from .utils import get_transformed_image

import streamlit as st
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import re
from mtranslate import translate
from .utils import (
    read_markdown,
    tokenizer,
    language_mapping,
    code_to_name,
    voicerss_tts
)
import requests
from PIL import Image
from .model.flax_clip_vision_mbart.modeling_clip_vision_mbart import (
    FlaxCLIPVisionMBartForConditionalGeneration,
)
from streamlit import caching


def app(state):
    mic_state = state
    with st.beta_expander("Usage"):
        st.write(read_markdown("usage.md"))
    st.write("\n")
    st.write(read_markdown("intro.md"))

    # st.sidebar.title("Generation Parameters")
    max_length = 64

    with st.sidebar.beta_expander('Generation Parameters'):
        do_sample = st.checkbox("Sample", value=False, help="Sample from the model instead of using beam search.")
        top_k = st.number_input("Top K", min_value=10, max_value=200, value=50, step=1, help="The number of highest probability vocabulary tokens to keep for top-k-filtering.")
        num_beams = st.number_input(label="Number of Beams", min_value=2, max_value=10, value=4, step=1, help="Number of beams to be used in beam search.")
        temperature = st.select_slider(label="Temperature", options = list(np.arange(0.0,1.1, step=0.1)), value=1.0, help ="The value used to module the next token probabilities.", format_func=lambda x: f"{x:.2f}")
        top_p = st.select_slider(label = "Top-P", options = list(np.arange(0.0,1.1, step=0.1)),value=1.0, help="Nucleus Sampling : If set to float < 1, only the most probable tokens with probabilities that add up to :obj:`top_p` or higher are kept for generation.", format_func=lambda x: f"{x:.2f}")
        if st.button("Clear All Cache"):
            caching.clear_cache()


    @st.cache
    def load_model(ckpt):
        return FlaxCLIPVisionMBartForConditionalGeneration.from_pretrained(ckpt)

    @st.cache
    def generate_sequence(pixel_values, lang_code, num_beams, temperature, top_p, do_sample, top_k, max_length):
        lang_code = language_mapping[lang_code]
        output_ids = mic_state.model.generate(input_ids=pixel_values, forced_bos_token_id=tokenizer.lang_code_to_id[lang_code], max_length=max_length, num_beams=num_beams, temperature=temperature, top_p = top_p, top_k=top_k, do_sample=do_sample)
        print(output_ids)
        output_sequence = tokenizer.batch_decode(output_ids[0], skip_special_tokens=True, max_length=max_length)
        return output_sequence

    mic_checkpoints = ["flax-community/clip-vit-base-patch32_mbart-large-50"]  # TODO: Maybe add more checkpoints?
    dummy_data = pd.read_csv("reference.tsv", sep="\t")

    first_index = 25
    # Init Session State
    if mic_state.image_file is None:
        mic_state.image_file = dummy_data.loc[first_index, "image_file"]
        mic_state.caption = dummy_data.loc[first_index, "caption"].strip("- ")
        mic_state.lang_id = dummy_data.loc[first_index, "lang_id"]

        image_path = os.path.join("images", mic_state.image_file)
        image = plt.imread(image_path)
        mic_state.image = image

    if mic_state.model is None:
        # Display Top-5 Predictions
        with st.spinner("Loading model..."):
            mic_state.model = load_model(mic_checkpoints[0])

    query1 = st.text_input(
        "Enter a URL to an image",
        value="http://images.cocodataset.org/val2017/000000397133.jpg",
    )

    col1, col2, col3 = st.beta_columns([2,1, 2])
    if col1.button(
        "Get a random example",
        help="Get a random example from the 100 `seeded` image-text pairs.",
    ):
        sample = dummy_data.sample(1).reset_index()
        mic_state.image_file = sample.loc[0, "image_file"]
        mic_state.caption = sample.loc[0, "caption"].strip("- ")
        mic_state.lang_id = sample.loc[0, "lang_id"]

        image_path = os.path.join("images", mic_state.image_file)
        image = plt.imread(image_path)
        mic_state.image = image

    col2.write("OR")

    if col3.button("Use above URL"):
        image_data = requests.get(query1, stream=True).raw
        image = np.asarray(Image.open(image_data))
        mic_state.image = image

    transformed_image = get_transformed_image(mic_state.image)

    new_col1, new_col2 = st.beta_columns([5,5])

    # Display Image
    new_col1.image(mic_state.image, use_column_width="always")
    # Display Reference Caption
    with new_col1.beta_expander("Reference Caption"):
        st.write("**Reference Caption**: " + mic_state.caption)
        st.markdown(
            f"""**English Translation**: {mic_state.caption if mic_state.lang_id == "en" else translate(mic_state.caption, 'en')}"""
        )

    # Select Language
    options = list(code_to_name.keys())
    lang_id = new_col2.selectbox(
        "Language",
        index=options.index(mic_state.lang_id),
        options=options,
        format_func=lambda x: code_to_name[x],
        help="The language in which caption is to be generated."
    )

    sequence = ['']
    if new_col2.button("Generate Caption", help="Generate a caption in the specified language."):
        with st.spinner("Generating Sequence... This might take some time, you can read our Article meanwhile!"):
            sequence = generate_sequence(transformed_image, lang_id, num_beams, temperature, top_p, do_sample, top_k, max_length)
    # print(sequence)

    if sequence!=['']:
        new_col2.write(
            "**Generated Caption**: "+sequence[0]
        )

        new_col2.write(
            "**English Translation**: "+ sequence[0] if lang_id=="en" else translate(sequence[0])
        )
        with new_col2:
            try:
                clean_text = re.sub(r'[^A-Za-z0-9 ]+', '', sequence[0])
                # st.write("**Cleaned Text**: ",clean_text)
                audio_bytes = voicerss_tts(clean_text, lang_id)
                st.markdown("**Audio for the generated caption**")
                st.audio(audio_bytes)
            except:
                st.info("Unabled to generate audio. Please try again in some time.")