Update app.py
Browse files
app.py
CHANGED
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@@ -1,6 +1,7 @@
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import os
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import io
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from typing import Optional, Tuple, List
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import numpy as np
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import pandas as pd
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@@ -22,7 +23,6 @@ MODEL_NAME = os.getenv("MODEL_NAME")
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SLIDER_MIN_YEAR = 2005 # фиксированный минимум
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# Для Excel (полный набор)
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COLUMN_LABELS_RU_EXCEL = {
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"№": "№",
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"score": "Сходство",
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@@ -97,7 +97,6 @@ DEFAULT_SCIENCES = {
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"Биологические",
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}
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# Паттерны для сопоставления с degree_pursued (на случай разных формулировок)
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SCIENCE_PATTERNS = {
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"Архитектура": ["архитектур"],
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"Биологические": ["биолог"],
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@@ -123,7 +122,6 @@ SCIENCE_PATTERNS = {
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def _keyify(label: str) -> str:
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# стабильный ключ для streamlit
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return "k_" + "".join(ch if ch.isalnum() else "_" for ch in label).strip("_")
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@@ -133,13 +131,11 @@ def _keyify(label: str) -> str:
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@st.cache_data(show_spinner="Загрузка данных...")
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def load_data():
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# 1) META
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ds_meta = load_dataset(HF_MERGED_REPO, split="train")
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df_meta = ds_meta.to_pandas()
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df_meta["registration_number"] = df_meta["registration_number"].astype(str)
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df_meta = df_meta.set_index("registration_number", drop=False)
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# 2) EMB
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ds_emb = load_dataset(HF_EMB_REPO, split="train")
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df_emb = ds_emb.to_pandas()
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df_emb["registration_number"] = df_emb["registration_number"].astype(str)
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@@ -150,7 +146,6 @@ def load_data():
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norms = np.linalg.norm(emb_matrix, axis=1, keepdims=True)
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emb_matrix = emb_matrix / np.maximum(norms, 1e-8)
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# выравниваем meta под reg_nums
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meta_aligned = df_meta.reindex(reg_nums)
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# dissertation_type masks
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@@ -208,15 +203,13 @@ def build_filter_mask(
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) -> np.ndarray:
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mask = np.ones(len(reg_nums), dtype=bool)
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# 1) Тип диссертации
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type_mask = np.zeros(len(reg_nums), dtype=bool)
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if candidate_selected:
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type_mask |= is_candidate
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if doctor_selected:
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type_mask |= is_doctor
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mask &= type_mask
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# 2) Науки
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if science_selected:
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sci_mask = np.zeros(len(reg_nums), dtype=bool)
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for label in science_selected:
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@@ -224,7 +217,6 @@ def build_filter_mask(
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sci_mask |= _contains_any(degree_lower, patterns)
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mask &= sci_mask
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# 3) Годы (NaN пропускаем)
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if year_range is not None:
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y0, y1 = int(year_range[0]), int(year_range[1])
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yr = year_arr
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@@ -321,18 +313,11 @@ def run_search(
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science_selected: List[str],
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year_range: Optional[Tuple[int, int]],
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):
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query = query.strip()
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if not query:
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return pd.DataFrame(), None, pd.DataFrame()
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mask = build_filter_mask(candidate_selected, doctor_selected, science_selected, year_range)
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results = search_core(query, top_k, mask=mask)
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df_raw = build_result_df(results)
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return pd.DataFrame(), None, df_raw
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# --- UI dataframe (скрываем №, рег.номер, тип) ---
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df_ui = df_raw.copy()
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df_ui = df_ui.rename(
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columns={
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@@ -344,11 +329,9 @@ def run_search(
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"vak_link": "ВАК",
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}
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)
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# Убираем не нужные колонки (как вы просили)
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df_ui = df_ui.drop(columns=["№", "registration_number", "dissertation_type"])
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#
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df_excel_ru = df_raw.rename(columns=COLUMN_LABELS_RU_EXCEL)
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output = io.BytesIO()
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with pd.ExcelWriter(output, engine="xlsxwriter") as writer:
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@@ -367,6 +350,16 @@ st.markdown(
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unsafe_allow_html=True,
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)
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data_has_years = np.isfinite(year_arr).any()
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year_max = int(np.nanmax(year_arr)) if data_has_years else None
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@@ -417,74 +410,135 @@ with st.form("search_form"):
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with c2:
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do_search = st.form_submit_button("🔍 Поиск", type="primary", use_container_width=True)
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if do_search:
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if not candidate_selected and not doctor_selected:
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st.warning("Выключены оба типа диссертаций. Включите «Кандидатские» и/или «Докторские».")
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df_ui_show,
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use_container_width=True,
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hide_index=True,
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column_order=["Сходство", "ФИО", "Название диссертации", "Организация", "Год", "ВАК"],
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column_config={
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"Сходство": st.column_config.NumberColumn("Сходство", format="%.4f", width="small"),
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"ФИО": st.column_config.TextColumn("ФИО", width="medium"),
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"Название диссертации": st.column_config.TextColumn("Название диссертации", width="large"),
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"Организация": st.column_config.TextColumn("Организация", width="medium"),
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"Год": st.column_config.NumberColumn("Год", width="small"),
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"ВАК": st.column_config.LinkColumn("ВАК", display_text="открыть", width="small"),
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},
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on_select="rerun",
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selection_mode="single-row",
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)
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st.
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else:
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st.info("Введите запрос
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st.markdown(
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"<p style='font-size: 0.8rem; text-align: right; color: gray;'>(с) Антон Лощилов, 2025</p>",
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unsafe_allow_html=True,
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import os
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import io
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from typing import Optional, Tuple, List
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from datetime import datetime, timezone
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import numpy as np
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import pandas as pd
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SLIDER_MIN_YEAR = 2005 # фиксированный минимум
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COLUMN_LABELS_RU_EXCEL = {
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"№": "№",
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"score": "Сходство",
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"Биологические",
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}
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SCIENCE_PATTERNS = {
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"Архитектура": ["архитектур"],
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"Биологические": ["биолог"],
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def _keyify(label: str) -> str:
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return "k_" + "".join(ch if ch.isalnum() else "_" for ch in label).strip("_")
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@st.cache_data(show_spinner="Загрузка данных...")
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def load_data():
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ds_meta = load_dataset(HF_MERGED_REPO, split="train")
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df_meta = ds_meta.to_pandas()
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df_meta["registration_number"] = df_meta["registration_number"].astype(str)
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df_meta = df_meta.set_index("registration_number", drop=False)
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ds_emb = load_dataset(HF_EMB_REPO, split="train")
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df_emb = ds_emb.to_pandas()
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df_emb["registration_number"] = df_emb["registration_number"].astype(str)
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norms = np.linalg.norm(emb_matrix, axis=1, keepdims=True)
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emb_matrix = emb_matrix / np.maximum(norms, 1e-8)
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meta_aligned = df_meta.reindex(reg_nums)
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# dissertation_type masks
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) -> np.ndarray:
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mask = np.ones(len(reg_nums), dtype=bool)
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type_mask = np.zeros(len(reg_nums), dtype=bool)
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if candidate_selected:
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type_mask |= is_candidate
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if doctor_selected:
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type_mask |= is_doctor
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mask &= type_mask
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if science_selected:
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sci_mask = np.zeros(len(reg_nums), dtype=bool)
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for label in science_selected:
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sci_mask |= _contains_any(degree_lower, patterns)
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mask &= sci_mask
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if year_range is not None:
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y0, y1 = int(year_range[0]), int(year_range[1])
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yr = year_arr
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science_selected: List[str],
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year_range: Optional[Tuple[int, int]],
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):
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mask = build_filter_mask(candidate_selected, doctor_selected, science_selected, year_range)
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results = search_core(query, top_k, mask=mask)
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df_raw = build_result_df(results)
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# UI: скрываем №, reg, type
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df_ui = df_raw.copy()
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df_ui = df_ui.rename(
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columns={
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"vak_link": "ВАК",
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}
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)
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df_ui = df_ui.drop(columns=["№", "registration_number", "dissertation_type"])
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# Excel: полный набор
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df_excel_ru = df_raw.rename(columns=COLUMN_LABELS_RU_EXCEL)
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output = io.BytesIO()
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with pd.ExcelWriter(output, engine="xlsxwriter") as writer:
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unsafe_allow_html=True,
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)
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# Подготовка session_state
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if "last_df_ui" not in st.session_state:
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st.session_state.last_df_ui = None
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if "last_df_raw" not in st.session_state:
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st.session_state.last_df_raw = None
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if "last_excel" not in st.session_state:
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st.session_state.last_excel = None
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if "requests_log" not in st.session_state:
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st.session_state.requests_log = [] # заглушка "БД"
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data_has_years = np.isfinite(year_arr).any()
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year_max = int(np.nanmax(year_arr)) if data_has_years else None
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with c2:
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do_search = st.form_submit_button("🔍 Поиск", type="primary", use_container_width=True)
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# Выполняем поиск и сохраняем результаты (чтобы они не исчезали на rerun)
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if do_search:
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if not candidate_selected and not doctor_selected:
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st.warning("Выключены оба типа диссертаций. Включите «Кандидатские» и/или «Докторские».")
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else:
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with st.spinner("Идёт поиск по базе диссертаций..."):
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df_ui, excel_bytes, df_raw = run_search(
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query=query,
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top_k=top_k,
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candidate_selected=candidate_selected,
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doctor_selected=doctor_selected,
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science_selected=science_selected,
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year_range=year_range,
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)
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st.session_state.last_df_ui = df_ui
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st.session_state.last_df_raw = df_raw
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st.session_state.last_excel = excel_bytes
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# ======= БЛОК РЕЗУЛЬТАТОВ (показывается, если есть сохранённые данные) =======
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df_ui_saved = st.session_state.last_df_ui
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df_raw_saved = st.session_state.last_df_raw
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excel_saved = st.session_state.last_excel
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if isinstance(df_ui_saved, pd.DataFrame) and not df_ui_saved.empty:
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st.success(f"Найдено записей: {len(df_ui_saved)}")
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df_ui_show = df_ui_saved[["Сходство", "ФИО", "Название диссертации", "Организация", "Год", "ВАК"]].copy()
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# Сортировка по клику на заголовок + чекбоксы выбора строк
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event = st.dataframe(
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df_ui_show,
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use_container_width=True,
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hide_index=True,
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column_order=["Сходство", "ФИО", "Название диссертации", "Организация", "Год", "ВАК"],
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column_config={
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"Сходство": st.column_config.NumberColumn("Сходство", format="%.4f", width="small"),
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"ФИО": st.column_config.TextColumn("ФИО", width="medium"),
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"Название диссертации": st.column_config.TextColumn("Название диссертации", width="large"),
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"Организация": st.column_config.TextColumn("Организация", width="medium"),
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"Год": st.column_config.NumberColumn("Год", width="small"),
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"ВАК": st.column_config.LinkColumn("ВАК", display_text="открыть", width="small"),
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},
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on_select="rerun",
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selection_mode="multi-row",
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)
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selected_rows = getattr(event, "selection", None).rows if getattr(event, "selection", None) else []
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selected_rows = list(selected_rows or [])
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# Детали (перенос текста) для выбранных
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if selected_rows:
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st.markdown("### Выбранные записи (предпросмотр)")
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for i in selected_rows[:10]:
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| 466 |
+
r = df_ui_show.iloc[i]
|
| 467 |
+
st.markdown(f"- **{r['ФИО']}** — {r['Название диссертации']} ({r['Год']})")
|
| 468 |
+
if len(selected_rows) > 10:
|
| 469 |
+
st.caption(f"Показаны первые 10 из {len(selected_rows)} выбранных.")
|
| 470 |
+
|
| 471 |
+
# Кнопка Excel
|
| 472 |
+
if excel_saved is not None:
|
| 473 |
+
st.download_button(
|
| 474 |
+
label="💾 Скачать результаты в Excel",
|
| 475 |
+
data=excel_saved,
|
| 476 |
+
file_name="search_results.xlsx",
|
| 477 |
+
mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
|
| 478 |
)
|
| 479 |
|
| 480 |
+
# ======= ЗАГЛУШКА "Запрос расширенной информации" =======
|
| 481 |
+
st.markdown("---")
|
| 482 |
+
st.subheader("Запрос расширенной информации")
|
| 483 |
+
|
| 484 |
+
with st.form("request_form"):
|
| 485 |
+
requester_fio = st.text_input("Ваше ФИО", placeholder="Иванов Иван Иванович")
|
| 486 |
+
requester_email = st.text_input("Email", placeholder="name@example.com")
|
| 487 |
+
requester_note = st.text_area(
|
| 488 |
+
"Дополнительная информация",
|
| 489 |
+
height=120,
|
| 490 |
+
placeholder="Что именно вы хотите уточнить/получить по выбранным диссертациям?",
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 491 |
)
|
| 492 |
|
| 493 |
+
st.caption(f"Выбрано записей: {len(selected_rows)}")
|
| 494 |
+
send_request = st.form_submit_button("📨 Отправить запрос", type="primary", use_container_width=True)
|
| 495 |
+
|
| 496 |
+
if send_request:
|
| 497 |
+
if not requester_fio.strip() or not requester_email.strip():
|
| 498 |
+
st.warning("Заполните «Ваше ФИО» и «Email».")
|
| 499 |
+
elif len(selected_rows) == 0:
|
| 500 |
+
st.warning("Выберите хотя бы одну запись в таблице (чекбоксами слева).")
|
| 501 |
+
else:
|
| 502 |
+
# Собираем payload (заглушка для отправки в БД)
|
| 503 |
+
# Берём данные из df_raw_saved по тем же индексам.
|
| 504 |
+
selected_payload_items = []
|
| 505 |
+
for i in selected_rows:
|
| 506 |
+
raw = df_raw_saved.iloc[i].to_dict()
|
| 507 |
+
selected_payload_items.append(
|
| 508 |
+
{
|
| 509 |
+
"author_fio": raw.get("fio"),
|
| 510 |
+
"title": raw.get("title"),
|
| 511 |
+
"org": raw.get("author_org_short"),
|
| 512 |
+
"year": raw.get("protection_year"),
|
| 513 |
+
"vak_link": raw.get("vak_link"),
|
| 514 |
+
"registration_number": raw.get("registration_number"),
|
| 515 |
+
}
|
| 516 |
+
)
|
| 517 |
+
|
| 518 |
+
payload = {
|
| 519 |
+
"created_at_utc": datetime.now(timezone.utc).isoformat(),
|
| 520 |
+
"requester": {
|
| 521 |
+
"fio": requester_fio.strip(),
|
| 522 |
+
"email": requester_email.strip(),
|
| 523 |
+
"note": requester_note.strip(),
|
| 524 |
+
},
|
| 525 |
+
"items": selected_payload_items,
|
| 526 |
+
}
|
| 527 |
+
|
| 528 |
+
# "Отправка в БД" — заглушка: сохраняем в session_state и показываем
|
| 529 |
+
st.session_state.requests_log.append(payload)
|
| 530 |
+
|
| 531 |
+
st.success("Запрос принят (заглушка). Ниже — данные, которые будут отправляться в БД.")
|
| 532 |
+
st.json(payload)
|
| 533 |
+
|
| 534 |
+
with st.expander("История запросов (заглушка)", expanded=False):
|
| 535 |
+
st.write(f"Всего запросов в текущей сессии: {len(st.session_state.requests_log)}")
|
| 536 |
+
st.json(st.session_state.requests_log[-1])
|
| 537 |
+
|
| 538 |
else:
|
| 539 |
+
st.info("Введите запрос и нажмите «Поиск». После этого результаты будут доступны для выбора и отправки запроса.")
|
| 540 |
|
| 541 |
+
# Футер
|
| 542 |
st.markdown(
|
| 543 |
"<p style='font-size: 0.8rem; text-align: right; color: gray;'>(с) Антон Лощилов, 2025</p>",
|
| 544 |
unsafe_allow_html=True,
|