Gradio_UI.py

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):
        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}**")

        if hasattr(step_log, "model_output") and step_log.model_output is not None:
            model_output = step_log.model_output.strip()
            model_output = re.sub(r"```\s*<end_code>", "```", model_output)
            model_output = re.sub(r"<end_code>\s*```", "```", model_output)
            model_output = re.sub(r"```\s*\n\s*<end_code>", "```", model_output)
            model_output = model_output.strip()
            yield gr.ChatMessage(role="assistant", content=model_output)

        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)}"

            args = first_tool_call.arguments
            if isinstance(args, dict):
                content = str(args.get("answer", str(args)))
            else:
                content = str(args).strip()

            if used_code:
                content = re.sub(r"```.*?\n", "", content)
                content = re.sub(r"\s*<end_code>\s*", "", content)
                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

            if hasattr(step_log, "observations") and (step_log.observations is not None and step_log.observations.strip()):
                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"},
                    )

            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"},
                )

            parent_message_tool.metadata["status"] = "done"

        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"})

        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):
    """Stream agent responses to Gradio interface"""
    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):
        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
    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:
    """Custom Gradio interface for the agent with specialized tools"""
    
    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.makedirs(file_upload_folder, exist_ok=True)

    def launch(self, **kwargs):
        import gradio as gr
        
        with gr.Blocks(title="Multi-Tool AI Assistant", theme=gr.themes.Soft(), fill_height=True) as demo:
            # Header with capabilities overview
            gr.Markdown("""
            # 🛠️ Multi-Tool AI Assistant
            
            This assistant specializes in:
            - **Time zone conversions** (e.g., "What time is 3pm EST in Tokyo?")
            - **Weather lookups** (e.g., "What's the weather in Paris?")
            - **Unit conversions** (e.g., "Convert 50 miles to kilometers")
            - **Web search** (e.g., "Find recent news about AI")
            - **Image generation** (e.g., "Create an image of a futuristic city")
            - **Code execution** (e.g., "Calculate factorial of 5")
            """)
            
            # State management
            stored_messages = gr.State([])
            file_uploads_log = gr.State([])
            
            # Chat interface
            with gr.Row():
                chatbot = gr.Chatbot(
                    label="Conversation",
                    avatar_images=(
                        None,
                        "https://huggingface.co/datasets/agents-course/course-images/resolve/main/en/communication/Alfred.png",
                    ),
                    height=500,
                    render=True,
                    bubble_full_width=False
                )
            
            # File upload and input section
            with gr.Row():
                if self.file_upload_folder is not None:
                    with gr.Column(scale=1):
                        upload_file = gr.File(
                            label="Upload documents (PDF, DOCX, TXT)",
                            file_types=[".pdf", ".docx", ".txt"],
                            height=100
                        )
                        upload_status = gr.Textbox(
                            label="Upload Status",
                            interactive=False,
                            visible=False
                        )
                
                with gr.Column(scale=4):
                    text_input = gr.Textbox(
                        placeholder="Type your question or request here...",
                        label="Your message",
                        lines=2,
                        max_lines=5,
                        container=False
                    )
            
            # Control buttons
            with gr.Row():
                submit_btn = gr.Button("Send", variant="primary")
                clear_btn = gr.Button("Clear Chat")
            
            # Event handlers
            upload_file.change(
                self.upload_file,
                [upload_file, file_uploads_log],
                [upload_status, file_uploads_log],
            )
            
            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]
            )
            
            submit_btn.click(
                self.log_user_message,
                [text_input, file_uploads_log],
                [stored_messages, text_input],
            ).then(
                self.interact_with_agent,
                [stored_messages, chatbot],
                [chatbot]
            )
            
            clear_btn.click(
                lambda: (None, [], []),
                outputs=[chatbot, stored_messages, file_uploads_log]
            )

        demo.launch(**kwargs)

    def upload_file(self, file, file_uploads_log, allowed_file_types=[
            "application/pdf", 
            "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
            "text/plain"]):
        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

        original_name = os.path.basename(file.name)
        sanitized_name = re.sub(r"[^\w\-.]", "_", original_name)

        type_to_ext = {}
        for ext, t in mimetypes.types_map.items():
            if t not in type_to_ext:
                type_to_ext[t] = ext

        sanitized_name = sanitized_name.split(".")[:-1]
        sanitized_name.append("" + type_to_ext[mime_type])
        sanitized_name = "".join(sanitized_name)

        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 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