import streamlit as st

st.set_page_config(page_title="Turkish Text Classification Tasks - via AG", page_icon='📖')
st.header("📖News Classification - TR")

with st.sidebar:
    hf_key = st.text_input("HuggingFace Access Key", key="hf_key", type="password")

MODEL_NEW = {
    "albert": "anilguven/albert_tr_turkish_news",  
    "distilbert": "anilguven/distilbert_tr_turkish_news",
    "bert": "anilguven/bert_tr_turkish_news",
    "xlm-roberta": "anilguven/xlm-roberta_tr_turkish_news",
    "electra": "anilguven/electra_tr_turkish_news",
}

MODEL_NEWS = ["albert","distilbert","bert","xlm-roberta","electra"]

# Use a pipeline as a high-level helper
from transformers import pipeline
# Create a mapping from formatted model names to their original identifiers
def format_model_name(model_key):
    name_parts = model_key
    formatted_name = ''.join(name_parts)  # Join them into a single string with title case
    return formatted_name

formatted_names_to_identifiers = {
    format_model_name(key): key for key in MODEL_NEW.keys()
}

with st.expander("About this app"):
    st.write(f"""
    1-Choose your model for news classification (Model has 7 label).\n
    2-Enter your sample news text.\n
    3-And model predict your text's result. 
    """)
    
# Debug to ensure names are formatted correctly
#st.write("Formatted Model Names to Identifiers:", formatted_names_to_identifiers)

model_name: str = st.selectbox("Model", options=MODEL_NEWS)
selected_model = MODEL_NEW[model_name]

if not hf_key:
    st.info("Please add your HuggingFace Access Key to continue.")
    st.stop()

access_token = hf_key
pipe = pipeline("text-classification", model=selected_model, token=access_token)

#from transformers import AutoTokenizer, AutoModelForSequenceClassification
#tokenizer = AutoTokenizer.from_pretrained(selected_model)
#pipe = AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path=selected_model)

comment = st.text_input("Enter your news text for analysis")#User input

st.text('')
if st.button("Submit for Analysis"):
    if not hf_key:
        st.info("Please add your HuggingFace Access Key to continue.")
        st.stop()
    else:
        result = pipe(comment)[0]
        label=''
        if result["label"] == "LABEL_0": label = "Economy"
        elif result["label"] == "LABEL_1": label = "Magazine"
        elif result["label"] == "LABEL_2": label = "Health"
        elif result["label"] == "LABEL_3": label = "Politics"
        elif result["label"] == "LABEL_4": label = "Sports"
        elif result["label"] == "LABEL_5": label = "Technology"
        else: label = "Events"
        st.text(label + " comment with " + str(result["score"]) + " accuracy")