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Update Gradio_UI.py
Browse files- Gradio_UI.py +21 -132
Gradio_UI.py
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#!/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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import mimetypes
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import os
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import re
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from typing import Optional
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from smolagents.agent_types import AgentAudio, AgentImage, AgentText, handle_agent_output_types
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from smolagents.agents import ActionStep
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from smolagents.memory import MemoryStep
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from smolagents.utils import _is_package_available
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def pull_messages_from_step(step_log: MemoryStep):
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import gradio as gr
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if isinstance(step_log, ActionStep):
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step_number = f"Step {step_log.step_number}" if step_log.step_number is not None else ""
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yield gr.ChatMessage(role="assistant", content=f"**{step_number}**")
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if hasattr(step_log, "model_output") and step_log.model_output is not None:
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model_output = step_log.model_output.strip()
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model_output = re.sub(r"```\s*<end_code>", "```", model_output)
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model_output = re.sub(r"<end_code>\s*```", "```", model_output)
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model_output = re.sub(r"```\s*\n\s*<end_code>", "```", model_output)
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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 hasattr(step_log, "tool_calls") and step_log.tool_calls is not None:
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first_tool_call = step_log.tool_calls[0]
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used_code = first_tool_call.name == "python_interpreter"
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parent_id = f"call_{len(step_log.tool_calls)}"
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args = first_tool_call.arguments
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content = str(args.get("answer", str(args))) if isinstance(args, dict) else str(args).strip()
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if used_code:
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content = re.sub(r"```.*?\n", "", content)
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content = re.sub(r"\s*<end_code>\s*", "", 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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parent_message_tool = gr.ChatMessage(
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role="assistant",
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content=content,
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metadata={"title": f"🛠️ Used tool {first_tool_call.name}", "id": parent_id, "status": "pending"},
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)
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yield parent_message_tool
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if hasattr(step_log, "observations") and step_log.observations and step_log.observations.strip():
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log_content = step_log.observations.strip()
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log_content = re.sub(r"^Execution logs:\s*", "", log_content)
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yield gr.ChatMessage(
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role="assistant",
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content=log_content,
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metadata={"title": "📝 Execution Logs", "parent_id": parent_id, "status": "done"},
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)
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if 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", "parent_id": parent_id, "status": "done"},
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)
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parent_message_tool.metadata["status"] = "done"
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elif hasattr(step_log, "error") and step_log.error is not None:
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yield gr.ChatMessage(role="assistant", content=str(step_log.error), metadata={"title": "💥 Error"})
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step_footnote = f"{step_number}"
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if hasattr(step_log, "input_token_count") and hasattr(step_log, "output_token_count"):
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token_str = (
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f" | Input-tokens:{step_log.input_token_count:,} | Output-tokens:{step_log.output_token_count:,}"
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)
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step_footnote += token_str
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if hasattr(step_log, "duration"):
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duration = round(float(step_log.duration), 2)
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step_footnote += f" | Duration: {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=step_footnote)
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yield gr.ChatMessage(role="assistant", content="-----")
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def stream_to_gradio(agent, task: str, reset_agent_memory: bool = False, additional_args: Optional[dict] = None):
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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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import gradio as gr
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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(task, stream=True, reset=reset_agent_memory, additional_args=additional_args):
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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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total_output_tokens += agent.model.last_output_token_count
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if isinstance(step_log, ActionStep):
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step_log.input_token_count = agent.model.last_input_token_count
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step_log.output_token_count = agent.model.last_output_token_count
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for message in pull_messages_from_step(step_log):
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yield message
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final_answer = handle_agent_output_types(step_log)
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import gradio as gr
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if isinstance(final_answer, AgentText):
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yield gr.ChatMessage(role="assistant", content=f"**Final answer:**\n{final_answer.to_string()}\n")
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elif isinstance(final_answer, AgentImage):
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yield gr.ChatMessage(role="assistant", content={"path": final_answer.to_string(), "mime_type": "image/png"})
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elif isinstance(final_answer, AgentAudio):
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yield gr.ChatMessage(role="assistant", content={"path": final_answer.to_string(), "mime_type": "audio/wav"})
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else:
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yield gr.ChatMessage(role="assistant", content=f"**Final answer:** {str(final_answer)}")
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class GradioUI:
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def __init__(self, agent, file_upload_folder: str = "./uploads"):
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self.agent = agent
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file_uploads_log.append(file_path)
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return gr.Textbox(f"File uploaded: {sanitized_name}", visible=True), file_uploads_log
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class GradioUI:
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def __init__(self, agent, file_upload_folder: str = "./uploads"):
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self.agent = agent
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file_uploads_log.append(file_path)
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return gr.Textbox(f"File uploaded: {sanitized_name}", visible=True), file_uploads_log
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def build_ui(self):
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import gradio as gr
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chatbot = gr.Chatbot()
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msg = gr.Textbox(placeholder="Ask something...", label="Your message")
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clear = gr.Button("Clear")
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file_uploads_log = []
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with gr.Blocks() as demo:
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with gr.Row():
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with gr.Column():
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chatbot.render()
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msg.render()
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clear.render()
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msg.submit(self.interact_with_agent, [msg, chatbot], chatbot)
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clear.click(lambda: None, None, chatbot, queue=False)
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return demo
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