import streamlit as st
import sparknlp

from sparknlp.base import *
from sparknlp.annotator import *
from pyspark.ml import Pipeline

# Page configuration
st.set_page_config(
    layout="wide", 
    initial_sidebar_state="auto"
)

# CSS for styling
st.markdown("""
    <style>
        .main-title {
            font-size: 36px;
            color: #4A90E2;
            font-weight: bold;
            text-align: center;
        }
        .section {
            background-color: #f9f9f9;
            padding: 10px;
            border-radius: 10px;
            margin-top: 10px;
        }
        .section p, .section ul {
            color: #666666;
        }
    </style>
""", unsafe_allow_html=True)

@st.cache_resource
def init_spark():
    return sparknlp.start()

@st.cache_resource
def create_pipeline(model):
    document_assembler = DocumentAssembler() \
        .setInputCol("text") \
        .setOutputCol("documents")

    t5 = T5Transformer() \
        .pretrained(model) \
        .setTask("question:")\
        .setMaxOutputLength(200)\
        .setInputCols(["documents"]) \
        .setOutputCol("answers")

    pipeline = Pipeline(stages=[document_assembler, t5])
    return pipeline

def fit_data(pipeline, data):
    df = spark.createDataFrame([[data]]).toDF("text")
    result = pipeline.fit(df).transform(df)
    return result.select('answers.result').collect()

# Sidebar content
model = st.sidebar.selectbox(
    "Choose the pretrained model",
    ['t5_base', 't5_small'],
    help="For more info about the models visit: https://sparknlp.org/models"
)

# Set up the page layout
title, sub_title = (
    'Automatically Answer Questions (OPEN BOOK)',
    'Automatically generate answers to questions without context.'
)

st.markdown(f'<div class="main-title">{title}</div>', unsafe_allow_html=True)
st.write(sub_title)
# Reference notebook link in sidebar
link = """
<a href="https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/streamlit_notebooks/public/QUESTION_ANSWERING_OPEN_BOOK.ipynb#scrollTo=SunNYkw3-Ic8">
    <img src="https://colab.research.google.com/assets/colab-badge.svg" style="zoom: 1.3" alt="Open In Colab"/>
</a>
"""
st.sidebar.markdown('Reference notebook:')
st.sidebar.markdown(link, unsafe_allow_html=True)

# Define the examples as a dictionary for easier access
examples = {
    "What does increased oxygen concentrations in the patient’s lungs displace?": """Hyperbaric (high-pressure) medicine uses special oxygen chambers to increase the partial pressure of O 2 around the patient and, when needed, the medical staff. Carbon monoxide poisoning, gas gangrene, and decompression sickness (the ’bends’) are sometimes treated using these devices. Increased O 2 concentration in the lungs helps to displace carbon monoxide from the heme group of hemoglobin. Oxygen gas is poisonous to the anaerobic bacteria that cause gas gangrene, so increasing its partial pressure helps kill them. Decompression sickness occurs in divers who decompress too quickly after a dive, resulting in bubbles of inert gas, mostly nitrogen and helium, forming in their blood. Increasing the pressure of O 2 as soon as possible is part of the treatment.""",
    "What category of game is Legend of Zelda: Twilight Princess?": """The Legend of Zelda: Twilight Princess (Japanese: ゼルダの伝説 トワイライトプリンセス, Hepburn: Zeruda no Densetsu: Towairaito Purinsesu?) is an action-adventure game developed and published by Nintendo for the GameCube and Wii home video game consoles. It is the thirteenth installment in the The Legend of Zelda series. Originally planned for release on the GameCube in November 2005, Twilight Princess was delayed by Nintendo to allow its developers to refine the game, add more content, and port it to the Wii. The Wii version was released alongside the console in North America in November 2006, and in Japan, Europe, and Australia the following month. The GameCube version was released worldwide in December 2006.""",
    "Who is founder of Alibaba Group?": """Alibaba Group founder Jack Ma has made his first appearance since Chinese regulators cracked down on his business empire. His absence had fuelled speculation over his whereabouts amid increasing official scrutiny of his businesses. The billionaire met 100 rural teachers in China via a video meeting on Wednesday, according to local government media. Alibaba shares surged 5% on Hong Kong's stock exchange on the news.""",
    "For what instrument did Frédéric write primarily for?": """Frédéric François Chopin (/ˈʃoʊpæn/; French pronunciation: ​[fʁe.de.ʁik fʁɑ̃.swa ʃɔ.pɛ̃]; 22 February or 1 March 1810 – 17 October 1849), born Fryderyk Franciszek Chopin,[n 1] was a Polish and French (by citizenship and birth of father) composer and a virtuoso pianist of the Romantic era, who wrote primarily for the solo piano. He gained and has maintained renown worldwide as one of the leading musicians of his era, whose "poetic genius was based on a professional technique that was without equal in his generation." Chopin was born in what was then the Duchy of Warsaw, and grew up in Warsaw, which after 1815 became part of Congress Poland. A child prodigy, he completed his musical education and composed his earlier works in Warsaw before leaving Poland at the age of 20, less than a month before the outbreak of the November 1830 Uprising.""",
    "The most populated city in the United States is which city?": """New York—often called New York City or the City of New York to distinguish it from the State of New York, of which it is a part—is the most populous city in the United States and the center of the New York metropolitan area, the premier gateway for legal immigration to the United States and one of the most populous urban agglomerations in the world. A global power city, New York exerts a significant impact upon commerce, finance, media, art, fashion, research, technology, education, and entertainment, its fast pace defining the term New York minute. Home to the headquarters of the United Nations, New York is an important center for international diplomacy and has been described as the cultural and financial capital of the world."""
}

# Create a select box for predefined examples
selected_text = st.selectbox('Select an Example:', list(examples.keys()))

# Add input fields for custom question and context
st.write('Try it yourself!')
custom_input_question = st.text_input('Create a question')
custom_input_context = st.text_input("Create its context")

# Check if custom input is provided
if custom_input_question and custom_input_context:
    custom_example = {custom_input_question: custom_input_context}
    selected_example_key = custom_input_question
    selected_example_context = custom_input_context
else:
    # Fallback to the selected example from dropdown
    selected_example_key = selected_text
    selected_example_context = examples[selected_text]

# Prepare the final selected_text
selected_text = f'"""{selected_example_key}""" """context : {selected_example_context}"""'

st.markdown('---')
# Display the selected or custom example
st.markdown(f"**Text:** {selected_example_key}")
st.markdown(f"**Context:** {selected_example_context}")


# Initialize Spark and create pipeline
spark = init_spark()
pipeline = create_pipeline(model)
output = fit_data(pipeline, selected_text)

# Display matched sentence
output_text = "".join(output[0][0])
st.markdown('---')
st.markdown(f"Answer: **{output_text.title()}**")