Create app.py
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app.py
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import gradio as gr
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from transformers import pipeline
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title = "Automatic Readability Assessment of Texts in Spanish"
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description = """
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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!
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"""
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article = """
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### What's Readability Assessment?
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[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.
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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).
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However, their dependance on surface statistics (e.g. average sentence length) makes them unreliable.
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As such, developing models that could estimate a text's readability by "looking beyond the surface" is a necessity.
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### Goal
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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/).
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### More Information
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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).
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### Team
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- [Laura Vásquez-Rodríguez](https://lmvasque.github.io/)
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- Pedro Cuenca
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- Sergio Morales
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- [Fernando Alva-Manchego](https://feralvam.github.io/)
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"""
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examples = [
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["Esta es una frase simple.", "simple or complex?"],
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["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?"],
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]
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model_binary = pipeline("sentiment-analysis", model="hackathon-pln-es/readability-es-sentences", return_all_scores=True)
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def predict(text, levels):
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if levels == 0:
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predicted_scores = model_binary(text)[0]
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output_scores = {}
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for e in predicted_scores:
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output_scores[e['label']] = e['score']
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return output_scores
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iface = gr.Interface(
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fn=predict,
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inputs=[
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gr.inputs.Textbox(lines=7, placeholder="Write a text in Spanish.", label="Text in Spanish"),
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# gr.inputs.Radio(choices=["simple or complex?", "basic, intermediate, or advanced?"], type="index", label="Readability Levels"),
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gr.inputs.Radio(choices=["simple or complex?"], type="index", label="Readability Levels"),
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],
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outputs=[
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gr.outputs.Label(num_top_classes=3, label="Predicted Readability Level")
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],
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theme="huggingface",
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title = title, description = description, article = article, examples=examples,
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allow_flagging="never",
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)
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iface.launch()
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