Upload folder using huggingface_hub

.config/.last_opt_in_prompt.yaml

@@ -0,0 +1 @@
+{}

.config/.last_survey_prompt.yaml

@@ -0,0 +1 @@
+last_prompt_time: 1760622052.5923831

.config/.last_update_check.json

@@ -0,0 +1 @@
+{"last_update_check_time": 1760622059.1239753, "last_update_check_revision": 20251010143653, "notifications": [], "last_nag_times": {}}

.config/active_config

@@ -0,0 +1 @@
+default

.config/configurations/config_default

@@ -0,0 +1,6 @@
+[component_manager]
+disable_update_check = true
+
+[compute]
+gce_metadata_read_timeout_sec = 0
+

.config/gce

@@ -0,0 +1 @@
+False

.gitattributes

@@ -33,3 +33,5 @@ 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
+sample_data/mnist_test.csv filter=lfs diff=lfs merge=lfs -text
+sample_data/mnist_train_small.csv filter=lfs diff=lfs merge=lfs -text

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
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+emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
+-----END CERTIFICATE-----

app.py

@@ -1,11 +1,15 @@
-
-import pathlib, shutil, zipfile
+import pathlib, shutil, zipfile, os, traceback
 import pandas as pd
 import gradio as gr
+
 from huggingface_hub import hf_hub_download
 from autogluon.tabular import TabularPredictor
 
-# ====== Settings (edit if your repo/zip name changes) ======
+# ---------------- UI copy ----------------
+TITLE = "🧱 LEGO Brick Classifier"
+DESC  = "Predicts whether a LEGO piece is Standard, Flat, or Sloped from basic dimensions."
+
+# ---------------- Settings ----------------
 MODEL_REPO_ID = "Iris314/classical-automl-model"
 ZIP_FILENAME  = "lego_predictor_dir.zip"
 
@@ -18,7 +22,7 @@ COLUMN_ALIAS = {
 }
 FEATURE_COLS_UI = ["Length", "Height", "Width", "Studs"]
 
-# ====== Load predictor from the model repo ======
+# ---------------- Load predictor ----------------
 CACHE_DIR = pathlib.Path("hf_cache"); EXTRACT_DIR = CACHE_DIR / "predictor"
 CACHE_DIR.mkdir(exist_ok=True, parents=True)
 
@@ -32,20 +36,26 @@ def load_predictor():
     )
     if EXTRACT_DIR.exists():
         shutil.rmtree(EXTRACT_DIR)
-    EXTRACT_DIR.mkdir(parents=True, exist_ok=True)
+    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()
+try:
+    PREDICTOR = load_predictor()
+except Exception as e:
+    PREDICTOR = None
+    print("Failed to load predictor:", e)
 
+# ---------------- 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"])
+    # gr.Number returns float; round & cast for integer feature
     row["Studs"]  = int(round(float(row["Studs"])))
     X_ui = pd.DataFrame([row], columns=FEATURE_COLS_UI)
     X_model = X_ui.rename(columns=COLUMN_ALIAS)
@@ -53,40 +63,45 @@ def _cast_and_rename(row_dict):
 
 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
+        if PREDICTOR is None:
+            raise RuntimeError("Model failed to load on startup. Check model artifact path & runtime deps.")
+
+        X = _cast_and_rename({
+            "Length": length, "Height": height, "Width": width, "Studs": studs
+        })
+
+        # 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]
+            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:
+            pred = PREDICTOR.predict(X)
+            pred_val = pred.iloc[0] if hasattr(pred, "iloc") else pred
             return {"prediction": str(pred_val)}
     except Exception as e:
-        import traceback
-        return {"error": f"{type(e).__name__}: {e}", "traceback": traceback.format_exc()}
+        return {
+            "error": f"{type(e).__name__}: {e}",
+            "traceback": traceback.format_exc(limit=1)
+        }
 
-with gr.Blocks() as demo:
-    gr.Markdown("# 🧱 LEGO Brick Classifier")
-    gr.Markdown("Enter dimensions to predict **Standard / Flat / Sloped**. Uses a classmate's AutoGluon model trained on `aedupuga/lego-sizes`.")
-    with gr.Row():
-        length = gr.Slider(1, 10, step=0.1, value=4,  label="Length")
-        height = gr.Slider(0.2, 5, step=0.1, value=1.2, label="Height")
-    with gr.Row():
-        width  = gr.Slider(1, 10, step=0.1, value=2,  label="Width")
-        studs  = gr.Number(value=4, precision=0, label="Studs")
-    out = gr.Label(num_top_classes=3, label="Predicted Class / Probabilities")
-    gr.Button("Classify Brick", variant="primary").click(
-        fn=classify_brick, inputs=[length, height, width, studs], outputs=out
-    )
-    gr.Examples(
-        examples=[[4, 1.2, 2, 4],[2, 0.6, 2, 2],[3, 2.0, 2, 2]],
-        inputs=[length, height, width, studs],
-        label="Examples",
-        cache_examples=False
-    )
+# ---------------- Gradio ----------------
+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=TITLE,
+    description=DESC
+)
 
 if __name__ == "__main__":
+    # In Spaces, no share=True needed
     demo.launch()

sample_data/README.md

@@ -0,0 +1,19 @@
+This directory includes a few sample datasets to get you started.
+
+*   `california_housing_data*.csv` is California housing data from the 1990 US
+    Census; more information is available at:
+    https://docs.google.com/document/d/e/2PACX-1vRhYtsvc5eOR2FWNCwaBiKL6suIOrxJig8LcSBbmCbyYsayia_DvPOOBlXZ4CAlQ5nlDD8kTaIDRwrN/pub
+
+*   `mnist_*.csv` is a small sample of the
+    [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is
+    described at: http://yann.lecun.com/exdb/mnist/
+
+*   `anscombe.json` contains a copy of
+    [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
+    was originally described in
+
+    Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
+    Statistician. 27 (1): 17-21. JSTOR 2682899.
+
+    and our copy was prepared by the
+    [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).

sample_data/anscombe.json

@@ -0,0 +1,49 @@
+[
+  {"Series":"I", "X":10.0, "Y":8.04},
+  {"Series":"I", "X":8.0, "Y":6.95},
+  {"Series":"I", "X":13.0, "Y":7.58},
+  {"Series":"I", "X":9.0, "Y":8.81},
+  {"Series":"I", "X":11.0, "Y":8.33},
+  {"Series":"I", "X":14.0, "Y":9.96},
+  {"Series":"I", "X":6.0, "Y":7.24},
+  {"Series":"I", "X":4.0, "Y":4.26},
+  {"Series":"I", "X":12.0, "Y":10.84},
+  {"Series":"I", "X":7.0, "Y":4.81},
+  {"Series":"I", "X":5.0, "Y":5.68},
+
+  {"Series":"II", "X":10.0, "Y":9.14},
+  {"Series":"II", "X":8.0, "Y":8.14},
+  {"Series":"II", "X":13.0, "Y":8.74},
+  {"Series":"II", "X":9.0, "Y":8.77},
+  {"Series":"II", "X":11.0, "Y":9.26},
+  {"Series":"II", "X":14.0, "Y":8.10},
+  {"Series":"II", "X":6.0, "Y":6.13},
+  {"Series":"II", "X":4.0, "Y":3.10},
+  {"Series":"II", "X":12.0, "Y":9.13},
+  {"Series":"II", "X":7.0, "Y":7.26},
+  {"Series":"II", "X":5.0, "Y":4.74},
+
+  {"Series":"III", "X":10.0, "Y":7.46},
+  {"Series":"III", "X":8.0, "Y":6.77},
+  {"Series":"III", "X":13.0, "Y":12.74},
+  {"Series":"III", "X":9.0, "Y":7.11},
+  {"Series":"III", "X":11.0, "Y":7.81},
+  {"Series":"III", "X":14.0, "Y":8.84},
+  {"Series":"III", "X":6.0, "Y":6.08},
+  {"Series":"III", "X":4.0, "Y":5.39},
+  {"Series":"III", "X":12.0, "Y":8.15},
+  {"Series":"III", "X":7.0, "Y":6.42},
+  {"Series":"III", "X":5.0, "Y":5.73},
+
+  {"Series":"IV", "X":8.0, "Y":6.58},
+  {"Series":"IV", "X":8.0, "Y":5.76},
+  {"Series":"IV", "X":8.0, "Y":7.71},
+  {"Series":"IV", "X":8.0, "Y":8.84},
+  {"Series":"IV", "X":8.0, "Y":8.47},
+  {"Series":"IV", "X":8.0, "Y":7.04},
+  {"Series":"IV", "X":8.0, "Y":5.25},
+  {"Series":"IV", "X":19.0, "Y":12.50},
+  {"Series":"IV", "X":8.0, "Y":5.56},
+  {"Series":"IV", "X":8.0, "Y":7.91},
+  {"Series":"IV", "X":8.0, "Y":6.89}
+]

sample_data/mnist_test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:51c292478d94ec3a01461bdfa82eb0885d262eb09e615679b2d69dedb6ad09e7
+size 18289443

sample_data/mnist_train_small.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ef64781aa03180f4f5ce504314f058f5d0227277df86060473d973cf43b033e
+size 36523880