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import streamlit as st |
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from streamlit_chat import message |
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import pandas as pd |
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import os |
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import json |
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from transformers import pipeline |
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from dotenv import load_dotenv |
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from utils.cno_utils import convert_to_cno, get_cno_description |
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AVATAR_PATH = "https://avatars.githubusercontent.com/u/122880210?s=200&v=4" |
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st.set_page_config( |
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"Clasificador CNO 🤖", "🤖", layout="wide", initial_sidebar_state="expanded" |
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) |
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st.markdown( |
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"""<link href="https://fonts.googleapis.com/css2?family=Inter:wght@300;400;600&display=swap" rel="stylesheet"> |
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<style> |
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html, body, [class*="css"] {font-family: "Inter", sans-serif;} |
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header, footer {visibility: hidden;} |
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.block-container {padding-top: 0.5rem; display: flex; flex-direction: column; min-height: 100vh;} |
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.stChatMessage.user {background: linear-gradient(120deg,#00c6ff 0%,#0072ff 100%); color:#fff; border-radius:1rem; padding:0.75rem 1rem; margin:0.25rem 0;} |
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.stChatMessage.bot {background:#fff; border:1px solid #e0e0e0; border-radius:1rem; padding:0.75rem 1rem; margin:0.25rem 0;} |
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.stChatMessage.bot img {height:24px;width:24px;border-radius:50%;margin-right:0.5rem;} |
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::-webkit-scrollbar {width:8px;} |
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::-webkit-scrollbar-thumb {background:#8f9dff;border-radius:10px;} |
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.sidebar-title {font-size:0.9rem;font-weight:600;margin:0.5rem 0 0.25rem;color:#4b4b4b;} |
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div.msg ul{list-style:none;padding-left:0;margin:0;} |
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[data-testid="stSidebarCollapseButton"] { |
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display: none; |
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} |
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</style>""", |
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unsafe_allow_html=True, |
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) |
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MODEL_ID = "bob-nlp/A5-CNO-ULL-BOB-ISTAC-D12" |
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@st.cache_resource |
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def load_huggingface_model(): |
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"""Carga el pipeline de inferencia desde Hugging Face Hub.""" |
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load_dotenv() |
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hf_token = os.environ.get("HF_TOKEN") |
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if not hf_token: |
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st.error( |
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"HF_TOKEN no encontrado. Por favor, configúralo en los 'Secrets' de tu Space.", |
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icon="🔑", |
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) |
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return None |
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try: |
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model_pipeline = pipeline( |
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"text-classification", |
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model=MODEL_ID, |
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token=hf_token, |
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) |
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return model_pipeline |
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except Exception as e: |
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st.error(f"Error al cargar el modelo '{MODEL_ID}': {e}", icon="🔥") |
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return None |
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def load_json_file(filename): |
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try: |
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with open(filename, "r", encoding="utf-8") as f: |
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return json.load(f) |
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except FileNotFoundError: |
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st.warning( |
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f"El archivo '{filename}' no se ha encontrado en el repositorio del Space." |
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) |
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return {} |
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except Exception as e: |
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st.error(f"Error al leer el archivo JSON '{filename}': {e}") |
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return {} |
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pipe = load_huggingface_model() |
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METADATA = load_json_file("src/data/metadata.json") |
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PROBLEMATIC_CNOS = load_json_file("src/data/problematic_cnos.json") |
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def run_inference(text_input): |
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""" |
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Función que ejecuta la inferencia usando el pipeline de Hugging Face |
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y formatea la salida para mostrarla en la UI. |
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""" |
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if not pipe: |
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return "Error: El modelo no está cargado." |
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try: |
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results = pipe(text_input, top_k=3) |
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PROBLEMATIC_CNO_MESSAGE = " ⚠️⚠️⚠️ **Cuidado: código poco fiable** " |
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out = [] |
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for response in results: |
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response["label"] = convert_to_cno(response["label"]) |
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response["description"] = get_cno_description(response["label"]) |
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main_msg = ( |
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f"Predicción: **{response['label']}**: {response['description']} " |
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f"Certeza: **{response['score']:.2f}** " |
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) |
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if response["label"] in PROBLEMATIC_CNOS: |
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main_msg += PROBLEMATIC_CNO_MESSAGE |
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out.append(main_msg) |
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return "\n".join(out) |
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except Exception as e: |
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return f"Ocurrió un error durante la inferencia: {e}" |
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st.sidebar.title("Clasificador CNO-11") |
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st.sidebar.markdown("---") |
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st.sidebar.markdown( |
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"<div class='sidebar-title'>🎛️ Filtros</div>", unsafe_allow_html=True |
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) |
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def init_state() -> None: |
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defaults = load_json_file("src/data/defaults_session_state.json") |
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for k, v in defaults.items(): |
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st.session_state.setdefault(k, v) |
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init_state() |
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def on_controls_change() -> None: |
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st.session_state.past.clear() |
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st.session_state.generated.clear() |
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for col, metadatas in METADATA.items(): |
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sel = st.sidebar.selectbox( |
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label=col, |
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options=list(metadatas), |
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key=f"select_{col}", |
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on_change=on_controls_change, |
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format_func=lambda x: x["textual"], |
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) |
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st.session_state.selections[col] = sel |
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st.sidebar.divider() |
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if st.sidebar.button("🗑️ Limpiar conversación", use_container_width=True): |
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on_controls_change() |
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st.rerun() |
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st.title("🤖 Clasificador CNO-11 ULL BOB-ISTAC") |
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st.info( |
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f"Utilizando el modelo: **[{MODEL_ID}](https://huggingface.co/bob-nlp/A5-CNO-BOB-ISTAC-D12)**" |
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) |
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def add_user_message(text: str): |
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st.session_state.past.append(text) |
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to_classify = f"{text}." |
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if st.session_state.selections: |
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for col, sel in st.session_state.selections.items(): |
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if sel: |
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to_classify += f" {sel['textual']}." |
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response = run_inference(to_classify) |
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st.session_state.generated.append((response)) |
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def render_chat(): |
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message( |
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"¡Hola! Soy el clasificador de códigos CNO-11. Por favor, introduce una descripción de la tarea o ocupación que quieres clasificar y te ayudaré a encontrar el código CNO correspondiente.", |
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is_user=False, |
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key="welcome", |
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logo=AVATAR_PATH, |
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) |
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for i, (u, b) in enumerate(zip(st.session_state.past, st.session_state.generated)): |
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message(u, is_user=True, key=f"u{i}", avatar_style="no-avatar") |
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message(b, key=f"b{i}", logo=AVATAR_PATH) |
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chat_box = st.container() |
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with chat_box: |
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st.markdown('<div class="chat-container">', unsafe_allow_html=True) |
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render_chat() |
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new_text = st.chat_input("Escribe aquí el texto a clasificar…") |
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if new_text: |
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add_user_message(new_text) |
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st.rerun() |
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st.markdown("</div>", unsafe_allow_html=True) |
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st.markdown("---") |
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st.markdown( |
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""" |
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<div style="text-align: center; color: #666; font-size: 0.8rem; padding: 1rem 0;"> |
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Desarrollado por <strong>Cátedra Cajasiete de Big Data, Open Data y Blockchain</strong><br> |
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Universidad de La Laguna |
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</div> |
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""", |
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unsafe_allow_html=True, |
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) |
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