import gradio as gr

from transformers import pipeline

title = "Automatic Readability Assessment of Texts in Spanish"

description = """
Is a text **complex** or **simple**? Can it be understood by someone learning Spanish with a **basic**, **intermediate** or **advanced** knowledge of the language (*coming soon!*)? Find out with our models below!
"""

article = """

### What's Readability Assessment?

[Automatic Readability Assessment](https://arxiv.org/abs/2105.00973) consists of determining "how difficult" it could be to read and understand a piece of text.
This could be estimated using readability formulas, such as [Flesch for English](https://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_readability_tests) or [similar ones for Spanish](https://www.siicsalud.com/imagenes/blancopet1.pdf).
However, their dependance on surface statistics (e.g. average sentence length) makes them unreliable.
As such, developing models that could estimate a text's readability by "looking beyond the surface" is a necessity.

### Goal

We aim to contribute to the development of **neural models for readability assessment for Spanish**, following previous work for [English](https://aclanthology.org/2021.cl-1.6/) and [Filipino](https://aclanthology.org/2021.ranlp-1.69/).


### More Information

Details about how we trained these models can be found in our [report](https://wandb.ai/readability-es/readability-es/reports/Texts-Readability-Analysis-for-Spanish--VmlldzoxNzU2MDUx).


### Team

- [Laura Vásquez-Rodríguez](https://lmvasque.github.io/)
- Pedro Cuenca
- Sergio Morales
- [Fernando Alva-Manchego](https://feralvam.github.io/)

"""

examples = [
    ["Esta es una frase simple.", "simple or complex?"],
    ["La ciencia nos enseña, en efecto, a someter nuestra razón a la verdad y a conocer y juzgar las cosas como son, es decir, como ellas mismas eligen ser y no como quisiéramos que fueran.", "simple or complex?"],
]


model_binary = pipeline("sentiment-analysis", model="hackathon-pln-es/readability-es-sentences", return_all_scores=True)
model_ternary = pipeline("sentiment-analysis", model="hackathon-pln-es/readability-es-3class-sentences", return_all_scores=True)

def predict(text, levels):
    if levels == 0:
        predicted_scores = model_binary(text)[0]
    else:
        predicted_scores = model_ternary(text)[0]
    
    output_scores = {}
    for e in predicted_scores:
        output_scores[e['label']] = e['score']
    
    return output_scores 


iface = gr.Interface(
    fn=predict,
    inputs=[
        gr.inputs.Textbox(lines=7, placeholder="Write a text in Spanish.", label="Text in Spanish"),
        # gr.inputs.Radio(choices=["simple or complex?", "basic, intermediate, or advanced?"], type="index", label="Readability Levels"),
        gr.inputs.Radio(choices=["simple or complex?"], type="index", label="Readability Levels"),
    ],
    outputs=[
        gr.outputs.Label(num_top_classes=3, label="Predicted Readability Level")
    ],
    theme="huggingface",
    title = title, description = description, article = article, examples=examples,
    allow_flagging="never",
)
iface.launch()