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import streamlit as st |
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import pandas as pd |
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import utils |
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import time |
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from transformers import pipeline |
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from transformers import AutoTokenizer |
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from transformers import AutoModelForSequenceClassification |
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model_berto='hackathon-somos-nlp-2023/DiagTrast-Berto' |
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tokenizer_berto = AutoTokenizer.from_pretrained(model_berto) |
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classifier_berto = pipeline("text-classification", model=model_berto) |
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model_xml='hackathon-somos-nlp-2023/DiagTrast-xlm-roberta-base' |
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tokenizer_xml = AutoTokenizer.from_pretrained(model_xml) |
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classifier_xml = pipeline("text-classification", model=model_xml) |
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st.title('🏥 Diagnóstico de Trastornos Mentales') |
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DemoTab, AboutTab = st.tabs(["Demo", "Acerca de"]) |
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with AboutTab: |
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st.subheader("Motivación") |
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st.markdown( |
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"[Colocar aquí la motivación]." |
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) |
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st.subheader("Recursos") |
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st.markdown(""" |
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Modelos usados: |
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- [hackathon-somos-nlp-2023/DiagTrast-Berto](https://huggingface.co/hackathon-somos-nlp-2023/DiagTrast-Berto) |
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- [hackathon-somos-nlp-2023/DiagTrast-xlm-roberta-base](https://huggingface.co/hackathon-somos-nlp-2023/DiagTrast-xlm-roberta-base) |
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Dataset: |
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- [hackathon-somos-nlp-2023/DiagTrast](https://huggingface.co/datasets/hackathon-somos-nlp-2023/DiagTrast) |
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""") |
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st.subheader("Equipo") |
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st.markdown(""" |
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- [Alberto Martín Garrido](https://huggingface.co/Stremie) |
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- [Edgar Mencia]() |
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- [Miguel Ángel Solís Orozco](https://huggingface.co/homosapienssapiens) |
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- [Jose Carlos Vílchez Villegas](https://huggingface.co/JCarlos) |
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""") |
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with DemoTab: |
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with st.form(key="diagtrast_form"): |
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sintomas = st.text_input(label = 'Introduce síntomas:', |
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value = 'La gente cuando me ve por la calle me mira y halagan mi belleza') |
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submit_button = st.form_submit_button(label="Clasificar") |
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if submit_button and not sintomas: |
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st.warning("⚠️ Debe introducir los síntomas.") |
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elif submit_button: |
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with st.spinner('Clasificando...'): |
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pred_berto = classifier_berto.predict(utils.clean_text(sintomas)) |
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pred_xml = classifier_xml.predict(utils.clean_text(sintomas)) |
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df = pd.DataFrame({ |
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'Modelo': ["DiagTrast-Berto", "DiagTrast-xlm-roberta-base"], |
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'Diagnóstico': [pred_berto[0]['label'], pred_xml[0]['label']], |
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'Score': [f"{pred_berto[0]['score']:.2%}", f"{pred_xml[0]['score']:.2%}"] |
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}) |
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st.markdown("### Resultados:") |
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st.caption("") |
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st.dataframe(df, use_container_width=True) |
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st.caption("") |
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alert = st.success("✅ ¡Hecho!") |
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