import gradio as gr
import torch
from transformers import pipeline

app_title = "Portuguese Counter Hate Speech Detection (NFAA)"

app_description = """
This app is the culmination of the kNOwHATE consortium project, which aimed to tackle Online Hate Speech in the Portuguese comunity. It serves as an user-friendly interface to classify text and identify instances of Hate Speech.
This app leverages state-of-the-art Natural Language Processing models developed in the scope of this project to classify harmful text.
Select a model from the dropdown menu and input your text to see the classification results. Explore the examples of Hate Speech and Non-Hate Speech offered, and join us in fostering a safer and more respectful online community.
For more information about the kNOwHATE project and its initiatives, visit our website [here](https://knowhate.eu) and to explore and use these models visit our Hugging Face page [here](https://huggingface.co/knowhate).
"""

app_examples = [
    ["As pessoas tem que perceber que ser 'panasca' não é deixar de ser homem, é deixar de ser humano 😂😂", "", "knowhate/twt-bertimbau"],
    ["Vamo-nos unir para criar um mundo mais inclusivo e tolerante.", "", "knowhate/twt-xlm-base"]
]

model_list = [
    "knowhate/twt-bertimbau",
    "knowhate/twt-xlm-base",
]

def predict(text, target, chosen_model):    
    
    # Initialize the pipeline with the chosen model
    model_pipeline = pipeline("text-classification", model=chosen_model)
    result = model_pipeline(text)

    
    predicted_label = result[0]['label']
    predicted_score = result[0]['score']

    non_predicted_label = "Hate Speech" if predicted_label == "Non Hate Speech" else "Non Hate Speech"
    non_predicted_score = 1 - predicted_score

    scores_dict = {
        predicted_label: predicted_score,
        non_predicted_label: non_predicted_score
    }
    
    return scores_dict

inputs = [
    gr.Textbox(label="Context", value= app_examples[0][0]),
    gr.Textbox(label="Target", value= app_examples[0][1]),
    gr.Dropdown(label="Model", choices=model_list, value=model_list[0])
]

outputs = [
    gr.Label(label="Result"),
]

gr.Interface(fn=predict, inputs=inputs, outputs=outputs, title=app_title, 
             description=app_description, examples=app_examples, theme=gr.themes.Base(primary_hue="red")).launch()