import streamlit as st
from transformers import pipeline

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

sample_sentences = {
    'Zulu': "Le ndoda ithi izo____ ukudla.",  
    'Tshivenda': "Mufana uyo____ vhukuma.",  
    'Sepedi': "Mosadi o ____ pheka.",  
    'Tswana': "Monna o ____ tsamaya.",  
    'Tsonga': "N'wana wa xisati u ____ ku tsaka."  
}

def fill_mask_for_languages(sentences):
    results = {}
    for language, sentence in sentences.items():
        masked_sentence = sentence.replace('____', unmasker.tokenizer.mask_token)
        unmasked = unmasker(masked_sentence)
        results[language] = unmasked 
    return results

def replace_mask(sentence, predicted_word):
    return sentence.replace("____", f"**predicted_word**")

st.title("Fill Mask for Multiple Languages | Zabantu-XLM-Roberta")
st.write("This app predicts the missing word for sentences in Zulu, Tshivenda, Sepedi, Tswana, and Tsonga using a Zabantu BERT model.")
st.write(f"")

col1, col2 = st.columns(2)

with col1:   
    user_sentence = st.text_area("Enter your own sentence with a masked word (use '____'):", "\n".join(
        f"'{lang}': '{sentence}'," for lang, sentence in sample_sentences.items()
    ))

    if st.button("Submit"):
        user_masked_sentence = user_sentence.replace('____', unmasker.tokenizer.mask_token)

with col2:
    if 'user_masked_sentence' in locals():  
        if user_masked_sentence:
            user_predictions = unmasker(user_masked_sentence)
            
            # st.write(user_predictions)  
        
            if len(user_predictions) > 0:
                # st.write(f"Top prediction for the masked token: {user_predictions[0]['sequence']}")
        
                st.write("### Predictions for Sample Sentences:")
                predictions = fill_mask_for_languages(sample_sentences)
                    st.write(f"{predictions}")

if 'predictions' in locals():  
        if predictions:
                for language, language_predictions in predictions.items():
                    # original_sentence = sample_sentences[language]
                    predicted_sentence = replace_mask(original_sentence, language_predictions[0]['token_str'])  
                    # st.write(language_predictions)
                    # st.write(f"Original sentence ({language}): {original_sentence}")
                    st.write(f"{language}: {predicted_sentence}\n")
                   

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)