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

unmasker = pipeline('fill-mask', model='dsfsi/zabantu-ven-120m')

st.set_page_config(layout="wide")

def fill_mask(sentences):
    results = {}
    warnings = []
    for sentence in sentences:
        if "<mask>" in sentence:
            unmasked = unmasker(sentence)
            results[sentence] = 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-ven-120m")
st.write(f"")

st.markdown("This is a variant of Zabantu pre-trained on a monolingual dataset of Tshivenda(ven) sentences on a transformer network with 120 million traininable parameters.")

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 = "Vhana vhane vha kha ḓi bva u bebwa vha kha khombo ya u <mask> nga Listeriosis."

        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 sentence, 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:
                index = 0
                for sentence, predictions in result.items():
                    index += 1
                    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} (line {index})</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)