Read excel in aria orchestrator

[Fede_O_WIIT]

Hello everyone,

I am trying to read an Excel file given as a mimeattach input using nodejs/python. In both cases, when I run the code, the workflow fails as follows:

Spoiler

errorOSError: [Errno 36] File name too long: '[test_entry.xlsx,application/vnd.openxmlformats-officedocument.spreadsheetml.sheet,15758]\nUEsDBBQABgAIAAAAIQAhjEY6cwEAAIwFAAATAAgCW0NvbnRlbnRfVHlwZXNdLnhtbCCiBAIooAACAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADEVMluwjAQvVfqP0S+VomBQ1VVBA5dji0S9ANMPCEWiW15Bgp/34lZVFUsQiD1kiix5232TH+4aupkCQGNs7noZh2RgC2cNnaWi6/Je/okEiRltaqdhVysAcVwcH/Xn6w9YMLVFnNREflnKbGooFGYOQ+WV0oXGkX8GWbSq2KuZiB7nc6jLJwlsJRSiyEG/Vco1aKm5G3FvzdKpsaK5GWzr6XKhfK+NoUiFiqXVv8hSV1ZmgK0KxYNQ2foAyiNFQA1deaDYcYwBiI2hkIe5AxQ42WkW1cZV0ZhWBmPD2z9CEO7ctzVtu6TjyMYDclIBfpQDXuXq1p+uzCfOjfPToNcGk2MKGuUsTvdJ/jjZpTx1b2xkNZfBL5QR++fdBDfdZDxeX0UEeaMcaR1DXjr44+g55grFUCPibtodnMBv7FP6eDWHgXnkadHgMtT2LVqW516BoJABvbNeujS7xl59FwdO7SzTYM+wC3jLB38AAAA//8DAFBLAwQUAAYACAAAACEAtVUwI/QAAABMAgAACwAIAl9yZWxzLy5yZWxzIKIEAiigAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAKySTU/DMAyG70j8h8j31d2QEEJLd0FIuyFUfoBJ3A+1jaMkG92/JxwQVBqDA0d/vX78ytvdPI3qyCH24jSsixIUOyO2d62Gl/pxdQcqJnKWRnGs4cQRdtX11faZR0p5KHa9jyqruKihS8nfI0bT8USxEM8uVxoJE6UchhY9mYFaxk1Z3mL4rgHVQlPtrYawtzeg6pPPm3/XlqbpDT+IOUzs0pkVyHNiZ9mufMhsIfX5GlVTaDlpsGKecjoieV9kbMDzRJu/E/18LU6cyFIiNBL4Ms9HxyWg9X9atDTxy515xDcJw6vI8MmCix+o3gEAAP//AwBQSwMEFAAGAAgAAAAhAAp706SJAwAA+AgAAA8AAAB4bC93b3JrYm9vay54bWysVW1vm0gQ/n7S/QfEd8IubwYUUhkDukhJFblu0kqRqjUsZhVgfcsSO4r63zuLjZPUVeVLz8L7Ojz7zMwzy/mHbVNrj1R0jLeRjs+QrtE25wVrV5H+eZEZvq51krQFqXlLI/2JdvqHi7//Ot9w8bDk/EEDgLaL9ErKdWiaXV7RhnRnfE1b2Cm5aIiEqViZ3VpQUnQVpbKpTQshz2wIa/UdQihOweBlyXKa8LxvaCt3IILWRAL9rmLrbkRr8lPgGiIe+rWR82YNEEtWM/k0gOpak4eXq5YLsqzB7S12ta2Ax4M/RtBY40mwdXRUw3LBO17KM4A2d6SP/MfIxPhNCLbHMTgNyTEFfWQqhwdWwnsnK++A5b2AYfTHaBikNWglhOC9E809cLP0i/OS1fR2J12NrNcfSaMyVetaTTqZFkzSItInMOUb+mZB9Ou4ZzXsWt7Exrp5cZDzjdAKWpK+lgsQ8ggPleF5geUqSxDGtJZUtETSGW8l6HDv159qbsCeVRwUrs3pvz0TFAoL9AW+QkvykCy7GyIrrRd1pM/C+88duH9f0oIKlvMz3iwFk+w+4Zu25lBr969ESo4r4j/IlOTKdxOc3xHcjX8OBPAU4SjFGyk0GF8mV5COT+QRkgMSKPa1ewnR9789Z7Opb8UWMhwbzwwHOZbhu/HEQK6HJkmMrOkk/g5eCC/MOelltU+4wox0B7J7tHVNtuMORmHPipfzn9H+Z6j+p2bc+648VVfbLaOb7kUaaqpt71hb8E2kG1gJ+untdDNs3rFCViCZwLHAZLf2D2WrChhj8FAVgrAUs0h/TjxkowRPjMTzseFYiWf4KbKNxM58J7BdFAfpwMh8RWm4RIHa0GvtIPyMr2rGMVzX6oZV4YWxCNUh4rIYRH5sDrfYwRzGB3NryPZ4TE7qHApDdQNugJEVKAu6lVedHHrQJAN3sIOmExQ4Bkpt13D8APLp2JYxcxIrdSdpksauyqf6aIT/x9U5lEY4fo0Uy4oIuRAkf4Bv2JyWMelAeYP/JvB9TTZ2/RjZQNHJcGY4OEBGHHuO4SaZ7U5wMkvd7IWscr9858Xlm8PblMgeilrV8zAPVZvtVw+L5W5hn9c3RRrOExX3/du/M/wE3tf0ROPs9kTD2cfrxfWJtlfp4ttddqrx9DpOpqfbT+fz6ddF+mU8wvxlQHcJV+0gU3OUycUPAAAA//8DAFBLAwQUAAYACAAAACEASqmmYfoAAABHAwAAGgAIAXhsL19yZWxzL3dvcmtib29rLnhtbC5yZWxzIKIEASigAAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAvJLNasQwDITvhb6D0b1xkv5Qyjp7KYW9ttsHMLESh01sY6k/efualO42sKSX0KMkNPMxzGb7OfTiHSN13ikoshwEutqbzrUKXvdPV/cgiLUzuvcOFYxIsK0uLzbP2GtOT2S7QCKpOFJgmcODlFRbHDRlPqBLl8bHQXMaYyuDrg+6RVnm+Z2MvzWgmmmKnVEQd+YaxH4Myflvbd80XY2Pvn4b0PEZC8mJC5Ogji2ygmn8XhZZAgV5nqFck+HDxwNZRD5xHFckp0u5BFP8M8xiMrdrwpDVEc0Lx1Q+OqUzWy8lc7MqDI996vqxKzTNP/ZyVv/qCwAA//8DAFBLAwQUAAYACAAAACEAisoO6tMFAAAoDwAAGAAAAHhsL3dvcmtzaGVldHMvc2hlZXQxLnhtbJyT246bMBCG7yv1HSzfg4FAQqKQFSIbde+qbg/XjhmCFYyp7ZxU9d07kMOulJt0JWAGY3///PYwfzqqhuzBWKnbjIZ+QAm0Qpey3WT0x/eVl1JiHW9L3ugWMnoCS58Wnz/ND9psbQ3gCBJam9HauW7GmBU1KG593UGLXyptFHf4ajbMdgZ4OSxSDYuCYMwUly09E2bmEYauKilgqcVOQevOEAMNd1i/rWVnrzQlHsEpbra7zhNadYhYy0a60wClRInZy6bVhq8b9H0MYy7I0eAV4T26ygzjd0pKCqOtrpyPZHau+d7+lE0ZFzfSvf+HMGHMDOxlf4BvqOhjJYXJjRW9wUYfhI1vsH67zGwny4z+KaZxOgmKwFslxbMXT0axl+dh6o2KNC/ycJrmy+QvXcxLiSfcuyIGqozm4WwVU7aYD/3zU8LBvsuJ4+tXaEA4QI2Qkr4911pv+4kvOBQg0Q4TeiIXTu6hgKbJ6PMEO/z3oIEpCrCbwvv8qrYaGvqrISVUfNe4b/rwBeSmdigb+wka7TtlVp6WYAW2KEr7o6TnCt0gBJ9ESfzXcIMVPw7xIEtX4/rAnwbjCBlrsG4leyQlYmedVr8uUy6gMyK+IDBeEFHqT6Jx8j8QlBvqwHitI/XTcByfS7mXZ4ORfwAAAP//AAAA//+Uk1sOgyAQRbdiWEDxgY8YNKmvfRBK0i/biLHt7gu2UWboR/0j3APDnTtwfVVq7sQsaj7dHsFUkYgE+i5GbVZlRoJnxIQsL69OaanGuSLhKUlJzaVlzxauSEICI2izu9QJp0vNqfwSjU8wSLQ+EUKi84kIEr1PxJAYfCLLNoQa71sD4iMNMPBmPUc1G3tTRdjalyhO4rRgaYHcfxjzuv0aZK5za6DjPaiPLLtalv82a6L7P2035xwHDUScMRCxPyCi7HtX3ANbp28A2t4ZkCY7YLC1sP0B1EzwOhJ0/x9vAAAA//8AAAD//8xVXY+bOhD9K9RaqU9ebGPMh5JIYECq1L60Ut+9rJNYC5gaR5u26n/vQHa3d1PtTXV1q/bFxuPhMGfmzLC6VV59VJ2B3dhhClp7GPwaUbRZPb8K/OdRr1FnJo8C1XX2vuzUcDe7BtPe3r8ZxoN/p6dJ7cDtwVg7Z90z4yent2skaSBFlEtKeBonAgVHlx/M7Rp9pTFNGCMSszImmDcFw2VZxLiiURFFNBYJib5BcH2bF53XblBeSzt4Pfjg2HfDlB8pU+0a7b0f8zCc2r3u1XTdm9bZyW79dWv70G63ptXhNDqtbqe91r7vQkYoDWmoWghnQep/hrGjHuBya12v/HRt3S7slbs7jBhgR8jhjemM/wxYRJyilHsLnwre608H4/S0Rkt8cLfs+ZzPDXrd2F1nLHt1VVyxHJboNUJoFf7DZxUC5RPYwr6BGtyo9m6zmoM5dIq+DPPksYD8eHOGPM/iZhU+L/xPhmmzGqHI75TbGVBMp7cgGHKdoMCZ3f7x2dtxscYouLHe2/7xtIeUazefIhRsrYUing7hCfeD9ocxGNWo3QfzBbSUocA6AwVeJLpGo3XeKQMydPmsGffmliJ4WR/9W0jmvAcHZ0BMUtaiiOMEZ0kpMb9JM1xUpcBVJrNIFkUZVc23x2ofKf9vqiFZmIEKzLAUlecvtBR/+hCk4lfUqY+tnlVJxMvo/09XPnUfy7ikDRE4JXWBuUwbXMqM4SpueMSSOq3Y0n2Qq/yHqI59vt2cK1gQkO98Mav4zHuap8BGskDCDBBscTzZTs7nApwBfsc0euJNklIkoArMSQlTp8wSnEYsA92ImnBCaBrJeer8G+9y7tzyisaXeFc0rx4G359nLkqW1lmS4LpKS8yjpsBpygmO4yJtmlikcSYuMZczc1jEJeY1zeu/hjlntUjTAmqexQJz6DOcFbzATcJqzuE3Qzm/xLyamVdX/BLxhubNrxJf2uVshkATwVQ7rcuMC++tu1t+XJvvAAAA//8DAFBLAwQUAAYACAAAACEAqB1s2OsHAABmLQAAGAAAAHhsL3dvcmtzaGVldHMvc2hlZXQyLnhtbJyT246bMBCG7yv1HZDvwZjTAgpZ5dCokXpRVT1cO8YEKxhT2zmp6rt3gCS7UlRttBIwBvv//hk8njyfZOMcuDZCtQUino8c3jJVinZboB/fV26KHGNpW9JGtbxAZ27Q8/Tjh8lR6Z2pObcOEFpToNraLsfYsJpLajzV8RZmKqUltfCqt9h0mtNyEMkGB76fYElFi0ZCrh9hqKoSjC8V20ve2hGieUMt5G9q0ZkrTbJHcJLq3b5zmZIdIDaiEfY8QJEjWb7etkrTTQN1n0hEmXPScAVwh1eb4fudkxRMK6Mq6wEZjznfl5/hDFN2I93X/xCGRFjzg+g38AUVvC8lEt9YwQssfCcsucH636XzvSgL9Cd5moezxSffTZ/8lRuRbOmm6XzlLqL5bL4k2dMyzv6i6aQUsMN9VY7mVYFmJF8TEiA8nQwd9FPwo3k1dqzqvvDKLnjTwGpIv+/QjVK7fuUanH2AGt5w1veKQyEc+GV1nEGX/x59YAwe+Gbyenw1XA1d/VU7Ja/ovrHf1PEzF9vawhGKvBiq7dslL89Lbhj0KZh7YdxzmWoAAk9Hiv7AQZ/R0xCPorR1gcLEC5IsDkkAGLY3Vslf4wy56EclFDgo+0IvSvBIwiB9SwmbMSghXpQk8TI/ecMwusggXmW+l5Ik+p8SD7X+AwAA//8AAAD//5SaYW7bOBCFrxL4AGtTpCg7cALUMSDxGEE2wP7qLuqgu739SmxEzrwXQp4/Rfv4zJmhZj7Jqs+3v97fP66vH6/P5x9///vw42nndg+3f16/3+a/PYbdw38uvL49/vnr+n57e//+8bQ7/OH73fP5bfF+m82zdJv//fP5cN7/fD7v3z7XLnLN6bUXudbptatci7Es7uf8SpKdJcnF/LSb/yypniBVuXaEVOVaD6nKtTh8naq3pLqYdaoOj3W2lDoGWHyRi/Xo8rW6qrVapTrX+YLff/EXMySL13m21GTh8ORarFdE5dNb8lnMkE/tLbVttGy7mGFb//WlHizbLmbYNny97dGy7WKGbWvXqkM4WbZdzLBtYzTdwbJvdsPGjUFyNjItbti40fTORJPsho0b3etMs5/deuOuzre6dM40p9kNG9dB1RubBs7xxHWNiXOmkctuyLgxc04O3dylG/crnrquTl3m5Jh3XILvP+9kEylJKvoA5bBup8PT2jWm1clx3d6Y57WD+8GYd9R1rp9aK0/So2/Acso30+kWN1zPxpR3csq3N+Yp7+AGPuYdVZ2kJKnoOiUbttPhR42uwYZOsmF7Y34w8HDvH/OOus71U+V6So+uUxJlOx2+9fsGUTpJlO2NmSgeng/HvKOuc/1UrVMouk7Joe10+N7vGxzqTBzKbj0QHjlUPIVDpCSp6DpNHOqYQ77Boc7EoeyGOpFDxVPrJA5Jj6rTmziU3ZBO67HdxCHPHPLIoeIpdZKSpKLrNHHIM4d8g0PexKHs1gcYkEPFU+skDkmPrtPEIc8cCg0OzV9a61ebzcHPbqgTOVQ8tU7ikPToOk0c8syh0OCQN3Eou6FO5FDx1DrXp6jCW+nRdZo45JlDocEhb+JQdkOdyKHiqXUSh6RH1RlMHMpuSKfBoWDiUHbDxsih4il1kpKkous0cSgwh0KDQ8HEoezWdfbIoeKpdRKHpEfXaeJQYA71DQ4FE4eyG+pEDhVPrZM4JD26ThOHAnOob3AomDiU3VAncqh4ap3EIenRdZo4FJhDfYNDwcSh7IY6kUPFU+skDkmPqrM3cSi7IZ0Gh3oTh7IbNkYOFU+pk5QkFV2niUM9c6jHd8DFs6bzQspVKjodE716/jYXkV7FU0+H6CU9Oh0TvXqmV4QXuZfiqaezfmpVrtKj0zExr+fvgBGZVzz1dIh50qPTMTGvZ+bFyrz8yuhSPPV01k/V0xGKTsdEyp7fYEUkZfHU0yFSSo9Ox0TKfnaXN/wR/n/ksqzm9z71XFC5So9OxETWfmWk+A8HaOKxeOq5EFmlR6UTLWS9ZLd8SfFCylUqOpSJtXH97rlWNZIykZKkooNbyHqJK1nLFSblKhUdykTNiPwbSZlISVLRwS1QGiPiZSIlSUWHMgEntkARLaAYs1t24ERKkorO2ASBKIZab2MZ4THiME6kJKmoUIPpwSe7Pw9Hb2MZvXHA0ZtISVLRoSzdPw7Y/RMpSSo6lKnXB+p1UpJUdChTkw54X5pISVLRoUzdNVB3kZKkokIdLd01ZrcaPVKSVHSoyt/hcetF1XhE/k6kJKnoUPUx8Z5Q+MA3HVFJUtGhKgLvCYVPT9MRlSQVHaqy655Q+EAyHVFJUlGhTrUt7giV3aotSElS0aFMbXGitiAlSUWHMrXFidqClCQVHcrUFidqC1KSVHQoU1ucqC1ISVJRodzB1Be/7aoxWEpKgnCm3nAHag6W5nDCBeFM/eEO1CAszeGEC8KZesQdqElYmsO1HrLcwdQnv+1w7QggyqWrEz+XuQMhrvxepnyFYSkpCcLZWqX82EWEw+6Zw1Gr7OvvCv8HAAD//wAAAP//NI1BCsJADEWvMuQAVhERpdO9i656gsikM0GdhDQieHpbYXb/fXj/94qZRrTMdQlPmj3CfneGYJxLyy76b08Q7uIur0aFMJFtdIQwi3iDbui33Yn8rUFRySb+UoQLBDGm6ugsNYKKuSH7+nflFMFu6QCr3H3EHksh8uEHAAD//wMAUEsDBBQABgAIAAAAIQAOEQmnVgcAAMggAAATAAAAeGwvdGhlbWUvdGhlbWUxLnhtbOxZW48bNRR+R+I/WPOe5jaTy6opyrVLu9tW3bSIR2/iZNz1jCPb2W2EKqHyxAsSEiBekHjjASGQQALxwo+p1IrLj+DYM8nYG4de2CJAu5FWGec7x8fnHH8+c3z1rYcJQ6dESMrTTlC9UgkQSSd8StN5J7g3HpVaAZIKp1PMeEo6wYrI4K1rb75xFe+pmCQEgXwq93AniJVa7JXLcgLDWF7hC5LCbzMuEqzgUczLU4HPQG/CyrVKpVFOME0DlOIE1I5BBk0puj2b0QkJrq3VDxnMkSqpByZMHGnlJJexsNOTqkbIlewzgU4x6wQw05SfjclDFSCGpYIfOkHF/AXla1fLeC8XYmqHrCU3Mn+5XC4wPamZOcX8eDNpGEZho7vRbwBMbeOGzWFj2NjoMwA8mcBKM1tcnc1aP8yxFij76tE9aA7qVQdv6a9v2dyN9MfBG1CmP9zCj0Z98KKDN6AMH23ho167N3D1G1CGb2zhm5XuIGw6+g0oZjQ92UJXoka9v17tBjLjbN8Lb0fhqFnLlRcoyIZNdukpZjxVu3ItwQ+4GAFAAxlWNEVqtSAzPIE87mNGjwVFB3QeQ+ItcMolDFdqlVGlDv/1JzTfTETxHsGWtLYLLJFbQ9oeJCeCLlQnuAFaAwvy9Kefnjz+4cnjH5988MGTx9/mcxtVjtw+Tue23O9fffzHF++j377/8vdPPs2mPo+XNv7ZNx8++/mXv1IPKy5c8fSz75798N3Tzz/69etPPNq7Ah/b8DFNiES3yBm6yxNYoMd+cixeTmIcY+pI4Bh0e1QPVewAb60w8+F6xHXhfQEs4wNeXz5wbD2KxVJRz8w348QBHnLOelx4HXBTz2V5eLxM5/7JxdLG3cX41Dd3H6dOgIfLBdAr9ansx8Qx8w7DqcJzkhKF9G/8hBDP6t6l1PHrIZ0ILvlMoXcp6mHqdcmYHjuJVAjt0wTisvIZCKF2fHN4H/U48616QE5dJGwLzDzGjwlz3HgdLxVOfCrHOGG2ww+win1GHq3ExMYNpYJIzwnjaDglUvpkbgtYrxX0m8Aw/rAfslXiIoWiJz6dB5hzGzngJ/0YJwuvzTSNbezb8gRSFKM7XPngh9zdIfoZ4oDTneG+T4kT7ucTwT0gV9ukIkH0L0vhieV1wt39uGIzTHws0xWJw65dQb3Z0VvOndQ+IIThMzwlBN1722NBjy8cnxdG34iBVfaJL7FuYDdX9XNKJEGmrtmmyAMqnZQ9InO+w57D1TniWeE0wWKX5lsQdSd14ZTzUultNjmxgbcoFICQL16n3Jagw0ru4S6td2LsnF36WfrzdSWc+L3IHoN9+eBl9yXIkJeWAWJ/Yd+MMXMmKBJmjKHA8NEtiDjhL0T0uWrEll65mbtpizBAYeTUOwlNn1v8nCt7on+m7PEXMBdQ8PgV/51SZxel7J8rcHbh/oNlzQAv0zsETpJtzrqsai6rmuB/X9Xs2suXtcxlLXNZy/jevl5LLVOUL1DZFF0e0/NJdrZ8ZpSxI7Vi5ECaro+EN5rpCAZNO8r0JDctwEUMX/MGk4ObC2xkkODqHarioxgvoDVUNc3OucxVzyVacAkdIzNsmqnknG7Td1omh3yadTqrVd3VzFwosSrGK9FmHLpUKkM3mkX3bqPe9EPnpsu6NkDLvowR1mSuEXWPEc31IEThr4wwK7sQK9oeK1pa/TpU6yhuXAGmbaICr9wIXtQ7QRRmHWRoxkF5PtVxyprJ6+jq4FxopHc5k9kZACX2OgOKSLe1rTuXp1eXpdoLRNoxwko31wgrDWN4Ec6z0265X2Ss20VIHfO0K9a7oTCj2XodsdYkco4bWGozBUvRWSdo1CO4V5ngRSeYQccYviYLyB2p37owm8PFy0SJbMO/CrMshFQDLOPM4YZ0MjZIqCICMZp0Ar38TTaw1HCIsa1aA0L41xrXBlr5txkHQXeDTGYzMlF22K0R7ensERg+4wrvr0b81cFaki8h3Efx9Awds6W4iyHFomZVO3BKJVwcVDNvTinchG2IrMi/cwdTTrv2VZTJoWwcs0WM8xPFJvMMbkh0Y4552vjAesrXDA7dduHxXB+wf/vUff5RrT1nkWZxZjqsok9NP5m+vkPesqo4RB2rMuo279Sy4Lr2musgUb2nxHNO3Rc4ECzTiskc07TF2zSsOTsfdU27wILA8kRjh982Z4TXE6968oPc+azVB8S6rjSJby7N7VttfvwAyGMA94dLpqQJJdxZCwxFX3YDmdEGbJGHKq8R4RtaCtoJ3qtE3bBfi/qlSisalsJ6WCm1om691I2ienUYVSuDXu0RHCwqTqpRdmE/gisMtsqv7c341tV9sr6luTLhSZmbK/myMdxc3VdrztV9dg2PxvpmPkAUSOe9Rm3Urrd7jVK73h2VwkGvVWr3G73SoNFvDkaDftRqjx4F6NSAw269HzaGrVKj2u+XwkZFm99ql5phrdYNm93WMOw+yssYWHlGH7kvwL3Grmt/AgAA//8DAFBLAwQUAAYACAAAACEAnc7ZBUIDAABsCAAADQAAAHhsL3N0eWxlcy54bWykVttu4zYQfS/QfyD4rlCSLcc2JC3WdgQssF0skBToKy1RCrG8qBSVlVv03zukZFvpBm2aPIkcDs9czsxQ6YdBCvTETMe1ynB0E2LEVKkrrpoM//pQBGuMOktVRYVWLMMn1uEP+c8/pZ09CXb/yJhFAKG6DD9a224J6cpHJml3o1um4KTWRlILW9OQrjWMVp27JAWJw3BFJOUKjwhbWb4GRFLzrW+DUsuWWn7kgtuTx8JIlttPjdKGHgW4OkRLWqIhWpkYDeZsxEt/sCN5aXSna3sDuETXNS/Zj+5uyIbQ8ooEyG9DihISxs9iH8wbkZbEsCfu6MN5WmtlO1TqXlkgExx1Kdh+U/q7KtyRE45aedr9gZ6oAEmESZ6WWmiDLFAHmfMSRSUbNfZU8KPhTq2mkovTKI6dwLM96UkOuXdC4vyYPh1c4kJcvFo4B0CQp0CfZUYVsEHT+uHUgnkFlTbCeL3/0G4MPUVx8voLnRa8cl40ex+0aY4ZLorbZB/d3TqY43TAVcUGVmV4tfToM4ddcN45/4EYj9pU0EXX3J9FeSpYbQHV8ObRfa1unQ1tLVRanlacNlpR4dI2glwWAFsyIe5dp/1WP8MeaqR6WUj7CdyDnnUJPy/Br2k54o0bhz9HG7FnsAmk5P/DoqG+4L/jNvq915Z9Nazmw1R/70GjbStOHwVvlGRjK+QpFPG4dePO8tLVfgmnDEbDd0PbBzb4pnGJGupX5SLGaJ7iSy6Qt+8K+6VYFq+h6x9YX3p5ZKbws3TC9GwCf7MieVYiF7KR6+QMf3GXBYOhMDGGjj0XlqsX6gNAq+FacaErf+umqq/FixkovIrVtBf24XKY4ev6F1bxXkKWJq2v/ElbD5Hh6/qza4xodZlCYFya3vemC+7lHiUzFeJ1QBcY/NzB4IEv6g3P8J93u9vN4a6Ig3W4WwfLBUuCTbI7BMlyvzscik0Yh/u/Zk/DOx4G/5JB2UTLbSfg+TBTrqbY76+yDM82Y/R+voDbc9838Sr8mERhUCzCKFiu6DpYrxZJUCRRfFgtd3dJkcx8T974gIQkisanyDmfbC2XTHB1pvpM8FwKHMP2X4IgZybI9Tch/xsAAP//AwBQSwMEFAAGAAgAAAAhADd+/3b7AgAADwgAABQAAAB4bC9zaGFyZWRTdHJpbmdzLnhtbHRVUW/bOAx+P+D+g+CHw+5hSXvXrsWWZEhcNzWWtL7a7Q73EjAyUwuVJU+S0ya/fkyH4QAxe9QnkiI/fqRGn19bLbbovLJmnJwOThKBRtpamadx8lBdv79MhA9gatDW4DjZoU8+T37/beR9EORr/DhpQug+DodeNtiCH9gODd1srGsh0NE9DX3nEGrfIIZWD/86OfkwbEGZREjbmzBOLi8S0Rv1rcf0B3BxnkxGXk1GYfIIvQ6jYZiMhgfgB1hh22kIGOOpNQZloGJW1a5j17fQMuzGUiV5EUe6v2JQWd6UDWrNHt2t0U3dc1Eu46uveV6tKNRK7tYrqNvTVZUti8W0ymLLqfzWwx/Qdp9K6Ihq8W7Wr9ca/Z/MsnYKgpIM1xJMAAfsopWWYcaDrjnsOhAL267B1YoHcg5r+zeLVZUM6p11jOppH+zcKc7gDLTexTFm1HjFQBVeKDc04l15lzJuZsScPuJVLMWjCqygFLb0MnskpRi9BqeV4QprcCOyVxK090fUt1YdcoHYrfIKTGx/hSJFyZtzhbpowLWxfdbxrmc0vPzF63wxjb2vFQ22YUnP0R/mBUUB3r9YV8d+c2Vlo0ThcK+sZ2TNe9BQgHyO/W6mlXgv7pza763ho5ovCpZibjZWomPjTrgDH1wfQs/1nZst1WDdhmvoC9KwWEb8ojcg7q33TBFLMEaJubN9F9dDV0cEubRGanSxMW0aRWPKpFC8jXgamxdTtjoKrGktW6bOw9b96DuQtI0PKkS3xWRSLB7K7P4xTzPBYtuXQ6ANY7VwUDMCSnRPyr6Rw1pdWkkd/efgxgouSULdEZj6Ak/4VvWMRMJprfDZqE7ja5z3g1FIaySGH6s8hg5blmFKBZE21E7UgoraKolM+/81SJ8Ul+5+v/r5wwjT260VUis8IuJrq6lLPJ/bq7uvbCk+3Ob/shUwY0NA38xqnt1m93n6y98ikODjUC/KnMaY9w3DLj+cD87OBufnF4Oz/+2H9KFPvgMAAP//AwBQSwMEFAAGAAgAAAAhADttMkvBAAAAQgEAACMAAAB4bC93b3Jrc2hlZXRzL19yZWxzL3NoZWV0MS54bWwucmVsc4SPwYrCMBRF9wP+Q3h7k9aFDENTNyK4VecDYvraBtuXkPcU/XuzHGXA5eVwz+U2m/s8qRtmDpEs1LoCheRjF2iw8HvaLb9BsTjq3BQJLTyQYdMuvpoDTk5KiceQWBULsYVRJP0Yw37E2bGOCamQPubZSYl5MMn5ixvQrKpqbfJfB7QvTrXvLOR9V4M6PVJZ/uyOfR88bqO/zkjyz4RJOZBgPqJIOchF7fKAYkHrd/aea30OBKZtzMvz9gkAAP//AwBQSwMEFAAGAAgAAAAhABPELBPCAAAAQgEAACMAAAB4bC93b3Jrc2hlZXRzL19yZWxzL3NoZWV0Mi54bWwucmVsc4SPwWrDMBBE74X8g9h7JDuHUIolX0oh1yb9AEVe26L2Smi3Jfn76NiEQo7DY94wXX9ZF/WLhWMiC61uQCGFNESaLHydPravoFg8DX5JhBauyNC7zUv3iYuXWuI5ZlbVQmxhFslvxnCYcfWsU0aqZExl9VJjmUz24dtPaHZNszflrwPcnVMdBgvlMLSgTtdcl5+70zjGgO8p/KxI8s+EySWSYDmiSD3IVe3LhGJB60f2mHf6HAmM68zdc3cDAAD//wMAUEsDBBQABgAIAAAAIQDsS7koQAQAAOgSAAAnAAAAeGwvcHJpbnRlclNldHRpbmdzL3ByaW50ZXJTZXR0aW5nczEuYmluimGIYQhnCGbwZfBkcGFwZjBgMAaKBDFkMiQz5DNkMCgwODKkgXmZQL4CUF0AUIUBgyGQDQKMLAwKdxh4hPj/MzAxMnAyzOI24UhhYGRgZ4gA8hmBJBNY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Would anyone be able to help me or give me some guidance on the right direction to follow? Thank you!

[xian_]

Can you share the code you already have?

[Fede_O_WIIT]

Hi Xian,

At the moment, I am trying something very simple to read the file:

Probably i should use a different approach to read the Excel file, but unfortunately, i am really struggling to figure it out...

[xian_]

inputs["File"] is a string with filename plus the file contents, base64 encoded. It is not a filename: your input is not saved automatically into the container running your python script.

The first line of the string is the filename, content type and length. The remaining part is the file contents, base64 encoded.

You can extract the filename, and also the file contents with string functions, then process it further.

The filename is: inputs["File"].split(',', 1)[0][1:]

The contents is: base64.b64decode(inputs["file"].split('\n',1)[1]))

You can put it into a Python file object with: io.BytesIO(base64.b64decode(inputs["file"].split('\n',1)[1]))) and possibly save it.

See my blog for the background and an implemented workflow: https://kuklis.github.io/cma/post/vro8-import-workflow/

Don't expect large files to work...

[Fede_O_WIIT]

Hi Xian,

Thank you! You've cleared up my doubts.

Now I've managed to read the file and convert it into JSON format, but when I output the contents of the JSON to a variable 'json_data', it turns out to be empty in the environment, even though it is correctly printed in the console logs. Are you aware of any output issues on orchestrator's Python environments?

Thanks!

[xian_ · Accepted solution]

As scriptable tasks can have more than one output variables (unlike actions), you need to return a Python dictionary with the output variable names and values. In your case:

return { "json_data": json_data }