Update config.py

config.py

@@ -1,36 +1,62 @@
-# uses postponed evaluation of type annotations for forward-compatible typing behavior
+# Enables postponed evaluation of type annotations for forward-compatible typing behavior
 from __future__ import annotations
+
 # Provides access to environment variables used to configure the application at runtime
 import os
 
 # defines the display title used by the ML app
 APP_TITLE = "Agentic Machine Learning"
+
 # reads the Hugging Face authentication token from the environment and removes surrounding whitespace
 HF_TOKEN = os.getenv("HF_TOKEN", "").strip()
+
 # selects the HF model ID while providing a default model when none is configured
-HF_MODEL_ID = os.getenv("HF_MODEL_ID", "Qwen/Qwen3-Coder-30B-A3B-Instruct").strip()
+HF_MODEL_ID = os.getenv(
+    "HF_MODEL_ID",
+    "Qwen/Qwen3-Coder-30B-A3B-Instruct",
+).strip()
+
 # selects the HF inference provider and falls back to automatic provider selection
 HF_PROVIDER = os.getenv("HF_PROVIDER", "auto").strip() or "auto"
 
 # sets the max allowed upload size in megabytes from an environment variable or default value
 MAX_UPLOAD_MB = int(os.getenv("MAX_UPLOAD_MB", "50"))
+
 # limits the number of rows used when profiling uploaded datasets
 MAX_PROFILE_ROWS = int(os.getenv("MAX_PROFILE_ROWS", "100000"))
+
 # limits the number of rows used during model training to control runtime and resource usage
 MAX_TRAIN_ROWS = int(os.getenv("MAX_TRAIN_ROWS", "25000"))
+
 # max number of tool-driven reasoning steps the agent may execute
 MAX_AGENT_STEPS = int(os.getenv("MAX_AGENT_STEPS", "4"))
+
 # default max number of tokens allowed in generated model responses
 DEFAULT_MAX_TOKENS = int(os.getenv("DEFAULT_MAX_TOKENS", "1800"))
+
 # deterministic random seed
 RANDOM_STATE = int(os.getenv("RANDOM_STATE", "42"))
 
+# Maximum ZeroGPU allocation for the decorated agent callback.
+# Hugging Face ZeroGPU defaults to 60 seconds; this project allows more time
+# because the agent can execute several bounded tool/inference steps.
+ZERO_GPU_DURATION = int(os.getenv("ZERO_GPU_DURATION", "120"))
+
 # lists the dataset file extensions accepted by the app
-SUPPORTED_DATA_EXTENSIONS = {".csv", ".parquet", ".json", ".jsonl", ".xlsx", ".xls"}
+SUPPORTED_DATA_EXTENSIONS = {
+    ".csv",
+    ".parquet",
+    ".json",
+    ".jsonl",
+    ".xlsx",
+    ".xls",
+}
 
 # HF repo containing the bundled example dataset
 EXAMPLE_DATASET_REPO = "scikit-learn/adult-census-income"
+
 # filename of the example dataset within the repo
 EXAMPLE_DATASET_FILE = "adult.csv"
+
 # target column used for the example ML modeling task
-EXAMPLE_DATASET_TARGET = "income"
+EXAMPLE_DATASET_TARGET = "income"