Delete app.py

app.py

@@ -1,334 +0,0 @@
-"""
-arXiv Article Classifier — Streamlit UI
-
-Запуск локально:
-    streamlit run app.py --server.port 8080
-"""
-
-import json
-import os
-import numpy as np
-import streamlit as st
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-# ---------------------------------------------------------------------------
-# Стили
-# ---------------------------------------------------------------------------
-st.markdown("""
-<style>
-    /* Фон */
-    .stApp { background-color: #f7faf7; }
-    .main .block-container { padding-top: 2rem; }
-
-    /* Заголовки */
-    h1 { color: #2d6a4f !important; letter-spacing: -0.5px; }
-    h2, h3 { color: #40916c !important; }
-
-    /* Текст */
-    p, label, .stMarkdown { color: #374151 !important; }
-
-    /* Radio */
-    .stRadio > label { color: #40916c !important; font-weight: 600; }
-
-    /* Поля ввода */
-    .stTextInput input, .stTextArea textarea {
-        background-color: #ffffff !important;
-        border: 1px solid #b7e4c7 !important;
-        color: #1f2937 !important;
-        border-radius: 8px !important;
-    }
-    .stTextInput input:focus, .stTextArea textarea:focus {
-        border-color: #52b788 !important;
-        box-shadow: 0 0 0 2px rgba(82,183,136,0.15) !important;
-    }
-    .stTextInput label, .stTextArea label {
-        color: #40916c !important;
-        font-weight: 600;
-    }
-
-    /* Кнопка */
-    .stButton > button {
-        background-color: #52b788 !important;
-        color: #ffffff !important;
-        border: none !important;
-        border-radius: 8px !important;
-        font-weight: 600;
-        transition: all 0.2s;
-    }
-    .stButton > button:hover {
-        background-color: #40916c !important;
-        color: #ffffff !important;
-    }
-
-    /* Divider */
-    hr { border-color: #d8f3dc !important; }
-
-    /* Success/error */
-    .stSuccess { background-color: #d8f3dc !important; color: #1b4332 !important; border-color: #95d5b2 !important; }
-    .stError   { background-color: #fef2f2 !important; }
-
-    /* Sidebar */
-    [data-testid="stSidebar"] {
-        background-color: #f0faf2 !important;
-        border-right: 1px solid #d8f3dc;
-    }
-    [data-testid="stSidebar"] p,
-    [data-testid="stSidebar"] span,
-    [data-testid="stSidebar"] div { color: #374151 !important; }
-    [data-testid="stSidebar"] a { color: #40916c !important; }
-
-    /* Карточка категории */
-    .cat-card {
-        background: #ffffff;
-        border: 1px solid #d8f3dc;
-        border-left: 4px solid #52b788;
-        border-radius: 8px;
-        padding: 10px 14px;
-        margin-bottom: 8px;
-    }
-    .cat-title { color: #1b4332; font-weight: 600; font-size: 0.95rem; }
-    .cat-code  { color: #74c69d; font-size: 0.78rem; font-family: monospace; margin-top: 2px; }
-    .cat-pct   { color: #40916c; font-size: 1.2rem; font-weight: 700; float: right; }
-
-    /* Заголовок колонки сравнения */
-    .col-header {
-        background: #d8f3dc;
-        border-radius: 8px;
-        padding: 8px 14px;
-        margin-bottom: 12px;
-        color: #1b4332 !important;
-        font-weight: 700;
-        font-size: 0.9rem;
-        text-align: center;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# ---------------------------------------------------------------------------
-# Конфиг моделей
-# ---------------------------------------------------------------------------
-MODELS = {
-    "large": {
-        "label":       "Большая",
-        "dir":         "./model_v2",
-        "base":        "allenai/scibert_scivocab_uncased",
-        "base_url":    "https://huggingface.co/allenai/scibert_scivocab_uncased",
-        "dataset":     "mteb/arxiv-clustering-p2p",
-        "dataset_url": "https://huggingface.co/datasets/mteb/arxiv-clustering-p2p",
-        "n_classes":   122,
-        "desc":        "SciBERT · 122 категории",
-        "topics":      "CS · Math · Physics · HEP · Astrophysics · Condensed Matter · Statistics · EESS · Quantitative Biology · Quantitative Finance · Economics · Nonlinear Sciences",
-    },
-    "small": {
-        "label":       "Простая",
-        "dir":         "./model",
-        "base":        "distilbert-base-cased",
-        "base_url":    "https://huggingface.co/distilbert-base-cased",
-        "dataset":     "ccdv/arxiv-classification",
-        "dataset_url": "https://huggingface.co/datasets/ccdv/arxiv-classification",
-        "n_classes":   11,
-        "desc":        "DistilBERT · 11 категорий",
-        "topics":      "cs.CV · cs.AI · cs.NE · cs.IT · cs.DS · cs.SY · cs.CE · cs.PL · math.AC · math.GR · math.ST",
-    },
-}
-
-MAX_LEN   = 256
-THRESHOLD = 0.95
-
-
-# ---------------------------------------------------------------------------
-# Загрузка модели
-# ---------------------------------------------------------------------------
-@st.cache_resource
-def load_model(model_dir: str):
-    device = (
-        "mps"  if torch.backends.mps.is_available() else
-        "cuda" if torch.cuda.is_available() else
-        "cpu"
-    )
-    tokenizer = AutoTokenizer.from_pretrained(model_dir)
-    model     = AutoModelForSequenceClassification.from_pretrained(model_dir)
-    model.to(device)
-    model.eval()
-
-    with open(f"{model_dir}/id2label.json") as f:
-        id2label = {int(k): v for k, v in json.load(f).items()}
-
-    label_full = {}
-    if os.path.exists(f"{model_dir}/label_full.json"):
-        with open(f"{model_dir}/label_full.json") as f:
-            label_full = json.load(f)
-
-    return tokenizer, model, id2label, label_full, device
-
-
-def predict_top95(title, abstract, model_dir):
-    tokenizer, model, id2label, label_full, device = load_model(model_dir)
-    text = title.strip()
-    if abstract.strip():
-        text = text + "\n\n" + abstract.strip()
-
-    enc = tokenizer(
-        text, max_length=MAX_LEN, padding="max_length",
-        truncation=True, return_tensors="pt",
-    ).to(device)
-
-    with torch.no_grad():
-        logits = model(**enc).logits
-
-    probs      = torch.softmax(logits, dim=-1).squeeze().cpu().numpy()
-    sorted_idx = np.argsort(probs)[::-1]
-
-    result, cumsum = [], 0.0
-    for idx in sorted_idx:
-        prob = float(probs[idx])
-        cat  = id2label[int(idx)]
-        result.append({
-            "category":    cat,
-            "full_name":   label_full.get(cat, cat),
-            "probability": prob,
-        })
-        cumsum += prob
-        if cumsum >= THRESHOLD:
-            break
-    return result
-
-
-def render_results(results):
-    for rank, r in enumerate(results, start=1):
-        pct = r["probability"] * 100
-        bar = int(r["probability"] * 20) * "█" + (20 - int(r["probability"] * 20)) * "░"
-        st.markdown(f"""
-        <div class="cat-card">
-            <span class="cat-pct">{pct:.1f}%</span>
-            <div class="cat-title">{rank}. {r['full_name']}</div>
-            <div class="cat-code">{r['category']}</div>
-            <div style="color:#95d5b2;font-size:0.75rem;letter-spacing:1px;margin-top:4px">{bar}</div>
-        </div>
-        """, unsafe_allow_html=True)
-
-
-# ---------------------------------------------------------------------------
-# UI
-# ---------------------------------------------------------------------------
-st.set_page_config(page_title="arXiv Classifier")
-
-st.markdown("# arXiv Classifier")
-st.markdown("<p style='color:#52b788;margin-top:-12px;margin-bottom:8px'>Классификация научных статей по тематике arxiv</p>", unsafe_allow_html=True)
-
-# Проверяем доступность моделей
-available = {k: v for k, v in MODELS.items() if os.path.exists(f"{v['dir']}/config.json")}
-if not available:
-    st.error("Модели не найдены. Сначала запустите обучение.")
-    st.stop()
-
-# ---------------------------------------------------------------------------
-# Режим работы
-# ---------------------------------------------------------------------------
-mode = st.radio(
-    "Режим",
-    ["Одна модель", "Сравнение моделей"],
-    horizontal=True,
-    label_visibility="collapsed",
-)
-
-# ---------------------------------------------------------------------------
-# Поля ввода
-# ---------------------------------------------------------------------------
-title    = st.text_input("Название статьи *", placeholder="Например: Attention Is All You Need")
-abstract = st.text_area(
-    "Аннотация (abstract)",
-    placeholder="Необязательно. Если не указана — классификация только по названию.",
-    height=150,
-)
-
-# Выбор модели (только в режиме одной)
-if mode == "Одна модель":
-    model_key = st.radio(
-        "Модель",
-        list(available.keys()),
-        format_func=lambda k: f"{available[k]['label']} — {available[k]['desc']}",
-        horizontal=True,
-    )
-    cfg = available[model_key]
-
-st.divider()
-run = st.button("Классифицировать", type="primary", use_container_width=True)
-
-# ---------------------------------------------------------------------------
-# Предсказание
-# ---------------------------------------------------------------------------
-if run:
-    if not title.strip():
-        st.error("Пожалуйста, введите название статьи.")
-        st.stop()
-
-    if mode == "Одна модель":
-        cfg = available[model_key]
-        with st.spinner("Предсказываем..."):
-            try:
-                results = predict_top95(title, abstract, cfg["dir"])
-            except Exception as e:
-                st.error(f"Ошибка: {e}"); st.stop()
-
-        st.success(f"Топ-{len(results)} категорий (суммарная вероятность ≥ 95%)")
-        render_results(results)
-
-    else:  # Сравнение
-        if len(available) < 2:
-            st.warning("Для сравнения нужны обе модели. Сейчас доступна только одна.")
-            st.stop()
-
-        with st.spinner("Запускаем обе модели..."):
-            try:
-                res_large = predict_top95(title, abstract, MODELS["large"]["dir"])
-                res_small = predict_top95(title, abstract, MODELS["small"]["dir"])
-            except Exception as e:
-                st.error(f"Ошибка: {e}"); st.stop()
-
-        col_l, col_r = st.columns(2)
-
-        with col_l:
-            st.markdown(
-                f"<div class='col-header'>{MODELS['large']['label']} — {MODELS['large']['desc']}</div>",
-                unsafe_allow_html=True,
-            )
-            render_results(res_large)
-
-        with col_r:
-            st.markdown(
-                f"<div class='col-header'>{MODELS['small']['label']} — {MODELS['small']['desc']}</div>",
-                unsafe_allow_html=True,
-            )
-            render_results(res_small)
-
-# ---------------------------------------------------------------------------
-# Сайдбар
-# ---------------------------------------------------------------------------
-with st.sidebar:
-    st.markdown("### О сервисе")
-
-    for key, cfg in available.items():
-        st.markdown(
-            f"**{cfg['label']}**  \n"
-            f"Модель: [{cfg['base']}]({cfg['base_url']})  \n"
-            f"Датасет: [{cfg['dataset']}]({cfg['dataset_url']})  \n"
-            f"Классов: **{cfg['n_classes']}**"
-        )
-        # Тематики в виде тегов
-        tags = cfg["topics"].split(" · ")
-        tags_html = " ".join(
-            f"<span style='display:inline-block;background:#d8f3dc;color:#1b4332;"
-            f"border-radius:4px;padding:1px 6px;font-size:0.72rem;"
-            f"margin:2px 2px 2px 0;font-family:monospace'>{t}</span>"
-            for t in tags
-        )
-        st.markdown(tags_html, unsafe_allow_html=True)
-        st.markdown("")
-
-    st.divider()
-    st.caption(
-        "**Top-95%** — категории выводятся по убыванию вероятности, "
-        "пока суммарная вероятность не превысит 95%."
-    )