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add agent description in UI
Browse files- Gradio_UI.py +77 -22
- app.py +13 -3
Gradio_UI.py
CHANGED
@@ -19,7 +19,12 @@ import re
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import shutil
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from typing import Optional
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-
from smolagents.agent_types import
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from smolagents.agents import ActionStep, MultiStepAgent
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from smolagents.memory import MemoryStep
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from smolagents.utils import _is_package_available
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@@ -33,7 +38,9 @@ def pull_messages_from_step(
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if isinstance(step_log, ActionStep):
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# Output the step number
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step_number =
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yield gr.ChatMessage(role="assistant", content=f"**{step_number}**")
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# First yield the thought/reasoning from the LLM
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@@ -41,9 +48,15 @@ def pull_messages_from_step(
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# Clean up the LLM output
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model_output = step_log.model_output.strip()
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# Remove any trailing <end_code> and extra backticks, handling multiple possible formats
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model_output = re.sub(
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model_output = model_output.strip()
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yield gr.ChatMessage(role="assistant", content=model_output)
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@@ -63,8 +76,12 @@ def pull_messages_from_step(
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if used_code:
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# Clean up the content by removing any end code tags
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content = re.sub(
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-
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content = content.strip()
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if not content.startswith("```python"):
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content = f"```python\n{content}\n```"
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@@ -90,7 +107,11 @@ def pull_messages_from_step(
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yield gr.ChatMessage(
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role="assistant",
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content=f"{log_content}",
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metadata={
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)
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# Nesting any errors under the tool call
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@@ -98,7 +119,11 @@ def pull_messages_from_step(
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yield gr.ChatMessage(
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role="assistant",
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content=str(step_log.error),
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metadata={
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)
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# Update parent message metadata to done status without yielding a new message
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@@ -106,17 +131,25 @@ def pull_messages_from_step(
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# Handle standalone errors but not from tool calls
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elif hasattr(step_log, "error") and step_log.error is not None:
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yield gr.ChatMessage(
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# Calculate duration and token information
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step_footnote = f"{step_number}"
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if hasattr(step_log, "input_token_count") and hasattr(
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step_footnote += token_str
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if hasattr(step_log, "duration"):
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step_duration =
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step_footnote += step_duration
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step_footnote = f"""<span style="color: #bbbbc2; font-size: 12px;">{step_footnote}</span> """
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yield gr.ChatMessage(role="assistant", content=f"{step_footnote}")
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@@ -139,7 +172,9 @@ def stream_to_gradio(
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total_input_tokens = 0
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total_output_tokens = 0
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for step_log in agent.run(
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# Track tokens if model provides them
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if hasattr(agent.model, "last_input_token_count"):
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total_input_tokens += agent.model.last_input_token_count
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@@ -172,19 +207,27 @@ def stream_to_gradio(
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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 gr.ChatMessage(
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class GradioUI:
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"""A one-line interface to launch your agent in Gradio"""
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def __init__(
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if not _is_package_available("gradio"):
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raise ModuleNotFoundError(
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"Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"
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)
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self.agent = agent
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self.file_upload_folder = file_upload_folder
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if self.file_upload_folder is not None:
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if not os.path.exists(file_upload_folder):
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os.mkdir(file_upload_folder)
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@@ -242,10 +285,14 @@ class GradioUI:
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sanitized_name = "".join(sanitized_name)
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# Save the uploaded file to the specified folder
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file_path = os.path.join(
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shutil.copy(file.name, file_path)
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return gr.Textbox(
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def log_user_message(self, text_input, file_uploads_log):
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return (
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@@ -262,6 +309,12 @@ class GradioUI:
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import gradio as gr
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with gr.Blocks(fill_height=True) as demo:
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stored_messages = gr.State([])
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file_uploads_log = gr.State([])
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chatbot = gr.Chatbot(
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@@ -277,7 +330,9 @@ class GradioUI:
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# If an upload folder is provided, enable the upload feature
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if self.file_upload_folder is not None:
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upload_file = gr.File(label="Upload a file")
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upload_status = gr.Textbox(
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upload_file.change(
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self.upload_file,
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[upload_file, file_uploads_log],
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@@ -293,4 +348,4 @@ class GradioUI:
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demo.launch(debug=True, share=True, **kwargs)
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__all__ = ["stream_to_gradio", "GradioUI"]
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import shutil
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from typing import Optional
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from smolagents.agent_types import (
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AgentAudio,
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AgentImage,
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AgentText,
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handle_agent_output_types,
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)
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from smolagents.agents import ActionStep, MultiStepAgent
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from smolagents.memory import MemoryStep
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from smolagents.utils import _is_package_available
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if isinstance(step_log, ActionStep):
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# Output the step number
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step_number = (
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f"Step {step_log.step_number}" if step_log.step_number is not None else ""
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)
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yield gr.ChatMessage(role="assistant", content=f"**{step_number}**")
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# First yield the thought/reasoning from the LLM
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# Clean up the LLM output
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model_output = step_log.model_output.strip()
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# Remove any trailing <end_code> and extra backticks, handling multiple possible formats
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model_output = re.sub(
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r"```\s*<end_code>", "```", model_output
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) # handles ```<end_code>
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model_output = re.sub(
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r"<end_code>\s*```", "```", model_output
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) # handles <end_code>```
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model_output = re.sub(
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r"```\s*\n\s*<end_code>", "```", model_output
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) # handles ```\n<end_code>
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model_output = model_output.strip()
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yield gr.ChatMessage(role="assistant", content=model_output)
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if used_code:
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# Clean up the content by removing any end code tags
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content = re.sub(
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r"```.*?\n", "", content
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) # Remove existing code blocks
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content = re.sub(
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r"\s*<end_code>\s*", "", content
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) # Remove end_code tags
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content = content.strip()
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if not content.startswith("```python"):
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content = f"```python\n{content}\n```"
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yield gr.ChatMessage(
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role="assistant",
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content=f"{log_content}",
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metadata={
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"title": "📝 Execution Logs",
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"parent_id": parent_id,
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"status": "done",
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},
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)
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# Nesting any errors under the tool call
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yield gr.ChatMessage(
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role="assistant",
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content=str(step_log.error),
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metadata={
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"title": "💥 Error",
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"parent_id": parent_id,
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"status": "done",
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},
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)
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# Update parent message metadata to done status without yielding a new message
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# Handle standalone errors but not from tool calls
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elif hasattr(step_log, "error") and step_log.error is not None:
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yield gr.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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# Calculate duration and token information
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step_footnote = f"{step_number}"
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if hasattr(step_log, "input_token_count") and hasattr(
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step_log, "output_token_count"
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):
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token_str = f" | Input-tokens:{step_log.input_token_count:,} | Output-tokens:{step_log.output_token_count:,}"
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step_footnote += token_str
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if hasattr(step_log, "duration"):
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step_duration = (
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f" | Duration: {round(float(step_log.duration), 2)}"
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if step_log.duration
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else None
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)
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step_footnote += step_duration
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step_footnote = f"""<span style="color: #bbbbc2; font-size: 12px;">{step_footnote}</span> """
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yield gr.ChatMessage(role="assistant", content=f"{step_footnote}")
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total_input_tokens = 0
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total_output_tokens = 0
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for step_log in agent.run(
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task, stream=True, reset=reset_agent_memory, additional_args=additional_args
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):
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# Track tokens if model provides them
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if hasattr(agent.model, "last_input_token_count"):
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total_input_tokens += agent.model.last_input_token_count
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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 gr.ChatMessage(
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role="assistant", content=f"**Final answer:** {str(final_answer)}"
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)
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class GradioUI:
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"""A one-line interface to launch your agent in Gradio"""
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def __init__(
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self,
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agent: MultiStepAgent,
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file_upload_folder: str | None = None,
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description: str | None = None, # Add description parameter
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):
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if not _is_package_available("gradio"):
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raise ModuleNotFoundError(
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"Please install 'gradio' extra to use the GradioUI: `pip install 'smolagents[gradio]'`"
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)
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self.agent = agent
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self.file_upload_folder = file_upload_folder
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self.description = description # Store description
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if self.file_upload_folder is not None:
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if not os.path.exists(file_upload_folder):
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os.mkdir(file_upload_folder)
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sanitized_name = "".join(sanitized_name)
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# Save the uploaded file to the specified folder
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file_path = os.path.join(
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self.file_upload_folder, os.path.basename(sanitized_name)
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)
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shutil.copy(file.name, file_path)
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return gr.Textbox(
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f"File uploaded: {file_path}", visible=True
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), file_uploads_log + [file_path]
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def log_user_message(self, text_input, file_uploads_log):
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return (
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import gradio as gr
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with gr.Blocks(fill_height=True) as demo:
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# Add welcome message at the top
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if self.description: # Use self.description instead of agent.description
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gr.Markdown(self.description)
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elif self.agent.description: # Fallback to agent.description if available
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gr.Markdown(self.agent.description)
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stored_messages = gr.State([])
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file_uploads_log = gr.State([])
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chatbot = gr.Chatbot(
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# If an upload folder is provided, enable the upload feature
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if self.file_upload_folder is not None:
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upload_file = gr.File(label="Upload a file")
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upload_status = gr.Textbox(
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label="Upload Status", interactive=False, visible=False
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)
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upload_file.change(
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self.upload_file,
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[upload_file, file_uploads_log],
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demo.launch(debug=True, share=True, **kwargs)
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__all__ = ["stream_to_gradio", "GradioUI"]
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app.py
CHANGED
@@ -74,6 +74,17 @@ image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_co
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with open("prompts.yaml", "r") as stream:
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prompt_templates = yaml.safe_load(stream)
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agent = CodeAgent(
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model=model,
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tools=[
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grammar=None,
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planning_interval=None,
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name=None,
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description=
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prompt_templates=prompt_templates,
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)
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GradioUI(agent).launch()
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with open("prompts.yaml", "r") as stream:
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prompt_templates = yaml.safe_load(stream)
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welcome_message = """
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## Welcome!
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I can help you with:
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- Getting cryptocurrency prices (e.g. "What's the current price of bitcoin?")
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- Searching the web for information
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- Generating images from text descriptions
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- General knowledge questions
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- And more!
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"""
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agent = CodeAgent(
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model=model,
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tools=[
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grammar=None,
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planning_interval=None,
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name=None,
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description=welcome_message,
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prompt_templates=prompt_templates,
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)
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GradioUI(agent, description=welcome_message).launch()
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