Upload app.py with huggingface_hub

app.py

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+
+# !pip install -q autogluon.tabular gradio huggingface_hub pandas
+
+from huggingface_hub import hf_hub_download
+from autogluon.tabular import TabularPredictor
+import pathlib, shutil, zipfile
+import pandas as pd
+import gradio as gr
+
+# ---------------- Settings ----------------
+MODEL_REPO_ID = "Iris314/classical-automl-model"
+ZIP_FILENAME  = "lego_predictor_dir.zip"
+
+# UI → model feature name mapping
+COLUMN_ALIAS = {
+    "Length": "Max Length (cm)",
+    "Height": "Max Height (cm)",
+    "Width":  "Width (cm)",
+    "Studs":  "Studs",
+}
+FEATURE_COLS_UI = ["Length", "Height", "Width", "Studs"]
+
+# ---------------- Load predictor ----------------
+CACHE_DIR = pathlib.Path("hf_cache"); EXTRACT_DIR = CACHE_DIR / "predictor"
+CACHE_DIR.mkdir(exist_ok=True, parents=True)
+
+def load_predictor():
+    local_zip = hf_hub_download(
+        repo_id=MODEL_REPO_ID,
+        filename=ZIP_FILENAME,
+        repo_type="model"
+    )
+    if EXTRACT_DIR.exists(): shutil.rmtree(EXTRACT_DIR)
+    EXTRACT_DIR.mkdir(parents=True)
+    with zipfile.ZipFile(local_zip, "r") as zf:
+        zf.extractall(EXTRACT_DIR)
+    kids = list(EXTRACT_DIR.iterdir())
+    path = kids[0] if len(kids) == 1 and kids[0].is_dir() else EXTRACT_DIR
+    return TabularPredictor.load(str(path), require_py_version_match=False)
+
+PREDICTOR = load_predictor()
+
+# ---------------- Helpers ----------------
+def _cast_and_rename(row_dict):
+    row = dict(row_dict)
+    row["Length"] = float(row["Length"])
+    row["Height"] = float(row["Height"])
+    row["Width"]  = float(row["Width"])
+    row["Studs"]  = int(round(float(row["Studs"])))  # gr.Number returns float
+    X_ui = pd.DataFrame([row], columns=FEATURE_COLS_UI)
+    X_model = X_ui.rename(columns=COLUMN_ALIAS)
+    return X_model
+
+def classify_brick(length, height, width, studs):
+    try:
+        X = _cast_and_rename({
+            "Length": length, "Height": height, "Width": width, "Studs": studs
+        })
+
+        pred = PREDICTOR.predict(X)
+        pred_val = pred.iloc[0] if hasattr(pred, "iloc") else pred
+
+        # Try probabilities; fall back to label
+        try:
+            proba = PREDICTOR.predict_proba(X)
+            s = proba.iloc[0] if hasattr(proba, "iloc") else proba
+            s = s.sort_values(ascending=False)
+            s.index = [str(k) for k in s.index]  # ensure JSON-serializable keys
+            return {k: float(v) for k, v in s.items()}
+        except Exception:
+            return {"prediction": str(pred_val)}
+    except Exception as e:
+        import traceback
+        return {"error": f"{type(e).__name__}: {e}", "traceback": traceback.format_exc()}
+
+# ---------------- Quick test (uses correct names) ----------------
+# test_X = _cast_and_rename({"Length": 4, "Height": 1.2, "Width": 2, "Studs": 4})
+# print("Prediction:", PREDICTOR.predict(test_X))
+# print("Probabilities:\n", PREDICTOR.predict_proba(test_X))
+
+# ---------------- Gradio (Interface version) ----------------
+demo = gr.Interface(
+    fn=classify_brick,
+    inputs=[
+        gr.Slider(1, 10, step=0.1, value=4,  label="Length"),
+        gr.Slider(0.2, 5, step=0.1, value=1.2, label="Height"),
+        gr.Slider(1, 10, step=0.1, value=2,  label="Width"),
+        gr.Number(value=4, precision=0, label="Studs"),
+    ],
+    outputs=gr.Label(num_top_classes=3, label="Predicted Class / Probabilities"),
+    examples=[[4, 1.2, 2, 4], [2, 0.6, 2, 2], [3, 2.0, 2, 2]],
+    title="🧱 LEGO Brick Classifier",
+    description="Predicts whether a LEGO piece is Standard, Flat, or Sloped."
+)
+
+demo.launch()