Llama-Agent-CSV / streaming.py
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Update message format and llama version
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from transformers.agents.agent_types import AgentAudio, AgentImage, AgentText, AgentType
from transformers.agents import ReactAgent
def pull_message(step_log: dict):
try:
from gradio import ChatMessage
except ImportError:
raise ImportError("Gradio should be installed in order to launch a gradio demo.")
if step_log.get("rationale"):
yield ChatMessage(role="assistant", content=step_log["rationale"])
if step_log.get("tool_call"):
used_code = step_log["tool_call"]["tool_name"] == "code interpreter"
content = step_log["tool_call"]["tool_arguments"]
if used_code:
content = f"```py\n{content}\n```"
yield ChatMessage(
role="assistant",
metadata={"title": f"🛠️ Used tool {step_log['tool_call']['tool_name']}"},
content=content,
)
if step_log.get("observation"):
yield ChatMessage(role="assistant", content=f"```\n{step_log['observation']}\n```")
if step_log.get("error"):
yield ChatMessage(
role="assistant",
content=str(step_log["error"]),
metadata={"title": "💥 Error"},
)
def stream_to_gradio(agent: ReactAgent, task: str, **kwargs):
"""Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages."""
try:
from gradio import ChatMessage
except ImportError:
raise ImportError("Gradio should be installed in order to launch a gradio demo.")
class Output:
output: AgentType | str = None
for step_log in agent.run(task, stream=True, **kwargs):
if isinstance(step_log, dict):
for message in pull_message(step_log):
print("message", message)
yield message
Output.output = step_log
if isinstance(Output.output, AgentText):
yield ChatMessage(role="assistant", content=f"**Final answer:**\n```\n{Output.output.to_string()}\n```")
elif isinstance(Output.output, AgentImage):
yield ChatMessage(
role="assistant",
content={"path": Output.output.to_string(), "mime_type": "image/png"},
)
elif isinstance(Output.output, AgentAudio):
yield ChatMessage(
role="assistant",
content={"path": Output.output.to_string(), "mime_type": "audio/wav"},
)
else:
yield ChatMessage(role="assistant", content=Output.output)