import streamlit as st
from transformers import pipeline
from io import StringIO

unmasker = pipeline('fill-mask', model='dsfsi/zabantu-xlm-roberta')

st.set_page_config(layout="wide")

def fill_mask(sentences):
    results = {}
    warnings = []
    for (language, sentence) in sentences.items():
        if "<mask>" in sentence:
            masked_sentence = sentence.replace('<mask>', unmasker.tokenizer.mask_token)
            unmasked = unmasker(masked_sentence)
            results[sentence] = (language, unmasked)
        else:
            warnings.append(f"Warning: No <mask> token found in sentence: {sentence}")
    return results, warnings

def replace_mask(sentence, predicted_word):
    return sentence.replace("<mask>", f"**{predicted_word}**")

st.title("Fill Mask | Zabantu-XLM-Roberta")
st.write(f"")

st.markdown("Zabantu-XLMR refers to a fleet of models trained on different combinations of South African Bantu languages. It supports the following languages Tshivenda, Nguni languages (Zulu, Xhosa, Swati), Sotho languages (Northern Sotho, Southern Sotho, Setswana), and Xitsonga.")

col1, col2 = st.columns(2)

if 'text_input' not in st.session_state:
    st.session_state['text_input'] = ""

if 'warnings' not in st.session_state:
    st.session_state['warnings'] = []

with col1:
    with st.container(border=True):
        st.markdown("Input :clipboard:")

        select_options = ['Choose option', 'Enter text input', 'Upload a file(csv/txt)']
        sample_sentence = "Rabulasi wa <mask> u khou bvelela nga u lima."

        option_selected = st.selectbox(f"Select an input option:", select_options, index=0)

        if option_selected == 'Enter text input':
            text_input = st.text_area(
                "Enter sentences with <mask> token(one sentence per line):",
                value=st.session_state['text_input']
            )
        
            input_sentences = text_input.split("\n")
    
            if st.button("Submit",use_container_width=True):
                result, warnings = fill_mask(input_sentences)
                st.session_state['warnings'] = warnings 

        if option_selected == 'Upload a file(csv/txt)':
            
            uploaded_file = st.file_uploader("Choose a file-(one sentence per line)")
            if uploaded_file is not None:
                
                stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
                string_data = stringio.read()
                
                input_sentences = string_data.split("\n")
    
                if st.button("Submit",use_container_width=True):
                    result, warnings = fill_mask(input_sentences)
                    st.session_state['warnings'] = warnings 
    
        if st.session_state['warnings']:
            for warning in st.session_state['warnings']:
                st.warning(warning)

        st.markdown("Example")
        st.code(sample_sentence, wrap_lines=True)
        if st.button("Test Example",use_container_width=True):
            result, warnings = fill_mask(sample_sentence.split("\n"))

with col2:
    with st.container(border=True):
        st.markdown("Output :bar_chart:")
        if 'result' in locals() and result:  
            if len(result) == 1:
                for language, predictions in result.items():
                    for prediction in predictions:
                        predicted_word = prediction['token_str']
                        score = prediction['score'] * 100
    
                        st.markdown(f"""
                        <div class="bar">
                            <div class="bar-fill" style="width: {score}%;"></div>
                        </div>
                        <div class="container">
                            <div style="align-items: left;">{predicted_word}</div>
                            <div style="align-items: center;">{score:.2f}%</div>
                        </div>
                        """, unsafe_allow_html=True)

            else:
                for language, predictions in result.items():
                    if predictions:
                        top_prediction = predictions[0]
                        predicted_word = top_prediction['token_str']
                        score = top_prediction['score'] * 100
    
                        st.markdown(f"""
                        <div class="bar">
                            <div class="bar-fill" style="width: {score}%;"></div>
                        </div>
                        <div class="container">
                            <div style="align-items: left;">{predicted_word} ({language})</div>
                            <div style="align-items: right;">{score:.2f}%</div>
                        </div>
                        """, unsafe_allow_html=True)

                
if 'result' in locals():  
    if result:
        line = 0
        for sentence, predictions in result.items():
            line += 1
            predicted_word = predictions[0]['token_str']
            full_sentence = replace_mask(sentence, predicted_word)
            st.write(f"**Sentence {line}:** {full_sentence }")

css = """
<style>
footer {display:none !important;}

.gr-button-primary {
    z-index: 14;
    height: 43px;
    width: 130px;
    left: 0px;
    top: 0px;
    padding: 0px;
    cursor: pointer !important; 
    background: none rgb(17, 20, 45) !important;
    border: none !important;
    text-align: center !important;
    font-family: Poppins !important;
    font-size: 14px !important;
    font-weight: 500 !important;
    color: rgb(255, 255, 255) !important;
    line-height: 1 !important;
    border-radius: 12px !important;
    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
    box-shadow: none !important;
}
.gr-button-primary:hover{
    z-index: 14;
    height: 43px;
    width: 130px;
    left: 0px;
    top: 0px;
    padding: 0px;
    cursor: pointer !important;
    background: none rgb(66, 133, 244) !important;
    border: none !important;
    text-align: center !important;
    font-family: Poppins !important;
    font-size: 14px !important;
    font-weight: 500 !important;
    color: rgb(255, 255, 255) !important;
    line-height: 1 !important;
    border-radius: 12px !important;
    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
    box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important;
}
.hover\:bg-orange-50:hover {
    --tw-bg-opacity: 1 !important;
    background-color: rgb(229,225,255) !important;
}
.to-orange-200 {
    --tw-gradient-to: rgb(37 56 133 / 37%) !important;
}
.from-orange-400 {
    --tw-gradient-from: rgb(17, 20, 45) !important;
    --tw-gradient-to: rgb(255 150 51 / 0);
    --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important;
}
.group-hover\:from-orange-500{
    --tw-gradient-from:rgb(17, 20, 45) !important; 
    --tw-gradient-to: rgb(37 56 133 / 37%);
    --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important;
}
.group:hover .group-hover\:text-orange-500{
    --tw-text-opacity: 1 !important;
    color:rgb(37 56 133 / var(--tw-text-opacity)) !important;
}

.container {
    display: flex;
    justify-content: space-between;
    align-items: center;
    margin-bottom: 5px;
    width: 100%;
}
.bar {
    # width: 70%;
    background-color: #e6e6e6;
    border-radius: 12px;
    overflow: hidden;
    margin-right: 10px;
    height: 5px;
}
.bar-fill {
    background-color: #17152e;
    height: 100%;
    border-radius: 12px;
}

</style>
"""

st.markdown(css, unsafe_allow_html=True)