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1454b2f fb8ace6 1454b2f fb8ace6 1454b2f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | # app.py
import os
import pandas as pd
import numpy as np
from huggingface_hub import hf_hub_download, HfApi
from autogluon.tabular import TabularPredictor
import gradio as gr
MODEL_REPO = "samder03/2025-24679-tabular-autolguon-predictor"
LOCAL_DIR = "/tmp/autogluon_predictor"
os.makedirs(LOCAL_DIR, exist_ok=True)
api = HfApi()
files = api.list_repo_files(repo_id=MODEL_REPO)
print("Files found:", files)
from autogluon.common.loaders import load_pkl
predictor = None
if "autogluon_predictor.pkl" in files:
predictor_path = hf_hub_download(
repo_id=MODEL_REPO,
filename="autogluon_predictor.pkl",
local_dir=LOCAL_DIR
)
predictor = load_pkl.load(path=predictor_path)
elif "autogluon_predictor_dir.zip" in files:
zip_path = hf_hub_download(
repo_id=MODEL_REPO,
filename="autogluon_predictor_dir.zip",
local_dir=LOCAL_DIR
)
import zipfile
with zipfile.ZipFile(zip_path, 'r') as zip_ref:
zip_ref.extractall(LOCAL_DIR)
predictor = TabularPredictor.load(LOCAL_DIR)
else:
raise FileNotFoundError("Could not find supported predictor file in repo.")
# load dataset sample to gather columns for widgets (optional: you can hardcode the widgets)
from datasets import load_dataset
ds_orig = load_dataset("ecopus/pokemon_cards", split="original")
ds_aug = load_dataset("ecopus/pokemon_cards", split="augmented")
df_orig = pd.DataFrame(ds_orig)
df_aug = pd.DataFrame(ds_aug)
df = pd.concat([df_orig, df_aug])
label_col = predictor.label if hasattr(predictor, "label") else predictor._label
features = [c for c in df.columns if c != label_col]
# same simple mapping to widgets as in the notebook:
feature_specs = {}
for c in features:
col = df[c]
if pd.api.types.is_numeric_dtype(col):
minv, maxv = float(col.min()), float(col.max())
step = max((maxv - minv)/100.0, 0.01)
feature_specs[c] = ("numeric", minv, maxv, step, float(col.median()))
else:
uniques = sorted(pd.Series(col.dropna().unique()).astype(str).tolist())
if len(uniques) <= 20:
feature_specs[c] = ("categorical", uniques)
else:
feature_specs[c] = ("text",)
# Build Gradio input widgets
inputs = []
input_names = []
for c in features:
input_names.append(c)
col = df[c]
if pd.api.types.is_numeric_dtype(col):
inputs.append(gr.Number(label=c))
elif pd.api.types.is_bool_dtype(col):
inputs.append(gr.Checkbox(label=c))
else:
inputs.append(gr.Textbox(value="", label=c))
# No submodels available in this predictor
prob_toggle = gr.Checkbox(value=True, label="Return probabilities (vs. hard label)")
def predict_record(*args):
record = {name: val for name, val in zip(input_names, args[:-1])}
return_prob = args[-1]
df_in = pd.DataFrame([record])
if return_prob:
try:
proba = predictor.predict_proba(df_in)
proba_row = proba.iloc[0].to_dict()
top_label = max(proba_row, key=proba_row.get)
return {"label": top_label, "probabilities": proba_row}
except Exception:
label = predictor.predict(df_in).iloc[0]
return {"label": label}
else:
label = predictor.predict(df_in).iloc[0]
return {"label": label}
iface = gr.Interface(
fn=predict_record,
inputs=inputs + [prob_toggle],
outputs=gr.Label(num_top_classes=3, label="Prediction"),
title="Pokémon Card Collector's Item Predictor (AutoGluon)",
description="Predicts whether a Pokémon card is a collector's item.")
if __name__ == "__main__":
iface.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))
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