Update app.py

app.py

@@ -1,81 +1,201 @@
+import os, json, calendar
+from datetime import date, timedelta
 
-import os, json
-os.environ["KERAS_BACKEND"] = "tensorflow"  # сказать Keras-3, что бэкенд — TF
-
+# Keras 3 поверх TF
+os.environ["KERAS_BACKEND"] = "tensorflow"
 import numpy as np
 import pandas as pd
-import gradio as gr
 import joblib
-import keras   # standalone Keras 3
+import gradio as gr
+import keras  # standalone Keras 3
 
-MODEL_PATH = "neiro1/model_v6.keras"
-PP_PATH    = "neiro1/preprocess_v6.joblib"
-META_PATH  = "neiro1/meta_v6.json"
+# ====== ПУТИ К АРТЕФАКТАМ ======
+MODEL_DIR = "neiro1"  # в Space лежит папка neiro1 рядом с app.py
+MODEL_PATH = os.path.join(MODEL_DIR, "model_v6.keras")
+PP_PATH    = os.path.join(MODEL_DIR, "preprocess_v6.joblib")
+META_PATH  = os.path.join(MODEL_DIR, "meta_v6.json")
 
-# === загрузка артефактов ===
-model = keras.models.load_model(MODEL_PATH, compile=False)  # compile=False чтобы не требовать старые оптимайзеры
-preprocess = joblib.load(PP_PATH)
+# ====== ЗАГРУЗКА ======
+model = keras.models.load_model(MODEL_PATH, compile=False)
+ct    = joblib.load(PP_PATH)
 with open(META_PATH, "r", encoding="utf-8") as f:
-    meta = json.load(f)
+    META = json.load(f)
+
+# ====== ПАРАМЕТРЫ И МЕТАДАННЫЕ ======
+ROUTE = META.get("route", "SFO_MIA")
+ORIGIN, DEST = ROUTE.split("_")
+AIRLINES = META.get("airlines", [])
+M_BLEND  = {int(k): float(v) for k, v in META.get("blend_month", {}).items()}
+A_BLEND  = {str(k): float(v) for k, v in META.get("blend_airline", {}).items()}
+AM_MEANS = {k: float(v) for k, v in META.get("am_means", {}).items()}
+AM_COUNTS= {k: int(v)   for k, v in META.get("am_counts", {}).items()}
+W_MONTH, W_AIR, W_AM = META.get("w_month", 0.0), META.get("w_air", 0.0), META.get("w_am", 0.0)
+FLOOR_FRAC = float(META.get("floor_frac", 0.25))
+EXOG       = META.get("exog_params", {})
+K_AIRLINE  = float(META.get("calibration_k_airline", 1.0))
+K_ROUTE    = float(META.get("calibration_k_route", 1.0))
+WHITELIST  = META.get("airline_whitelist", AIRLINES)
+SHARE      = META.get("airline_share_overall", {})
+CAP_TARGET = float(META.get("capacity_target_summer", 1000.0))
+
+# ====== ХЕЛПЕРЫ ======
+def nth_weekday(year, month, weekday, n):
+    d = date(year, month, 1)
+    add = (weekday - d.weekday()) % 7
+    return d + timedelta(days=add) + timedelta(weeks=n-1)
+
+def last_weekday(year, month, weekday):
+    d = date(year, month, calendar.monthrange(year, month)[1])
+    sub = (d.weekday() - weekday) % 7
+    return d - timedelta(days=sub)
+
+def daterange(d0, d1):
+    cur, out = d0, []
+    while cur <= d1:
+        out.append(cur)
+        cur += timedelta(days=1)
+    return out
+
+def holiday_windows_for_year(y):
+    mem  = set(daterange(last_weekday(y,5,0)-timedelta(days=3), last_weekday(y,5,0)))
+    july = set(daterange(date(y,6,30), date(y,7,7)))
+    labor= set(daterange(nth_weekday(y,9,0,1)-timedelta(days=3), nth_weekday(y,9,0,1)))
+    tg   = set(daterange(nth_weekday(y,11,3,4)-timedelta(days=2), nth_weekday(y,11,3,4)+timedelta(days=3)))
+    xny  = set(daterange(date(y,12,20), date(y,12,31))) | set(daterange(date(y+1,1,1), date(y+1,1,5)))
+    return {"memorial":mem, "july4":july, "labor":labor, "thanksgiving":tg, "xmas_newyear":xny}
+
+def exog_multiplier(ts: pd.Timestamp) -> float:
+    y = int(ts.year); m = int(ts.month); dow = int(ts.weekday())
+    P = {k: float(EXOG.get(k, v)) for k, v in dict(
+        SUMMER_MULT=1.60, FRI_MULT=1.10, SAT_MULT=1.15, SUN_MULT=1.10,
+        SPRING_BREAK_MULT=1.12, MEMORIAL_MULT=1.35, JULY4_MULT=1.30,
+        LABOR_MULT=1.25, THANKSGIVING_MULT=1.60, XMAS_NEWYEAR_MULT=1.70
+    ).items()}
+    mult = 1.0
+    if m in (6,7,8): mult *= P["SUMMER_MULT"]
+    if pd.Timestamp(year=y, month=3, day=10) <= ts <= pd.Timestamp(year=y, month=4, day=10):
+        mult *= P["SPRING_BREAK_MULT"]
+    if dow == 4: mult *= P["FRI_MULT"]
+    if dow == 5: mult *= P["SAT_MULT"]
+    if dow == 6: mult *= P["SUN_MULT"]
+    hw = holiday_windows_for_year(y); hw_prev = holiday_windows_for_year(y-1)
+    if ts.date() in hw["memorial"]:     mult *= P["MEMORIAL_MULT"]
+    if ts.date() in hw["july4"]:        mult *= P["JULY4_MULT"]
+    if ts.date() in hw["labor"]:        mult *= P["LABOR_MULT"]
+    if ts.date() in hw["thanksgiving"]: mult *= P["THANKSGIVING_MULT"]
+    if ts.date() in hw["xmas_newyear"] or ts.date() in hw_prev["xmas_newyear"]:
+        mult *= P["XMAS_NEWYEAR_MULT"]
+    return float(mult)
+
+def _blend(child_mean, child_n, prior_mean, prior_w):
+    child_n = float(child_n)
+    return float((child_n*child_mean + prior_w*prior_mean) / (child_n + prior_w))
 
-# Список фич для x_in
-FEATURES = None
-for attr in ["feature_names_in_", "features_in_", "input_features_"]:
-    if hasattr(preprocess, attr):
+def baseline_pair(a, m):
+    gmean = np.mean(list(A_BLEND.values())) if A_BLEND else 0.0
+    a0 = A_BLEND.get(str(a), gmean)
+    m0 = M_BLEND.get(int(m), gmean)
+    prior = 0.5*a0 + 0.5*m0
+    key = f"{a}_{int(m)}"
+    if key in AM_MEANS:
+        mean = AM_MEANS[key]; cnt = AM_COUNTS.get(key, 0)
+        return _blend(mean, cnt, prior, W_AM)
+    return float(prior)
+
+def make_inputs(airline: str, date_str: str):
+    d = pd.to_datetime(date_str)
+    dow, month = int(d.weekday()), int(d.month)
+    weekofyear = int(d.isocalendar().week)
+    is_weekend = int(dow in [5,6])
+    sin_dow, cos_dow = np.sin(2*np.pi*dow/7), np.cos(2*np.pi*dow/7)
+    sin_mon, cos_mon = np.sin(2*np.pi*(month-1)/12), np.cos(2*np.pi*(month-1)/12)
+    exog = exog_multiplier(d)
+    X_df = pd.DataFrame([{
+        "airline": airline,
+        "sin_dow": sin_dow, "cos_dow": cos_dow,
+        "sin_month": sin_mon, "cos_month": cos_mon,
+        "weekofyear": weekofyear, "is_weekend": is_weekend,
+        "exog_mult": exog,
+    }])
+    base = baseline_pair(airline, month) * exog
+    return ct.transform(X_df), np.array([[base]], dtype=float), dict(
+        dow=dow, month=month, weekofyear=weekofyear, is_weekend=bool(is_weekend),
+        exog=exog, baseline=float(base)
+    )
+
+def predict_one(airline, date_str):
+    X, B, info = make_inputs(airline, date_str)
+    y_raw = float(model.predict([X, B], verbose=0)[0,0])
+    y_cal = y_raw * K_AIRLINE
+    # safety floor: минимум 25% от baseline (или META['floor_frac'])
+    floor_val = float(B[0,0]) * max(FLOOR_FRAC, 0.25)
+    y_cal = max(y_cal, floor_val)
+    return y_cal, info, y_raw
+
+# ====== UI ======
+with gr.Blocks(title="Прогноз пассажиров (v6.2)") as demo:
+    gr.Markdown("### Прогноз числа пассажиров (учтены сезонность/праздники)")
+    with gr.Row():
+        gr.Textbox(label="Маршрут", value=ROUTE.replace('_', '→'), interactive=False)
+        dd_airline = gr.Dropdown(
+            choices=(WHITELIST or AIRLINES or ["American airlines","United"]),
+            value=(WHITELIST[0] if WHITELIST else (AIRLINES[0] if AIRLINES else "American airlines")),
+            label="Авиакомпания",
+            allow_custom_value=False
+        )
+        tb_date = gr.Textbox(label="Дата (YYYY-MM-DD)", value=str(pd.Timestamp.today().date()))
+    sum_all = gr.Checkbox(label="Суммарно по всем авиакомпаниям", value=True)
+
+    def predict_ui(airline, date_str, sum_all):
+        # простая валидация даты
         try:
-            arr = getattr(preprocess, attr)
-            if arr is not None and len(arr) > 0:
-                FEATURES = list(arr)
-                break
+            _ = pd.to_datetime(date_str)
         except Exception:
-            pass
-if FEATURES is None:
-    FEATURES = meta.get("features", []) or meta.get("expected_features", [])
-if not FEATURES:
-    # fallback, чтобы интерфейс хоть как-то поднялся
-    FEATURES = [f"feat{i+1}" for i in range(14)]
-
-# Имя/описание базовой фичи для второго входа
-BASELINE_NAME = meta.get("baseline_name", "baseline_in")
-BASELINE_HINT = meta.get("baseline_hint", "Базовое значение (скаляp)")
-
-def build_features(inputs_dict):
-    X = pd.DataFrame([{k: inputs_dict[k] for k in FEATURES}])
-    Xp = preprocess.transform(X)
-    return Xp
-
-def predict_fn(baseline_value, **kwargs):
-    # kwargs содержит остальные признаки
-    Xp = build_features(kwargs)                       # (1, n_features) -> препроцессор
-    b  = np.array([[float(baseline_value)]], dtype=np.float32)  # (1,1)
-    # Модель ожидает 2 входа: [x_in_preprocessed, baseline_in]
-    y = model.predict([Xp, b], verbose=0)
-    if y.ndim == 2 and y.shape[1] == 1:
-        y = y.ravel()
-    return f"Прогноз: {float(y[0]):,.4f}"
-
-# === UI ===
-with gr.Blocks(title="Нейронка v6") as demo:
-    gr.Markdown("# Нейронка v6\nЗаполни параметры и базовое значение — получи прогноз.")
-
-    # сначала baseline для второго входа
-    baseline_input = gr.Number(label=BASELINE_NAME, info=BASELINE_HINT)
-
-    # затем признаки для препроцессора/первого входа
-    feat_inputs = [gr.Number(label=f) for f in FEATURES]
-
-    btn = gr.Button("Предсказать")
-    out = gr.Textbox(label="Результат", lines=2)
-
-    def _wrap(b, *vals):
-        data = {FEATURES[i]: vals[i] for i in range(len(FEATURES))}
-        return predict_fn(baseline_value=b, **data)
-
-    btn.click(_wrap, inputs=[baseline_input, *feat_inputs], outputs=out)
+            return 0, "Некорректная дата, формат YYYY-MM-DD."
+
+        if sum_all:
+            total_raw, lines = 0.0, []
+            for a in (WHITELIST or AIRLINES or [airline]):
+                X, B, info = make_inputs(a, date_str)
+                y_raw = float(model.predict([X, B], verbose=0)[0,0])
+                total_raw += y_raw
+                lines.append(f"- {a}: base×M={info['baseline']:.1f} (M={info['exog']:.2f}) → raw {y_raw:.1f}")
+            total = total_raw * K_ROUTE
+            msg = (
+                f"Маршрут: **{ORIGIN}→{DEST}**, дата: **{date_str}**  \n"
+                f"Суммарный прогноз: **{round(total)}** "
+                f"(raw_sum={total_raw:.1f}, k_route={K_ROUTE:.2f}; целевой летний≈{int(CAP_TARGET)})\n"
+                + "\n".join(lines)
+            )
+            return round(total), msg
+
+        y, info, y_raw = predict_one(airline, date_str)
+        warn = ""
+        if SHARE and SHARE.get(airline, 0.0) < 0.05:
+            warn = (
+                "\n\n_Примечание: доля этой авиакомпании на маршруте <5%, "
+                "прогноз может быть занижен; попробуйте «Суммарно по всем»._"
+            )
+        msg = (
+            f"Маршрут: **{ORIGIN}→{DEST}**, авиакомпания: **{airline}**, дата: **{date_str}**  \n"
+            f"M(date)={info['exog']:.2f}; baseline×M={info['baseline']:.1f}  \n"
+            f"Прогноз: **{round(y)}** (raw={y_raw:.1f}, k_airline={K_AIRLINE:.2f}){warn}"
+        )
+        return round(y), msg
+
+    btn = gr.Button("Рассчитать")
+    out_pred = gr.Number(label="Прогноз пассажиров", interactive=False)
+    out_expl = gr.Markdown()
+
+    btn.click(predict_ui, [dd_airline, tb_date, sum_all], [out_pred, out_expl])
+
+    # очередь без спорных параметров (кросс-версийно)
     try:
-        demo.queue(max_size=32)   # gradio>=4
+        demo.queue(max_size=32)
     except TypeError:
         demo.queue()
 
+# HF Spaces сам запускает; локально — так:
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)
+