" not in st.session_state:
    st.session_state["quiz_corpus"] = ""

# make prediction function #


def make_pred(_model, _image):
    """
    we return the predicted label to be used later
    """

    label, idx, preds = _model.predict(_image)
    print(
        f"This is a picture of {label}, model is {preds[idx] * 100:.2f}% sure.")
    st.write(
        f"This is a picture of {label}, model is {preds[idx] * 100:.2f}% sure.")

    return label


# Load Model function #
@st.experimental_singleton
def load_model():
    path = Path()
    learn_inf = load_learner(path / 'star-wars-characters-model_res34.pkl')

    return learn_inf

# scrape web data for summary function #


@st.experimental_singleton
def srape_wiki(star_wars_character):

    url = "https://starwars.fandom.com/wiki/"+star_wars_character
    print(url)
    r = requests.get(url)
    soup = BeautifulSoup(r.text, "html.parser")

    div_top = soup.find("div", class_="quote")
    div_bot = soup.find("div", id="toc", class_="toc")

    content = ""
    item = div_top.nextSibling

    while item != div_bot:
        content += str(item)
        item = item.nextSibling

    # beautify content #
    content_b = BeautifulSoup(content, "html.parser")

    text_arr = []
    for sentence in content_b.find_all("p"):
        text_arr.append(sentence.text.strip())

    # make text one continous string #
    text_str = " ".join(text_arr)

    # remove '\n' #
    text_str = text_str.split("\n")
    text_str = " ".join(text_str)

    # remove brackets and all inside them #
    corpus = re.sub(r'\[.*?\]', "", text_str)

    return corpus

# make quiz prediction #


@st.experimental_singleton
def model_predict(payload):
    nltk.download('universal_tagset')
    nltk.download('stopwords')
    from Questgen import main
    # load t5 model #
    qg_mcq = main.QGen()
    model_out = qg_mcq.predict_mcq(payload)

    for i in model_out["questions"]:
        i["options"].insert(random.randint(0, 3), i["answer"])

    return model_out


st.markdown("<h1 style='text-align: center; color: grey;'>Star Wars Character App</h1>",
            unsafe_allow_html=True)

# st.markdown("<h2 style='text-align: center; color: black;'>Character Classification</h2>",
#             unsafe_allow_html=True)

# containers #
col1, col2, col3 = st.columns(3)

# loading fastai model #
learn_inf = load_model()

# CLASSIFICATION SECTION #
with st.expander("Image Classification"):
    st.markdown("<h2 style='text-align: center; color: black;'>Character Classification</h2>",
                unsafe_allow_html=True)

    # upload image #
    uploaded_file1 = st.file_uploader(
        "Upload Star wars character", type=['png', 'jpeg', 'jpg'])

    if uploaded_file1 is not None:
        image_file1 = PILImage.create((uploaded_file1))
        # with st.expander("See Image"):
        st.image(image_file1.to_thumb(200, 200),
                 caption='Uploaded Image')

    with st.form(key="image_class"):

        classify_img = st.form_submit_button("Submit")
        if classify_img:
            # with st.expander("See explanation"):
            #     st.image(image_file1.to_thumb(200, 200), caption='Uploaded Image')
            st.session_state["char_label"] = make_pred(learn_inf, image_file1)

st.markdown("<br></br>", unsafe_allow_html=True)

# SUMMARY SECTION #
with st.expander("Summary"):
    st.markdown("<h2 style='text-align: center; color: black;'>Character Summary</h2>",
                unsafe_allow_html=True)
    st.write("Summary of: ", st.session_state["char_label"])
    try:
        st.session_state["quiz_corpus"] = srape_wiki(
            st.session_state["char_label"])
        st.write(st.session_state["quiz_corpus"])
    except AttributeError:
        st.error(
            "Please choose a different variation of the character name")
        out_text_area = st.text_input(
            "Charater name", st.session_state["char_label"])

    with st.form(key="summary"):
        #st.write("Summary of ", st.session_state["char_label"])
        summary = st.form_submit_button("Summary")
        if summary:
            st.write(out_text_area)
            st.session_state["char_label"] = out_text_area
            st.session_state["quiz_corpus"] = srape_wiki(
                st.session_state["char_label"])
            st.write(st.session_state["quiz_corpus"])


st.markdown("<br></br>", unsafe_allow_html=True)

# QUIZ SECTION #
with st.expander("Quiz"):
    st.markdown("<h2 style='text-align: center; color: black;'>Character Quiz</h2>",
                unsafe_allow_html=True)

    payload = {
        "input_text": st.session_state["quiz_corpus"]
    }

    try:
        model_output = model_predict(payload)
        for i in model_output["questions"]:
            with st.form(key=str(i["id"])):
                st.write(f"Question  {i['id']}")
                entry = st.radio(label=i["question_statement"],
                                 options=(i["options"]), key=str(i["id"]))
        
                checkbox_val = st.checkbox("Do you want a clue?")
        
                submitted = st.form_submit_button(label='Submit')
                if submitted:
                    if i["answer"] == entry:
                        st.write("CORRECT!")
                    else:
                        st.write("Wrong, check clue")
                        if checkbox_val:
                            st.write(i["context"])
    except KeyError:
        print("error caught")