Create app.py

app.py

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+import os  # For filesystem operations
+import shutil  # For directory cleanup
+import zipfile  # For extracting model archives
+import pathlib  # For path manipulations
+import pandas  # For tabular data handling
+import gradio  # For interactive UI
+import huggingface_hub  # For downloading model assets
+import autogluon.tabular  # For loading and running AutoGluon predictors
+
+# Settings
+MODEL_REPO_ID = "its-zion-18/flowers-tabular-autolguon-predictor"
+ZIP_FILENAME  = "autogluon_predictor_dir.zip"
+CACHE_DIR = pathlib.Path("hf_assets")
+EXTRACT_DIR = CACHE_DIR / "predictor_native"
+
+FEATURE_COLS = [
+    "flower_diameter_cm",
+    "petal_length_cm",
+    "petal_width_cm",
+    "petal_count",
+    "stem_height_cm",
+]
+TARGET_COL = "color"
+
+# If your repo id has a typo in "autolguon", fix it here if download fails.
+# Download & load the native predictor
+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)
+
+def do_predict(flower_diameter_cm, petal_length_cm, petal_width_cm, petal_count, stem_height_cm, top_k):
+    row = {
+        FEATURE_COLS[0]: float(flower_diameter_cm),
+        FEATURE_COLS[1]: float(petal_length_cm),
+        FEATURE_COLS[2]: float(petal_width_cm),
+        FEATURE_COLS[3]: int(petal_count),
+        FEATURE_COLS[4]: float(stem_height_cm),
+    }
+    X = pandas.DataFrame([row], columns=FEATURE_COLS)
+
+    # Predicted label (string)
+    pred_series = PREDICTOR.predict(X)
+    pred_label = str(pred_series.iloc[0])
+
+    # Probabilities: dict[class] -> float
+    proba_dict = None
+    try:
+        proba = PREDICTOR.predict_proba(X)
+        # AutoGluon can return Series for binary; normalize to DataFrame row
+        if isinstance(proba, pandas.Series):
+            proba = proba.to_frame().T
+        row0 = proba.iloc[0].sort_values(ascending=False)
+        if isinstance(top_k, (int, float)) and top_k > 0:
+            row0 = row0.head(int(top_k))
+        proba_dict = {str(cls): float(val) for cls, val in row0.items()}
+    except Exception:
+        # If proba not available, just put 1.0 on the predicted class
+        proba_dict = {pred_label: 1.0}
+
+    # Return TWO outputs: (predicted color string, dict of numeric probs)
+    return proba_dict
+
+# ----------------
+# Example records
+# ----------------
+EXAMPLES = [
+    [4.5, 5.2, 1.8, 5, 35.0],  # diam, petal_len, petal_wid, count, stem_h
+    [2.1, 3.3, 0.9, 8, 22.0],
+    [6.8, 7.1, 2.5, 6, 55.0],
+    [9.0, 4.0, 1.2, 12, 80.0],
+    [1.8, 2.6, 0.5, 4, 15.0],
+]
+
+with gradio.Blocks() as demo:
+    gradio.Markdown("# 🌼 Flower Color Classifier\nPredict the flower **color** from five measurements.")
+    gradio.Markdown(
+        "Enter a single flower’s measurements below. "
+        "Use **Top-K** to see the most likely colors with their probabilities."
+    )
+
+    with gradio.Row():
+        flower_diameter_cm = gradio.Slider(0.0, 20.0, step=0.1, value=4.5, label="flower_diameter_cm")
+        petal_length_cm    = gradio.Slider(0.0, 15.0, step=0.1, value=5.2, label="petal_length_cm")
+        petal_width_cm     = gradio.Slider(0.0, 10.0, step=0.1, value=1.8, label="petal_width_cm")
+
+    with gradio.Row():
+        petal_count        = gradio.Slider(1, 100, step=1, value=5, label="petal_count")
+        stem_height_cm     = gradio.Slider(0.0, 200.0, step=0.5, value=35.0, label="stem_height_cm")
+        top_k              = gradio.Slider(1, 10, step=1, value=3, label="Top-K classes shown")
+
+    # Separate outputs: Textbox for label, Label for probs (dict must be numeric)
+    proba_pretty = gradio.Label(num_top_classes=10, label="Class probabilities")
+
+    inputs = [flower_diameter_cm, petal_length_cm, petal_width_cm, petal_count, stem_height_cm, top_k]
+
+    # Trigger on any change
+    for comp in inputs:
+        comp.change(fn=do_predict, inputs=inputs, outputs=[proba_pretty])
+
+    # Examples: only pass the first 5 inputs (excluding top_k) to match example rows
+    gradio.Examples(
+        examples=EXAMPLES,
+        inputs=inputs[:-1],  # exclude top_k so example length matches
+        label="Representative examples",
+        examples_per_page=5,
+        cache_examples=False,
+    )
+
+if __name__ == "__main__":
+    demo.launch()