Create the output and run the model

Finally, we can create the output to catch all created data variables and run the model afterwards.
        return {'block_number': input.block_number,
                'count_txs': count.data[0]['count_tx'],
                'max_gas': max_gas,
                'total_gas_cost': total_gas_cost,
                'count_address': count_address}
In order to run the model, we will have to write credmark-dev run contrib.ape-count
The contrib.ape count is the model that uses -i '{"block_number": XXXX}' to pass in the input and use -j to pretty print the output.
Note: In Windows native (non-WSL), you need double double-quotes `-i '{""block_number"": XXXX}.
> credmark-dev run contrib.ape-count -i '{"block_number_count": 2}' -j
2022-10-25 16:01:34,156 - credmark.cmf.engine.context - INFO - Using latest block number 15823462
{
    "slug": "contrib.ape-count",
    "version": "1.0",
    "chainId": 1,
    "blockNumber": 15823462,
    "output": {
        "block_number_count": 2,
        "count_txs": 238,
        "max_gas": 2138710,
        "total_gas_cost": 0.1784772783190307,
        "count_address": 373
    },
    "dependencies": {
        "ledger.transaction_data": {
            "1.0": 2
        },
        "ledger.receipt_data": {
            "1.0": 1
        },
        "contrib.ape-count": {
            "1.0": 1
        }
    }
}
🥳 Congratulations! You finished your first model! 🥳
For all patient reader, the model source is available is https://github.com/credmark/credmark-models-py/blob/main/models/contrib/kunlun/example_count.py​
Last modified 1yr ago