Gradio_UI.py

#!/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 # Ensure MultiStepAgent is correctly referenced
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)}_{step_log.step_number or 'x'}" # Make parent_id more unique

            # Tool call becomes the parent message with timing info
            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)  # 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"): # Ensure it's a python block
                    content = f"```python\n{content}\n```"
                else: # If it is, ensure newlines are correct
                    content = content.replace("```python", "```python\n").replace("\n```", "\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: # Only yield if there's actual 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"},
                )
            # This direct update might not work as expected as yield creates new objects.
            # Status update is visual; actual logic might be more complex.
            parent_message_tool.metadata["status"] = "done" 

        elif hasattr(step_log, "error") and step_log.error is not None: # Standalone errors
            yield gr.ChatMessage(role="assistant", content=str(step_log.error), metadata={"title": "💥 Error"})

        step_footnote_parts = [step_number]
        if hasattr(step_log, "input_token_count") and step_log.input_token_count is not None and \
           hasattr(step_log, "output_token_count") and step_log.output_token_count is not None:
            token_str = (
                f" | Input-tokens:{step_log.input_token_count:,} | Output-tokens:{step_log.output_token_count:,}"
            )
            step_footnote_parts.append(token_str)
        if hasattr(step_log, "duration") and step_log.duration is not None:
            step_duration = f" | Duration: {round(float(step_log.duration), 2)}s"
            step_footnote_parts.append(step_duration)
        
        step_footnote_text = "".join(filter(None, step_footnote_parts))
        if step_footnote_text:
             step_footnote = f"""<span style="color: #bbbbc2; font-size: 12px;">{step_footnote_text}</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

    # Reset interaction logs for the new run if the agent has this attribute
    if hasattr(agent, 'interaction_logs'):
        agent.interaction_logs.clear()
        print("DEBUG: Cleared agent interaction_logs for new run.")


    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") and agent.model.last_input_token_count is not None: # Check for None
            if isinstance(step_log, ActionStep): # Only add token counts to ActionSteps
                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

    # After the loop, step_log holds the final answer or the last step's log
    final_answer_content = step_log 
    final_answer_processed = handle_agent_output_types(final_answer_content)

    if isinstance(final_answer_processed, AgentText):
        yield gr.ChatMessage(role="assistant", content=f"**Final answer:**\n{final_answer_processed.to_string()}\n")
    elif isinstance(final_answer_processed, AgentImage):
        yield gr.ChatMessage(role="assistant", content={"path": final_answer_processed.to_string(), "mime_type": "image/png"})
    elif isinstance(final_answer_processed, AgentAudio):
        yield gr.ChatMessage(role="assistant", content={"path": final_answer_processed.to_string(), "mime_type": "audio/wav"})
    else:
        yield gr.ChatMessage(role="assistant", content=f"**Final answer:** {str(final_answer_processed)}")


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.makedirs(self.file_upload_folder, exist_ok=True) # Use makedirs
        
        self._latest_file_path_for_download = None # For download button state

    def _check_for_created_file(self):
        """Helper function to check interaction logs for a created file path."""
        self._latest_file_path_for_download = None # Reset
        if hasattr(self.agent, 'interaction_logs') and self.agent.interaction_logs:
            print(f"DEBUG UI: Checking {len(self.agent.interaction_logs)} interaction log entries.")
            for log_entry in self.agent.interaction_logs:
                if log_entry.get("tool_name") == "create_document":
                    tool_output_value = log_entry.get("tool_output")
                    print(f"DEBUG UI: Log for 'create_document', output: {tool_output_value}")
                    if tool_output_value and isinstance(tool_output_value, str):
                        if not tool_output_value.strip().startswith("ERROR:"):
                            normalized_path = os.path.normpath(tool_output_value)
                            if os.path.exists(normalized_path):
                                self._latest_file_path_for_download = normalized_path
                                print(f"DEBUG UI: File path for download set: {self._latest_file_path_for_download}")
                                return True # Found a valid file
                            else:
                                print(f"DEBUG UI: Path from log ('{normalized_path}') does not exist.")
                        else:
                            print(f"DEBUG UI: 'create_document' tool reported error: {tool_output_value}")
        return False


    def interact_with_agent(self, prompt, messages_history, download_btn_state, file_output_state):
        import gradio as gr
        
        messages_history.append(gr.ChatMessage(role="user", content=prompt))
        yield messages_history, gr.update(visible=False), gr.update(value=None, visible=False) # Hide download items initially

        # Stream agent messages to chatbot
        for msg in stream_to_gradio(self.agent, task=prompt, reset_agent_memory=False):
            messages_history.append(msg)
            yield messages_history, gr.update(visible=False), gr.update(value=None, visible=False) # Keep hidden during streaming

        # After streaming all agent messages, check for created file
        file_found = self._check_for_created_file()
        
        # Update UI based on whether a file was found
        # Yielding final state for chatbot, download button, and file component
        yield messages_history, gr.update(visible=file_found), gr.update(value=None, visible=False)


    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)
            if mime_type is None: # Fallback if guess_type returns None
                mime_type = file.type # Gradio File object has a 'type' attribute
        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(f"File type '{mime_type}' disallowed", visible=True), file_uploads_log

        original_name = os.path.basename(file.name)
        sanitized_name = re.sub(r"[^\w\-.]", "_", original_name)
        
        # Ensure correct extension based on mime type, if possible
        base_name, current_ext = os.path.splitext(sanitized_name)
        
        type_to_ext_map = {
            "application/pdf": ".pdf",
            "application/vnd.openxmlformats-officedocument.wordprocessingml.document": ".docx",
            "text/plain": ".txt",
        }
        expected_ext = type_to_ext_map.get(mime_type)
        if expected_ext and current_ext.lower() != expected_ext:
            sanitized_name = base_name + expected_ext
        
        file_path = os.path.join(self.file_upload_folder, sanitized_name)
        shutil.copy(file.name, file_path) # file.name is the temp path of the uploaded file

        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):
        # This function prepares the prompt that goes to the agent.
        # It also clears the text_input box.
        full_prompt = text_input
        if file_uploads_log: # Check if list is not empty
            full_prompt += (
                f"\nYou have been provided with these files, which might be helpful or not: {file_uploads_log}"
            )
        return full_prompt, "" # Return the full prompt and an empty string to clear input

    def prepare_and_show_download_file(self):
        import gradio as gr
        if self._latest_file_path_for_download and os.path.exists(self._latest_file_path_for_download):
            print(f"DEBUG UI: Preparing download for UI: {self._latest_file_path_for_download}")
            return gr.File.update(value=self._latest_file_path_for_download, 
                                  label=os.path.basename(self._latest_file_path_for_download), 
                                  visible=True)
        else:
            print("DEBUG UI: No valid file path to prepare for download component.")
            gr.Warning("No file available for download or path is invalid.")
            return gr.File.update(visible=False)

    def launch(self, **kwargs):
        import gradio as gr

        with gr.Blocks(fill_height=True, theme=gr.themes.Soft()) as demo: # Added a theme
            # --- State Variables ---
            # stored_messages is used to build the prompt for the agent, not directly for chatbot display here.
            # The chatbot takes messages directly from interact_with_agent.
            # We'll use chat_history_state for the chatbot's message list.
            chat_history_state = gr.State([]) 
            file_uploads_log = gr.State([]) # Tracks paths of uploaded files

            # --- UI Layout ---
            gr.Markdown("# Smol Talk with your Agent") # Title

            with gr.Row():
                with gr.Column(scale=3): # Main chat area
                    chatbot = gr.Chatbot(
                        label="Agent Interaction",
                        # Bubble full width can make text hard to read, try default
                        # bubble_full_width=False, 
                        avatar_images=(
                            None, # User avatar
                            "https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo-round.png" # Agent avatar
                        ),
                        height=600 
                    )
                    text_input = gr.Textbox(
                        lines=1, 
                        label="Your Message to the Agent", 
                        placeholder="Type your message and press Enter..."
                    )
                
                with gr.Column(scale=1): # Sidebar for uploads and downloads
                    if self.file_upload_folder is not None:
                        gr.Markdown("### File Upload")
                        upload_file_component = gr.File(label="Upload a supporting file")
                        upload_status_display = gr.Textbox(label="Upload Status", interactive=False, visible=True, lines=2) # Make visible by default
                        upload_file_component.upload( # Use 'upload' event for gr.File
                            self.upload_file,
                            [upload_file_component, file_uploads_log],
                            [upload_status_display, file_uploads_log],
                        )
                    
                    gr.Markdown("### Generated File")
                    # This button becomes visible if a file is created by the agent
                    download_btn = gr.Button("Download Generated File", visible=False) 
                    # This gr.File component becomes visible and populated when the button above is clicked
                    file_output_display = gr.File(label="Downloadable Document", visible=False, interactive=False) 

            # --- Event Handling ---
            
            # When user submits text_input:
            # 1. log_user_message: prepares the prompt (text + file info), clears text_input.
            #    The output 'prepared_prompt' is then passed to interact_with_agent.
            # 2. interact_with_agent: streams agent's responses to chatbot, updates download button.
            
            # We need a state to hold the prepared prompt temporarily if log_user_message is separate
            prepared_prompt_state = gr.State("")

            text_input.submit(
                self.log_user_message,
                [text_input, file_uploads_log],
                [prepared_prompt_state, text_input] # prepared_prompt_state gets the full prompt, text_input is cleared
            ).then(
                self.interact_with_agent,
                [prepared_prompt_state, chat_history_state, download_btn, file_output_display], # Pass current UI states
                [chat_history_state, download_btn, file_output_display] # Update these UI states
            )

            # When download_btn is clicked:
            download_btn.click(
                self.prepare_and_show_download_file, 
                [], # No inputs needed from UI for this action
                [file_output_display] # Update the file_output_display component
            )

        # Launch the Gradio app
        # Set share=False if running locally or on Spaces where share=True might be an issue
        demo.launch(debug=True, share=kwargs.get("share", False), **kwargs)


__all__ = ["stream_to_gradio", "GradioUI"]