#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. 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 mimetypes
import os
import re
import shutil
from typing import Optional
from smolagents.agent_types import AgentAudio, AgentImage, AgentText, handle_agent_output_types
from smolagents.agents import ActionStep, MultiStepAgent
from smolagents.memory import MemoryStep
from smolagents.utils import _is_package_available
def pull_messages_from_step(
step_log: MemoryStep,
):
"""Extract ChatMessage objects from agent steps with proper nesting"""
import gradio as gr
if isinstance(step_log, ActionStep):
# Output the step number
step_number = f"Step {step_log.step_number}" if step_log.step_number is not None else ""
yield gr.ChatMessage(role="assistant", content=f"**{step_number}**")
# First yield the thought/reasoning from the LLM
if hasattr(step_log, "model_output") and step_log.model_output is not None:
# Clean up the LLM output
model_output = step_log.model_output.strip()
# Remove any trailing <end_code> and extra backticks, handling multiple possible formats
model_output = re.sub(r"```\s*<end_code>", "```", model_output) # handles ```<end_code>
model_output = re.sub(r"<end_code>\s*```", "```", model_output) # handles <end_code>```
model_output = re.sub(r"```\s*\n\s*<end_code>", "```", model_output) # handles ```\n<end_code>
model_output = model_output.strip()
yield gr.ChatMessage(role="assistant", content=model_output)
# For tool calls, create a parent message
if hasattr(step_log, "tool_calls") and step_log.tool_calls is not None:
first_tool_call = step_log.tool_calls[0]
used_code = first_tool_call.name == "python_interpreter"
parent_id = f"call_{len(step_log.tool_calls)}"
# Tool call becomes the parent message with timing info
# First we will handle arguments based on type
args = first_tool_call.arguments
if isinstance(args, dict):
content = str(args.get("answer", str(args)))
else:
content = str(args).strip()
if used_code:
# Clean up the content by removing any end code tags
content = re.sub(r"```.*?\n", "", content) # Remove existing code blocks
content = re.sub(r"\s*<end_code>\s*", "", content) # Remove end_code tags
content = content.strip()
if not content.startswith("```python"):
content = f"```python\n{content}\n```"
parent_message_tool = gr.ChatMessage(
role="assistant",
content=content,
metadata={
"title": f"🛠️ Used tool {first_tool_call.name}",
"id": parent_id,
"status": "pending",
},
)
yield parent_message_tool
# Nesting execution logs under the tool call if they exist
if hasattr(step_log, "observations") and (
step_log.observations is not None and step_log.observations.strip()
): # Only yield execution logs if there's actual content
log_content = step_log.observations.strip()
if log_content:
log_content = re.sub(r"^Execution logs:\s*", "", log_content)
yield gr.ChatMessage(
role="assistant",
content=f"{log_content}",
metadata={"title": "📝 Execution Logs", "parent_id": parent_id, "status": "done"},
)
# Nesting any errors under the tool call
if hasattr(step_log, "error") and step_log.error is not None:
yield gr.ChatMessage(
role="assistant",
content=str(step_log.error),
metadata={"title": "💥 Error", "parent_id": parent_id, "status": "done"},
)
# Update parent message metadata to done status without yielding a new message
parent_message_tool.metadata["status"] = "done"
# Handle standalone errors but not from tool calls
elif hasattr(step_log, "error") and step_log.error is not None:
yield gr.ChatMessage(role="assistant", content=str(step_log.error), metadata={"title": "💥 Error"})
# Calculate duration and token information
step_footnote = f"{step_number}"
if hasattr(step_log, "input_token_count") and hasattr(step_log, "output_token_count"):
token_str = (
f" | Input-tokens:{step_log.input_token_count:,} | Output-tokens:{step_log.output_token_count:,}"
)
step_footnote += token_str
if hasattr(step_log, "duration"):
step_duration = f" | Duration: {round(float(step_log.duration), 2)}" if step_log.duration else None
step_footnote += step_duration
step_footnote = f"""<span style="color: #bbbbc2; font-size: 12px;">{step_footnote}</span> """
yield gr.ChatMessage(role="assistant", content=f"{step_footnote}")
yield gr.ChatMessage(role="assistant", content="-----")
def stream_to_gradio(
agent,
task: str,
reset_agent_memory: bool = False,
additional_args: Optional[dict] = None,
):
"""Runs an agent with the given task and streams the messages from the agent as gradio ChatMessages."""
if not _is_package_available("gradio"):
raise ModuleNotFoundError(
"Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"
)
import gradio as gr
total_input_tokens = 0
total_output_tokens = 0
for step_log in agent.run(task, stream=True, reset=reset_agent_memory, additional_args=additional_args):
# Track tokens if model provides them
if hasattr(agent.model, "last_input_token_count"):
total_input_tokens += agent.model.last_input_token_count
total_output_tokens += agent.model.last_output_token_count
if isinstance(step_log, ActionStep):
step_log.input_token_count = agent.model.last_input_token_count
step_log.output_token_count = agent.model.last_output_token_count
for message in pull_messages_from_step(
step_log,
):
yield message
final_answer = step_log # Last log is the run's final_answer
final_answer = handle_agent_output_types(final_answer)
if isinstance(final_answer, AgentText):
yield gr.ChatMessage(
role="assistant",
content=f"**Final answer:**\n{final_answer.to_string()}\n",
)
elif isinstance(final_answer, AgentImage):
yield gr.ChatMessage(
role="assistant",
content={"path": final_answer.to_string(), "mime_type": "image/png"},
)
elif isinstance(final_answer, AgentAudio):
yield gr.ChatMessage(
role="assistant",
content={"path": final_answer.to_string(), "mime_type": "audio/wav"},
)
else:
yield gr.ChatMessage(role="assistant", content=f"**Final answer:** {str(final_answer)}")
class GradioUI:
"""A one-line interface to launch your agent in Gradio"""
def __init__(self, agent: MultiStepAgent, file_upload_folder: str | None = None):
if not _is_package_available("gradio"):
raise ModuleNotFoundError(
"Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"
)
self.agent = agent
self.file_upload_folder = file_upload_folder
if self.file_upload_folder is not None:
if not os.path.exists(file_upload_folder):
os.mkdir(file_upload_folder)
def interact_with_agent(self, prompt, messages):
import gradio as gr
messages.append(gr.ChatMessage(role="user", content=prompt))
yield messages
for msg in stream_to_gradio(self.agent, task=prompt, reset_agent_memory=False):
messages.append(msg)
yield messages
yield messages
def upload_file(
self,
file,
file_uploads_log,
allowed_file_types=[
"application/pdf",
"application/vnd.openxmlformats-officedocument.wordprocessingml.document",
"text/plain",
],
):
"""
Handle file uploads, default allowed types are .pdf, .docx, and .txt
"""
import gradio as gr
if file is None:
return gr.Textbox("No file uploaded", visible=True), file_uploads_log
try:
mime_type, _ = mimetypes.guess_type(file.name)
except Exception as e:
return gr.Textbox(f"Error: {e}", visible=True), file_uploads_log
if mime_type not in allowed_file_types:
return gr.Textbox("File type disallowed", visible=True), file_uploads_log
# Sanitize file name
original_name = os.path.basename(file.name)
sanitized_name = re.sub(
r"[^\w\-.]", "_", original_name
) # Replace any non-alphanumeric, non-dash, or non-dot characters with underscores
type_to_ext = {}
for ext, t in mimetypes.types_map.items():
if t not in type_to_ext:
type_to_ext[t] = ext
# Ensure the extension correlates to the mime type
sanitized_name = sanitized_name.split(".")[:-1]
sanitized_name.append("" + type_to_ext[mime_type])
sanitized_name = "".join(sanitized_name)
# Save the uploaded file to the specified folder
file_path = os.path.join(self.file_upload_folder, os.path.basename(sanitized_name))
shutil.copy(file.name, file_path)
return gr.Textbox(f"File uploaded: {file_path}", visible=True), file_uploads_log + [file_path]
def log_user_message(self, text_input, file_uploads_log):
return (
text_input
+ (
f"\nYou have been provided with these files, which might be helpful or not: {file_uploads_log}"
if len(file_uploads_log) > 0
else ""
),
"",
)
def launch(self, **kwargs):
import gradio as gr
with gr.Blocks(fill_height=True) as demo:
stored_messages = gr.State([])
file_uploads_log = gr.State([])
chatbot = gr.Chatbot(
label="Agent",
type="messages",
avatar_images=(
None,
"https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/Alfred.png",
),
resizeable=True,
scale=1,
)
# If an upload folder is provided, enable the upload feature
if self.file_upload_folder is not None:
upload_file = gr.File(label="Upload a file")
upload_status = gr.Textbox(label="Upload Status", interactive=False, visible=False)
upload_file.change(
self.upload_file,
[upload_file, file_uploads_log],
[upload_status, file_uploads_log],
)
text_input = gr.Textbox(lines=1, label="Chat Message")
text_input.submit(
self.log_user_message,
[text_input, file_uploads_log],
[stored_messages, text_input],
).then(self.interact_with_agent, [stored_messages, chatbot], [chatbot])
demo.launch(debug=True, share=True, **kwargs)
__all__ = ["stream_to_gradio", "GradioUI"]