import json
import requests
from mtranslate import translate
from prompts import PROMPT_LIST
import streamlit as st
import random
import transformers
from transformers import GPT2Tokenizer, GPT2LMHeadModel
import fasttext
import SessionState

LOGO = "huggingwayang.png"

MODELS = {
    "GPT-2 Small": "flax-community/gpt2-small-indonesian",
    "GPT-2 Medium": "flax-community/gpt2-medium-indonesian",
    "GPT-2 Small Finetuned on Indonesian Journals": "Galuh/id-journal-gpt2"
}

headers = {}

@st.cache(show_spinner=False, persist=True, hash_funcs={transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel: lambda _: None})
def load_gpt(model_type):
    model = GPT2LMHeadModel.from_pretrained(MODELS[model_type])

    return model

@st.cache(show_spinner=False, persist=True, hash_funcs={transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer: lambda _: None})
def load_gpt_tokenizer(model_type):
    tokenizer = GPT2Tokenizer.from_pretrained(MODELS[model_type])

    return tokenizer

def get_image(text: str):
    url = "https://wikisearch.uncool.ai/get_image/"
    try:
        payload = {
            "text": text,
            "image_width": 400
        }
        data = json.dumps(payload)
        response = requests.request("POST", url, headers=headers, data=data)
        print(response.content)
        image = json.loads(response.content.decode("utf-8"))["url"]
    except:
        image = ""
    return image

st.set_page_config(page_title="Indonesian GPT-2 Demo")

st.title("Indonesian GPT-2")

ft_model = fasttext.load_model('lid.176.ftz')

# Sidebar
st.sidebar.image(LOGO)
st.sidebar.subheader("Configurable parameters")

max_len = st.sidebar.number_input(
    "Maximum length",
    value=100,
    help="The maximum length of the sequence to be generated."
)

temp = st.sidebar.slider(
    "Temperature",
    value=1.0,
    min_value=0.0,
    max_value=100.0,
    help="The value used to module the next token probabilities."
)

top_k = st.sidebar.number_input(
    "Top k",
    value=50,
    help="The number of highest probability vocabulary tokens to keep for top-k-filtering."
)

top_p = st.sidebar.number_input(
    "Top p",
    value=1.0,
    help=" If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation."
)

st.markdown(
    """
    This demo uses the [small](https://huggingface.co/flax-community/gpt2-small-indonesian) and 
    [medium](https://huggingface.co/flax-community/gpt2-medium-indonesian) Indonesian GPT2 model 
    trained on the Indonesian [Oscar](https://huggingface.co/datasets/oscar), [MC4](https://huggingface.co/datasets/mc4) 
    and [Wikipedia](https://huggingface.co/datasets/wikipedia) dataset. We created it as part of the 
    [Huggingface JAX/Flax event](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/).

    The demo supports "multi language" ;-), feel free to try a prompt on your language. We are also experimenting with 
    the sentence based image search using Wikipedia passages encoded with distillbert, and search the encoded sentence
    in the encoded passages using Facebook's Faiss.
    """
)

model_name = st.selectbox('Model',(['GPT-2 Small', 'GPT-2 Medium', 'GPT-2 Small Finetuned on Indonesian Journals']))

if model_name in ["GPT-2 Small", "GPT-2 Medium"]:
    prompt_group_name = "GPT-2"
elif model_name in ["GPT-2 Small Finetuned on Indonesian Journals"]:
    prompt_group_name = "Indonesian Journals"

ALL_PROMPTS = list(PROMPT_LIST[prompt_group_name].keys())+["Custom"]
prompt = st.selectbox('Prompt', ALL_PROMPTS, index=len(ALL_PROMPTS)-1)

session_state = SessionState.get(prompt_box=None)

if prompt == "Custom":
    prompt_box = "Enter your text here"
else:
    prompt_box = random.choice(PROMPT_LIST[prompt_group_name][prompt])

session_state.prompt_box = prompt_box

text = st.text_area("Enter text", session_state.prompt_box)

if st.button("Run"):
    with st.spinner(text="Getting results..."):
        lang_predictions, lang_probability = ft_model.predict(text.replace("\n", " "), k=3)
        if "__label__id" in lang_predictions:
            lang = "id"
        else:
            lang = lang_predictions[0].replace("__label__", "")
            text = translate(text, "id", lang)

        st.subheader("Result")
        model = load_gpt(model_name)
        tokenizer = load_gpt_tokenizer(model_name)

        input_ids = tokenizer.encode(text, return_tensors='pt')
        output = model.generate(input_ids=input_ids,
                                max_length=max_len,
                                temperature=temp,
                                top_k=top_k,
                                top_p=top_p,
                                repetition_penalty=2.0)

        text = tokenizer.decode(output[0], 
                                skip_special_tokens=True)
        st.write(text.replace("\n", "  \n"))

        st.text("Translation")
        translation = translate(text, "en", "id")

        if lang == "id":
            st.write(translation.replace("\n", "  \n"))

        else:
            st.write(translate(text, lang, "id").replace("\n", "  \n"))

        image_cat = "https://media.giphy.com/media/vFKqnCdLPNOKc/giphy.gif"
        image = get_image(translation.replace("\"", "'"))

        if image is not "":
            st.image(image, width=400)

        else:
            # display cat image if no image found
            st.image(image_cat, width=400)