Create app.py

app.py

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+# Install necessary packages (only for local environment)
+# !pip install pandas granite-tsfm
+
+import pandas as pd
+from granite_tsfm import TimeSeriesPreprocessor, TinyTimeMixerForPrediction, TimeSeriesForecastingPipeline
+
+# Load dataset (Replace with actual dataset)
+data = pd.read_csv('your_dataset.csv', parse_dates=['timestamp_column'])
+
+# Preprocess the data
+tsp = TimeSeriesPreprocessor(
+    id_columns=[],
+    timestamp_column='timestamp_column',
+    target_columns=['value1', 'value2'],  # Replace with your target column names
+    prediction_length=96,
+    context_length=512,
+    scaling=True
+)
+processed_data = tsp.fit_transform(data)
+
+# Load the pre-trained model
+model = TinyTimeMixerForPrediction.from_pretrained(
+    'ibm-granite/granite-timeseries-ttm-r2',
+    num_input_channels=tsp.num_input_channels
+)
+
+# Generate forecasts
+pipeline = TimeSeriesForecastingPipeline(
+    model=model,
+    feature_extractor=tsp
+)
+forecasts = pipeline(data)
+
+# Display the forecasts
+print(forecasts)