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- #
- # Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- #
- import random
- from abc import ABC
- from functools import partial
- from typing import Tuple, Union
-
- import pandas as pd
-
- from agent.component.base import ComponentBase, ComponentParamBase
-
-
- class AnswerParam(ComponentParamBase):
-
- """
- Define the Answer component parameters.
- """
- def __init__(self):
- super().__init__()
- self.post_answers = []
-
- def check(self):
- return True
-
-
- class Answer(ComponentBase, ABC):
- component_name = "Answer"
-
- def _run(self, history, **kwargs):
- if kwargs.get("stream"):
- return partial(self.stream_output)
-
- ans = self.get_input()
- if self._param.post_answers:
- ans = pd.concat([ans, pd.DataFrame([{"content": random.choice(self._param.post_answers)}])], ignore_index=False)
- return ans
-
- def stream_output(self):
- res = None
- if hasattr(self, "exception") and self.exception:
- res = {"content": str(self.exception)}
- self.exception = None
- yield res
- self.set_output(res)
- return
-
- stream = self.get_stream_input()
- if isinstance(stream, pd.DataFrame):
- res = stream
- answer = ""
- for ii, row in stream.iterrows():
- answer += row.to_dict()["content"]
- yield {"content": answer}
- else:
- for st in stream():
- res = st
- yield st
- if self._param.post_answers:
- res["content"] += random.choice(self._param.post_answers)
- yield res
-
- self.set_output(res)
-
- def set_exception(self, e):
- self.exception = e
-
- def output(self, allow_partial=True) -> Tuple[str, Union[pd.DataFrame, partial]]:
- if allow_partial:
- return super.output()
-
- for r, c in self._canvas.history[::-1]:
- if r == "user":
- return self._param.output_var_name, pd.DataFrame([{"content": c}])
-
- self._param.output_var_name, pd.DataFrame([])
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