import gradio as gr
import spaces
from transformers import pipeline
import torch
import os 


token = os.getenv('HF_AUTH_TOKEN')

binary_example = [["Yahudi terörüne karşı protestolar kararlılıkla devam ediyor."]]
category_example = [["Ermeni zulmü sırasında hayatını kaybeden kadınlar anısına dikilen anıt ziyarete açıldı."]]
target_example = [["Dün 5 bin suriyeli enik doğmuştur zaten Türkiyede aq 5 bin suriyelinin gitmesi çok çok az"]]

DESCRIPTION = """"
## Hate Speech Detection in Turkish News
"""

CITATION = """"
"""

def binary_classification(input):
    model = pipeline(model='gokceuludogan/berturk_tr_hate_print_w0.1', token=token)
    return model(input)[0]

def category_classification(input):
    model = pipeline(model='gokceuludogan/berturk/tr_hateprint_cat_w0.1_b128', token=token)
    return model(input)[0]

def target_detection(input):
    model = pipeline(model='gokceuludogan/turna_generation_tr_hateprint_target', token=token)
    return model(input)[0]

with gr.Blocks(theme="abidlabs/Lime") as demo:

    #gr.Markdown("# TURNA")
    #gr.Image("images/turna-logo.png", width=100, show_label=False, show_download_button=False, show_share_button=False)

    with gr.Tab("TRHatePrint"):
        gr.Markdown(DESCRIPTION)
    
    with gr.Tab("Binary Classification"):
        gr.Markdown("Enter text to analyse hatefulness and pick the model.")
        with gr.Column():
            with gr.Row():
                with gr.Column():
                    # sentiment_choice = gr.Radio(choices = ["turna_", "berturk"], label ="Model", value="turna_")
                    sentiment_input = gr.Textbox(label="Input")
                
                    sentiment_submit = gr.Button()
                sentiment_output = gr.Textbox(label="Output")
                sentiment_submit.click(binary_classification, inputs=[sentiment_input], outputs=sentiment_output)
            sentiment_examples = gr.Examples(examples = binary_example, inputs = [sentiment_input], outputs=sentiment_output, fn=binary_classification)
        
    with gr.Tab("Hate Speech Categorization"):
        gr.Markdown("Enter a hateful text to categorize or try the example.")
        with gr.Column():
            with gr.Row():
                with gr.Column():
                    text_input = gr.Textbox(label="Input")
                
                    text_submit = gr.Button()
                text_output = gr.Textbox(label="Output")
                text_submit.click(category_classification, inputs=[text_input], outputs=text_output)
            text_examples = gr.Examples(examples = category_example,inputs=[text_input], outputs=text_output, fn=category_classification)
        
    
    with gr.Tab("Target Detection"):
        gr.Markdown("Enter text to detect targets ")
        with gr.Column():
            with gr.Row():
                with gr.Column():
                    nli_first_input = gr.Textbox(label="Input")
                    nli_submit = gr.Button()
                nli_output = gr.Textbox(label="Output")
                nli_submit.click(target_detection, inputs=[nli_first_input], outputs=nli_output)
            nli_examples = gr.Examples(examples = target_example, inputs = [nli_first_input], outputs=nli_output, fn=target_example)
    
    gr.Markdown(CITATION)

demo.launch()