Upload amazon_chronos-2_0.py with huggingface_hub

amazon_chronos-2_0.py

@@ -0,0 +1,77 @@
+# /// script
+# requires-python = ">=3.12"
+# dependencies = [
+#     "torch",
+#     "torchvision",
+#     "transformers",
+#     "diffusers",
+#     "sentence-transformers",
+#     "accelerate",
+#     "peft",
+#     "slack-sdk",
+# ]
+# ///
+
+try:
+    import pandas as pd
+    from chronos import BaseChronosPipeline
+    
+    pipeline = BaseChronosPipeline.from_pretrained("amazon/chronos-2", device_map="cuda")
+    
+    # Load historical data
+    context_df = pd.read_csv("https://autogluon.s3.us-west-2.amazonaws.com/datasets/timeseries/misc/AirPassengers.csv")
+    
+    # Generate predictions
+    pred_df = pipeline.predict_df(
+        context_df,
+        prediction_length=36,  # Number of steps to forecast
+        quantile_levels=[0.1, 0.5, 0.9],  # Quantiles for probabilistic forecast
+        id_column="item_id",  # Column identifying different time series
+        timestamp_column="Month",  # Column with datetime information
+        target="#Passengers",  # Column(s) with time series values to predict
+    )
+    with open('amazon_chronos-2_0.txt', 'w', encoding='utf-8') as f:
+        f.write('Everything was good in amazon_chronos-2_0.txt')
+except Exception as e:
+    import os
+    from slack_sdk import WebClient
+    client = WebClient(token=os.environ['SLACK_TOKEN'])
+    client.chat_postMessage(
+        channel='#hub-model-metadata-snippets-sprint',
+        text='Problem in <https://huggingface.co/datasets/model-metadata/code_execution_files/blob/main/amazon_chronos-2_0.txt|amazon_chronos-2_0.txt>',
+    )
+
+    with open('amazon_chronos-2_0.txt', 'a', encoding='utf-8') as f:
+        import traceback
+        f.write('''```CODE: 
+import pandas as pd
+from chronos import BaseChronosPipeline
+
+pipeline = BaseChronosPipeline.from_pretrained("amazon/chronos-2", device_map="cuda")
+
+# Load historical data
+context_df = pd.read_csv("https://autogluon.s3.us-west-2.amazonaws.com/datasets/timeseries/misc/AirPassengers.csv")
+
+# Generate predictions
+pred_df = pipeline.predict_df(
+    context_df,
+    prediction_length=36,  # Number of steps to forecast
+    quantile_levels=[0.1, 0.5, 0.9],  # Quantiles for probabilistic forecast
+    id_column="item_id",  # Column identifying different time series
+    timestamp_column="Month",  # Column with datetime information
+    target="#Passengers",  # Column(s) with time series values to predict
+)
+```
+
+ERROR: 
+''')
+        traceback.print_exc(file=f)
+    
+finally:
+    from huggingface_hub import upload_file
+    upload_file(
+        path_or_fileobj='amazon_chronos-2_0.txt',
+        repo_id='model-metadata/code_execution_files',
+        path_in_repo='amazon_chronos-2_0.txt',
+        repo_type='dataset',
+    )