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README.md

@@ -1,12 +1,6 @@
----
-title: Cheese Texture App
-emoji: 📊
-colorFrom: purple
-colorTo: gray
-sdk: gradio
-sdk_version: 5.47.2
-app_file: app.py
-pinned: false
----
+# Cheese Texture Gradio App
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+This Space loads **rlogh/cheese-texture-autogluon-classifier** (AutoGluon Tabular) and offers a simple UI.
+
+**Inputs:** fat, protein, price, origin (dropdown or custom), holed (yes/no)  
+**Output:** predicted texture (soft / semi-soft / semi-hard / hard) and class probabilities.

app.py

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+
+import gradio as gr, pandas as pd
+from huggingface_hub import list_repo_files, hf_hub_download
+import zipfile, cloudpickle, shutil
+from pathlib import Path
+import autogluon.tabular as ag
+
+MODEL_REPO_ID = "rlogh/cheese-texture-autogluon-classifier"
+ZIP_CANDIDATES = ['cheese_texture_predictor_dir.zip', 'autogluon_predictor_dir.zip', 'predictor_dir.zip']
+PKL_CANDIDATES = ['cheese_texture_predictor.pkl', 'autogluon_predictor.pkl', 'predictor.pkl']
+CACHE_DIR = Path("hf_assets"); CACHE_DIR.mkdir(parents=True, exist_ok=True)
+EXTRACT_DIR = CACHE_DIR / "predictor_native"
+
+def _pick_first(files, cands):
+    for n in cands:
+        if n in files: return n
+    return None
+
+def load_predictor(repo_id):
+    files = list_repo_files(repo_id, repo_type="model")
+    zn = _pick_first(files, ZIP_CANDIDATES)
+    pn = _pick_first(files, PKL_CANDIDATES)
+    if zn:
+        zp = hf_hub_download(repo_id, filename=zn, 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(zp, "r") as zf: zf.extractall(str(EXTRACT_DIR))
+        kids = list(EXTRACT_DIR.iterdir())
+        root = kids[0] if (len(kids)==1 and kids[0].is_dir()) else EXTRACT_DIR
+        return ag.TabularPredictor.load(str(root), require_py_version_match=False)
+    elif pn:
+        pp = hf_hub_download(repo_id, filename=pn, repo_type="model", local_dir=str(CACHE_DIR), local_dir_use_symlinks=False)
+        with open(pp, "rb") as f: return cloudpickle.load(f)
+    raise FileNotFoundError("No predictor artifact found.")
+
+PREDICTOR = load_predictor(MODEL_REPO_ID)
+FEATURES = ["fat","origin","holed","price","protein"]
+HOLED_MAP = {"Yes (has holes)":1,"No (solid)":0}
+COMMON_ORIGINS = ["Italy","France","USA","Switzerland","Netherlands","Spain","Greece","Mexico","Denmark","Norway","India","Cyprus"]
+
+def predict(fat, origin_pick, origin_free, holed_label, price, protein, k):
+    origin = origin_free.strip() if origin_free and origin_free.strip() else origin_pick
+    row = {"fat":float(fat), "origin":origin, "holed":HOLED_MAP[holed_label], "price":float(price), "protein":float(protein)}
+    X = pd.DataFrame([row], columns=FEATURES)
+    pred = PREDICTOR.predict(X).iloc[0]
+    try:
+        proba = PREDICTOR.predict_proba(X)
+        if hasattr(proba, "to_frame"): proba = proba.to_frame().T if proba.ndim==1 else proba
+        row0 = proba.iloc[0].to_dict()
+        probs = {str(k): float(v) for k,v in row0.items()}
+        probs = dict(sorted(probs.items(), key=lambda kv: kv[1], reverse=True)[:int(k)])
+    except Exception:
+        probs = {}
+    return f"**Prediction:** {pred}", probs
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Cheese Texture Classifier")
+    fat     = gr.Slider(0, 60, value=25.0, step=0.1, label="Fat (g/100g)")
+    protein = gr.Slider(0, 40, value=22.0, step=0.1, label="Protein (g/100g)")
+    price   = gr.Slider(0, 10, value=2.50, step=0.01, label="Price")
+    holed   = gr.Radio(list(HOLED_MAP.keys()), value="No (solid)", label="Has holes?")
+    origin_pick = gr.Dropdown(COMMON_ORIGINS, value="Italy", label="Origin (common)")
+    origin_free = gr.Textbox(value="", placeholder="Or type a custom origin", label="Custom origin (optional)")
+    topk = gr.Slider(1, 4, value=4, step=1, label="Top-K")
+
+    summary = gr.Markdown()
+    probs   = gr.Label(num_top_classes=4, label="Class probabilities")
+
+    gr.Button("Predict").click(predict,
+        [fat, origin_pick, origin_free, holed, price, protein, topk],
+        [summary, probs])
+
+demo.launch()

requirements.txt

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+autogluon.tabular
+gradio
+huggingface_hub
+cloudpickle
+pandas