116 lines
4.5 KiB
Python
116 lines
4.5 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from .agent_types import AgentAudio, AgentImage, AgentText
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from .utils import console
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def pull_message(step_log: dict, test_mode: bool = True):
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try:
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from gradio import ChatMessage
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except ImportError:
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if test_mode:
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class ChatMessage:
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def __init__(self, role, content, metadata=None):
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self.role = role
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self.content = content
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self.metadata = metadata
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else:
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raise ImportError("Gradio should be installed in order to launch a gradio demo.")
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if step_log.get("rationale"):
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yield ChatMessage(role="assistant", content=step_log["rationale"])
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if step_log.get("tool_call"):
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used_code = step_log["tool_call"]["tool_name"] == "code interpreter"
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content = step_log["tool_call"]["tool_arguments"]
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if used_code:
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content = f"```py\n{content}\n```"
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yield ChatMessage(
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role="assistant",
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metadata={"title": f"🛠️ Used tool {step_log['tool_call']['tool_name']}"},
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content=str(content),
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)
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if step_log.get("observation"):
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yield ChatMessage(role="assistant", content=f"```\n{step_log['observation']}\n```")
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if step_log.get("error"):
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yield ChatMessage(
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role="assistant",
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content=str(step_log["error"]),
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metadata={"title": "💥 Error"},
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)
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def stream_to_gradio(agent, task: str, test_mode: bool = False, **kwargs):
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"""Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages."""
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try:
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from gradio import ChatMessage
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except ImportError:
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if test_mode:
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class ChatMessage:
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def __init__(self, role, content, metadata=None):
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self.role = role
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self.content = content
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self.metadata = metadata
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else:
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raise ImportError("Gradio should be installed in order to launch a gradio demo.")
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for step_log in agent.run(task, stream=True, **kwargs):
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if isinstance(step_log, dict):
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for message in pull_message(step_log, test_mode=test_mode):
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yield message
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final_answer = step_log # Last log is the run's final_answer
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if isinstance(final_answer, AgentText):
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yield ChatMessage(role="assistant", content=f"**Final answer:**\n```\n{final_answer.to_string()}\n```")
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elif isinstance(final_answer, AgentImage):
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yield ChatMessage(
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role="assistant",
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content={"path": final_answer.to_string(), "mime_type": "image/png"},
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)
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elif isinstance(final_answer, AgentAudio):
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yield ChatMessage(
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role="assistant",
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content={"path": final_answer.to_string(), "mime_type": "audio/wav"},
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)
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else:
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yield ChatMessage(role="assistant", content=str(final_answer))
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class Monitor:
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def __init__(self, tracked_llm_engine):
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self.step_durations = []
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self.tracked_llm_engine = tracked_llm_engine
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if getattr(self.tracked_llm_engine, "last_input_token_count", "Not found") != "Not found":
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self.total_input_token_count = 0
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self.total_output_token_count = 0
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def update_metrics(self, step_log):
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step_duration = step_log["step_duration"]
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self.step_durations.append(step_duration)
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console.print(f"Step {len(self.step_durations)}:")
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console.print(f"- Time taken: {step_duration:.2f} seconds")
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if getattr(self.tracked_llm_engine, "last_input_token_count", None) is not None:
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self.total_input_token_count += self.tracked_llm_engine.last_input_token_count
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self.total_output_token_count += self.tracked_llm_engine.last_output_token_count
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console.print(f"- Input tokens: {self.total_input_token_count}")
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console.print(f"- Output tokens: {self.total_output_token_count}")
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