#!/usr/bin/env python
# coding=utf-8

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:
                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"<span style='color: #bbbbc2; font-size: 12px;'>{step_number}</span> "
        yield gr.ChatMessage(role="assistant", content=f"{step_footnote}\n-----")

def stream_to_gradio(agent, task: str, reset_agent_memory: bool = False, additional_args: Optional[dict] = None):
    import gradio as gr
    for step_log in agent.run(task, stream=True, reset=reset_agent_memory, additional_args=additional_args):
        for message in pull_messages_from_step(step_log):
            yield message

    final_answer = handle_agent_output_types(step_log)

    if isinstance(final_answer, AgentText):
        yield gr.ChatMessage(role="assistant", content=f"**Final answer:**\n{final_answer.to_string()}")
    elif isinstance(final_answer, AgentImage):
        yield gr.ChatMessage(role="assistant", content=gr.Image(final_answer.to_raw()))
    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"**** {str(final_answer)}")

class GradioUI:
    def __init__(self, agent: MultiStepAgent, file_upload_folder: str | None = None):
        self.agent = agent
        self.file_upload_folder = file_upload_folder
        if self.file_upload_folder and 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):
            messages.append(msg)
            yield messages

    def log_user_message(self, text_input, file_uploads_log):
        return text_input + (f"\nFiles: {file_uploads_log}" if file_uploads_log 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", resizeable=True, scale=1)
            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, **kwargs)

__all__ = ["stream_to_gradio", "GradioUI"]