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

unmasker = pipeline('fill-mask', model='dsfsi/zabantu-bantu-250m')

st.set_page_config(layout="wide")

def fill_mask(sentences):
    results = {}
    warnings = []
    # warnings.append(f"= {sentences.items()}")
    for key, (language, sentence) in sentences.items():
        if language == 'choose language':
            warnings.append(f"Warning: Choose language for {sentence}")
            continue
            
        if language != 'choose language' and sentence == "":
            warnings.append(f"Warning: Enter sentence for {language}")
            continue
        
        if "<mask>" in sentence:
            masked_sentence = sentence.replace('<mask>', unmasker.tokenizer.mask_token)
            unmasked = unmasker(masked_sentence)
            results[key] = (unmasked,language,sentence)
        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'] = []

input_sentences = {}
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 = {'tshivenda': "Rabulasi wa <mask> u khou bvelela nga u lima.",
                "tsonga": "N'wana wa xisati u <mask> ku tsaka."
        }
        language_options = ['Choose language', 'Zulu', 'Tshivenda', 'Sepedi', 'Tswana', 'Tsonga']
        
        option_selected = st.selectbox(f"Select an input option:", select_options, index=0)
        
        if option_selected == 'Enter text input':
            st.session_state['warnings'].clear()
            @st.fragment
            def choose_language(i):
                language = st.selectbox(f"Select language for input {i+1}:", 
                     language_options, key=f'language_{i}', index=0)
                return language
            
            input1, input2 = st.columns(2)
            for i in range(5):
                with input1:
                    language = choose_language(i)
                    # st.write(f"lang : {language}")
                with input2:
                    sentence = st.text_input(f"Enter sentence for input {i+1} (with <mask>):", key=f'text_input_{i}')
                    if sentence:
                        if language:
                            input_sentences[f'{i+1}'] = (language.lower(), sentence)
                        else:
                            warnings = []
                            warnings.append(f"Warning: Choose the language for input {i+1}")
                            st.session_state['warnings'] = warnings 

            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.session_state['warnings'].clear()

        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:
                warnings = []
                stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
                string_data = stringio.read()
                
                sentences = string_data.split("\n")

                i = 0
                for sentence in sentences:
                    i += 1
                    if ":" in sentence:
                        splitted = sentence.split(":")
                        language = splitted[0]
                        sentence_mask = splitted[1]
                        input_sentences[f'{i}'] = (language.lower(), sentence)
                        
                    else:
                        warnings.append(f"Warning: No ':' token found in sentence: {sentence} in line {i}")
                        st.session_state['warnings'] = warnings 
    
                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.session_state['warnings'].clear()

        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)

with col2:
    with st.container(border=True):
        st.markdown("Output :bar_chart:")
        if 'result' in locals() and result:  
            if len(result) == 1:
                for key,(predictions, language, sentence) 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 key,(predictions, language, sentence) 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 key,(predictions, language, sentence) 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)