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from typing import Dict, Any |
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from flow_modules.Tachi67.AbstractBossFlowModule import CtrlExMemFlow |
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from aiflows.base_flows import CircularFlow |
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class CtrlExMem_JarvisFlow(CtrlExMemFlow): |
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def _on_reach_max_round(self): |
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self._state_update_dict({ |
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"result": "the maximum amount of rounds was reached before the Jarvis flow has done the job", |
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"summary": "JarvisFlow: the maximum amount of rounds was reached before the flow has done the job", |
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"status": "unfinished" |
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}) |
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@CircularFlow.output_msg_payload_processor |
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def detect_finish_or_continue(self, output_payload: Dict[str, Any], src_flow) -> Dict[str, Any]: |
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command = output_payload["command"] |
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if command == "finish": |
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return { |
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"EARLY_EXIT": True, |
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"result": output_payload["command_args"]["summary"], |
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"summary": "Jarvis: " + output_payload["command_args"]["summary"], |
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"status": "finished" |
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} |
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elif command == "manual_finish": |
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return { |
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"EARLY_EXIT": True, |
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"result": "JarvisFlow was terminated explicitly by the user, process is unfinished", |
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"summary": "Jarvis: process terminated by the user explicitly, nothing generated", |
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"status": "unfinished" |
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} |
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elif command == "update_plan": |
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keys_to_fetch_from_state = ["memory_files"] |
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fetched_state = self._fetch_state_attributes_by_keys(keys=keys_to_fetch_from_state) |
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output_payload["command_args"]["memory_files"] = fetched_state["memory_files"] |
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return output_payload |
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elif command == "re_plan": |
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keys_to_fetch_from_state = ["plan", "memory_files"] |
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fetched_state = self._fetch_state_attributes_by_keys(keys=keys_to_fetch_from_state) |
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output_payload["command_args"]["plan_file_location"] = fetched_state["memory_files"]["plan"] |
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output_payload["command_args"]["plan"] = fetched_state["plan"] |
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return output_payload |
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else: |
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return output_payload |
