Deploy from Colab

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+neiro1/model_v6.keras filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,12 +1 @@
----
-title: Neiro1
-emoji: 💻
-colorFrom: blue
-colorTo: pink
-sdk: gradio
-sdk_version: 5.49.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Нейронка v6 на Gradio (HF Spaces)

app.py

@@ -0,0 +1,71 @@
+
+import json, os
+import numpy as np
+import pandas as pd
+import gradio as gr
+import joblib
+import tensorflow as tf
+
+MODEL_PATH = "neiro1/model_v6.keras"
+PP_PATH    = "neiro1/preprocess_v6.joblib"
+META_PATH  = "neiro1/meta_v6.json"
+
+model = tf.keras.models.load_model(MODEL_PATH)
+preprocess = joblib.load(PP_PATH)
+
+with open(META_PATH, "r", encoding="utf-8") as f:
+    meta = json.load(f)
+
+# 1) пытаемся взять список фич из препроцессора (sklearn>=1.0)
+FEATURES = None
+for attr in ["feature_names_in_", "features_in_", "input_features_"]:
+    if hasattr(preprocess, attr):
+        try:
+            arr = getattr(preprocess, attr)
+            if arr is not None and len(arr) > 0:
+                FEATURES = list(arr)
+                break
+        except Exception:
+            pass
+
+# 2) если нет — из meta.json
+if FEATURES is None:
+    FEATURES = meta.get("features", [])
+
+# 3) если и тут пусто — можно руками прописать:
+if not FEATURES:
+    FEATURES = meta.get("expected_features", []) or ["feat1","feat2","feat3"]
+
+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(**kwargs):
+    Xp = build_features(kwargs)
+    y = model.predict(Xp, verbose=0)
+    if y.ndim == 2 and y.shape[1] == 1:
+        y = y.ravel()
+    pred = float(y[0])
+    return f"Прогноз: {pred:,.4f}"
+
+with gr.Blocks(title="Нейронка v6") as demo:
+    gr.Markdown("# Нейронка v6\nЗаполни параметры → жми Предсказать.")
+
+    inputs = []
+    for f in FEATURES:
+        # при желании замени на Slider/Dropdown для категорий
+        inputs.append(gr.Number(label=f))
+
+    out = gr.Textbox(label="Результат", lines=2)
+    btn = gr.Button("Предсказать")
+
+    def _wrap(*vals):
+        data = {FEATURES[i]: vals[i] for i in range(len(FEATURES))}
+        return predict_fn(**data)
+
+    btn.click(_wrap, inputs=inputs, outputs=out)
+    demo.queue(concurrency_count=4, max_size=32)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

neiro1/meta_v6.json

@@ -0,0 +1,145 @@
+{
+  "route": "SFO_MIA",
+  "airlines": [
+    "Alaska",
+    "American airlines",
+    "Delta",
+    "Jetblue",
+    "Southwest",
+    "Spirit",
+    "United"
+  ],
+  "global_mean": 56.50381679389313,
+  "blend_month": {
+    "7": 31.946564885496187,
+    "8": 54.81622844218264,
+    "9": 52.90095419847328,
+    "10": 57.78141873021783,
+    "11": 60.75095419847328,
+    "12": 72.88152671755725
+  },
+  "blend_airline": {
+    "Alaska": 54.12659033078881,
+    "American airlines": 57.262529040823104,
+    "Delta": 50.641526717557255,
+    "Jetblue": 56.56152671755726,
+    "Southwest": 55.407442748091604,
+    "Spirit": 57.372519083969465,
+    "United": 61.62265318753868
+  },
+  "am_means": {
+    "Alaska_7": 1.0,
+    "Alaska_8": 48.333333333333336,
+    "Alaska_9": 50.0,
+    "Alaska_10": 56.25,
+    "Alaska_11": 63.5,
+    "Alaska_12": 86.0,
+    "American airlines_7": 2.0,
+    "American airlines_8": 74.0,
+    "American airlines_9": 44.75,
+    "American airlines_10": 47.0,
+    "American airlines_11": 65.33333333333333,
+    "American airlines_12": 90.0,
+    "Delta_7": 1.25,
+    "Delta_9": 62.333333333333336,
+    "Delta_10": 57.75,
+    "Delta_11": 69.5,
+    "Jetblue_7": 1.0,
+    "Jetblue_8": 80.0,
+    "Jetblue_9": 53.25,
+    "Jetblue_10": 62.75,
+    "Jetblue_11": 60.8,
+    "Southwest_7": 1.0,
+    "Southwest_8": 57.25,
+    "Southwest_9": 52.6,
+    "Southwest_10": 58.0,
+    "Southwest_11": 52.0,
+    "Southwest_12": 79.5,
+    "Spirit_8": 42.75,
+    "Spirit_9": 50.833333333333336,
+    "Spirit_10": 61.333333333333336,
+    "Spirit_11": 63.42857142857143,
+    "Spirit_12": 77.5,
+    "United_8": 47.333333333333336,
+    "United_9": 50.8,
+    "United_10": 55.666666666666664,
+    "United_11": 61.6,
+    "United_12": 84.625
+  },
+  "am_counts": {
+    "Alaska_7": 1,
+    "Alaska_8": 3,
+    "Alaska_9": 3,
+    "Alaska_10": 4,
+    "Alaska_11": 2,
+    "Alaska_12": 1,
+    "American airlines_7": 1,
+    "American airlines_8": 2,
+    "American airlines_9": 4,
+    "American airlines_10": 1,
+    "American airlines_11": 3,
+    "American airlines_12": 2,
+    "Delta_7": 4,
+    "Delta_9": 3,
+    "Delta_10": 4,
+    "Delta_11": 4,
+    "Jetblue_7": 1,
+    "Jetblue_8": 1,
+    "Jetblue_9": 4,
+    "Jetblue_10": 4,
+    "Jetblue_11": 5,
+    "Southwest_7": 1,
+    "Southwest_8": 4,
+    "Southwest_9": 5,
+    "Southwest_10": 6,
+    "Southwest_11": 4,
+    "Southwest_12": 2,
+    "Spirit_8": 4,
+    "Spirit_9": 6,
+    "Spirit_10": 6,
+    "Spirit_11": 7,
+    "Spirit_12": 2,
+    "United_8": 3,
+    "United_9": 5,
+    "United_10": 6,
+    "United_11": 5,
+    "United_12": 8
+  },
+  "w_month": 10,
+  "w_air": 10,
+  "w_am": 25,
+  "floor_frac": 0.25,
+  "exog_params": {
+    "SUMMER_MULT": 1.6,
+    "FRI_MULT": 1.1,
+    "SAT_MULT": 1.15,
+    "SUN_MULT": 1.1,
+    "SPRING_BREAK_MULT": 1.12,
+    "MEMORIAL_MULT": 1.35,
+    "JULY4_MULT": 1.3,
+    "LABOR_MULT": 1.25,
+    "THANKSGIVING_MULT": 1.6,
+    "XMAS_NEWYEAR_MULT": 1.7
+  },
+  "calibration_k_airline": 1.0354253783141563,
+  "calibration_k_route": 5.177126891570781,
+  "airline_share_overall": {
+    "United": 0.23169413671980546,
+    "Spirit": 0.1949473115374223,
+    "Southwest": 0.16319913536881925,
+    "Jetblue": 0.11469873007295325,
+    "American airlines": 0.10159416373952986,
+    "Alaska": 0.09916238854363686,
+    "Delta": 0.09470413401783302
+  },
+  "airline_whitelist": [
+    "United",
+    "Spirit",
+    "Southwest",
+    "Jetblue",
+    "American airlines",
+    "Alaska",
+    "Delta"
+  ],
+  "capacity_target_summer": 1000.0
+}

neiro1/model_v6.keras

@@ -0,0 +1,3 @@
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+size 369255

neiro1/preprocess_v6.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3642891c51a1f85f3d37ebb0ac4cee67d58112d82f6e16ec4e8f1f33fb25dc7f
+size 2947

requirements.txt

@@ -0,0 +1,6 @@
+gradio>=4.44.0
+tensorflow==2.15.0
+scikit-learn
+joblib
+pandas
+numpy