Upload app.py.py

app.py.py

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+# -*- coding: utf-8 -*-
+"""tabular_gradio.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1-_lDVeqDrMkiSuBA380Q7IC2WYa0NHcF
+"""
+
+!pip install autogluon.tabular --quiet
+
+import os  # For filesystem operations
+import shutil  # For directory cleanup
+import zipfile  # For extracting model archives
+import pathlib  # For path manipulations
+import pandas as pd # For tabular data handling
+import gradio as gr  # For interactive UI
+import huggingface_hub  # For downloading model assets
+import autogluon.tabular  # For loading and running AutoGluon predictors
+
+# Settings
+MODEL_REPO_ID = "mrob937/2024-24679-tabular-autogluon-predictor"
+ZIP_FILENAME  = "autogluon_predictor_dir.zip"
+CACHE_DIR = pathlib.Path("hf_assets")
+EXTRACT_DIR = CACHE_DIR / "predictor_native"
+
+FEATURE_COLS = [
+    "Tgt",
+    "Rec",
+    "Yds",
+    "YBC/R",
+    "YAC/R",
+    "ADOT",
+    "Drop%",
+    "Rat"
+]
+TARGET_COL = "Elite"
+
+def _prepare_predictor_dir() -> str:
+    CACHE_DIR.mkdir(parents=True, exist_ok=True)
+    local_zip = huggingface_hub.hf_hub_download(
+        repo_id=MODEL_REPO_ID,
+        filename=ZIP_FILENAME,
+        repo_type="model",
+        local_dir=str(CACHE_DIR),
+        local_dir_use_symlinks=False,
+    )
+    if EXTRACT_DIR.exists():
+        shutil.rmtree(EXTRACT_DIR)
+    EXTRACT_DIR.mkdir(parents=True, exist_ok=True)
+    with zipfile.ZipFile(local_zip, "r") as zf:
+        zf.extractall(str(EXTRACT_DIR))
+    contents = list(EXTRACT_DIR.iterdir())
+    predictor_root = contents[0] if (len(contents) == 1 and contents[0].is_dir()) else EXTRACT_DIR
+    return str(predictor_root)
+
+PREDICTOR_DIR = _prepare_predictor_dir()
+PREDICTOR = autogluon.tabular.TabularPredictor.load(PREDICTOR_DIR, require_py_version_match=False)
+
+OUTCOME_MAP = {
+    0: "Not Elite",
+    1: "Elite",
+}
+
+def _human_label(c):
+    try:
+        ci = int(c)
+        if ci in OUTCOME_MAP:
+            return OUTCOME_MAP[ci]
+    except Exception:
+        pass
+    return str(c)
+
+def do_predict(player, tgt, rec, yds, ybc_r, yac_r, adot, drop_pct, rat):
+    row = {
+        "Player": str(player),
+        FEATURE_COLS[0]: float(tgt),
+        FEATURE_COLS[1]: float(rec),
+        FEATURE_COLS[2]: float(yds),
+        FEATURE_COLS[3]: float(ybc_r),
+        FEATURE_COLS[4]: float(yac_r),
+        FEATURE_COLS[5]: float(adot),
+        FEATURE_COLS[6]: float(drop_pct),
+        FEATURE_COLS[7]: float(rat),
+    }
+    X = pd.DataFrame([row], columns=["Player"] + FEATURE_COLS)
+
+    pred_series = PREDICTOR.predict(X)
+    raw_pred = pred_series.iloc[0]
+
+    try:
+        proba = PREDICTOR.predict_proba(X)
+        if isinstance(proba, pd.Series):
+            proba = proba.to_frame().T
+    except Exception:
+        proba = None
+
+    pred_label = _human_label(raw_pred)
+
+    proba_dict = None
+    if proba is not None:
+        row0 = proba.iloc[0]
+        tmp = {}
+        for cls, val in row0.items():
+            key = _human_label(cls)
+            tmp[key] = float(val) + float(tmp.get(key, 0.0))
+        proba_dict = dict(sorted(tmp.items(), key=lambda kv: kv[1], reverse=True))
+
+    # integrate player name into output dict
+    if proba_dict:
+        return {f"{player} → {k}": v for k, v in proba_dict.items()}
+    else:
+        return {f"{player} → {pred_label}": 1.0}
+
+# -------- Example rows (with player names) --------
+EXAMPLES = [
+    ["Justin Jefferson", 8.0, 5.0, 65.0, 6.0, 4.0, 8.5, 1.5, 7.2],
+    ["Cooper Kupp", 15.0, 10.0, 140.0, 8.0, 6.0, 9.0, 0.5, 8.5],
+    ["Rookie WR", 3.0, 2.0, 12.0, 4.0, 3.0, 5.0, 4.0, 5.5],
+    ["Tyreek Hill", 20.0, 14.0, 220.0, 10.0, 8.0, 12.0, 0.2, 9.0],
+    ["Bench Player", 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 10.0, 3.0],
+]
+
+# -------- Gradio UI --------
+with gr.Blocks() as demo:
+    gr.Markdown("# Football: Will this player be Elite?")
+    gr.Markdown("Enter the player's name and stats below. The model will predict whether they're Elite and show probabilities.")
+
+    player_name = gr.Textbox(value="Example Player", label="Player Name")
+
+    with gr.Row():
+        tgt = gr.Slider(0, 30, value=8.0, step=0.5, label=FEATURE_COLS[0])
+        rec = gr.Slider(0, 20, value=5.0, step=0.5, label=FEATURE_COLS[1])
+
+    with gr.Row():
+        yds = gr.Number(value=60.0, precision=1, label=FEATURE_COLS[2])
+        ybc_r = gr.Number(value=6.0, precision=2, label=FEATURE_COLS[3])
+
+    with gr.Row():
+        yac_r = gr.Number(value=4.0, precision=2, label=FEATURE_COLS[4])
+        adot = gr.Number(value=8.0, precision=2, label=FEATURE_COLS[5])
+
+    with gr.Row():
+        drop_pct = gr.Slider(0.0, 100.0, value=1.5, step=0.1, label=FEATURE_COLS[6])
+        rat = gr.Slider(0.0, 10.0, value=7.2, step=0.1, label=FEATURE_COLS[7])
+
+    proba_pretty = gr.Label(num_top_classes=5, label="Class probabilities")
+
+    inputs = [player_name, tgt, rec, yds, ybc_r, yac_r, adot, drop_pct, rat]
+    for comp in inputs:
+        comp.change(fn=do_predict, inputs=inputs, outputs=[proba_pretty])
+
+    gr.Examples(
+        examples=EXAMPLES,
+        inputs=inputs,
+        label="Representative examples",
+        examples_per_page=5,
+        cache_examples=False,
+    )
+
+if __name__ == "__main__":
+    demo.launch()