app.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The Footscray Coding Collective. All rights reserved.
import mimetypes
import os
import re
import shutil
from typing import Optional

from dotenv import load_dotenv
from huggingface_hub import login
import gradio as gr

from scripts.text_inspector_tool import TextInspectorTool
from scripts.text_web_browser import (
    ArchiveSearchTool,
    FinderTool,
    FindNextTool,
    PageDownTool,
    PageUpTool,
    SimpleTextBrowser,
    VisitTool,
)
from scripts.visual_qa import visualizer
from scripts.frontmatter_tool import FrontmatterGeneratorTool
from scripts.text_cleaner_tool import TextCleanerTool
from scripts.document_tool import LegalDocumentTool

from smolagents import (
    CodeAgent,
    HfApiModel,
    LiteLLMModel,
    OpenAIServerModel,
    TransformersModel,
    GoogleSearchTool,
    Tool,
)
from smolagents.agent_types import AgentText, AgentImage, AgentAudio
from smolagents.gradio_ui import pull_messages_from_step, handle_agent_output_types

# ------------------------ Configuration and Setup ------------------------
# Constants and configurations
AUTHORIZED_IMPORTS = [
    "requests",  # Web requests (fetching data from the internet)
    "zipfile",  # Working with ZIP archives
    "pandas",  # Data manipulation and analysis (DataFrames)
    "numpy",  # Numerical computing (arrays, linear algebra)
    "sympy",  # Symbolic mathematics (algebra, calculus)
    "json",  # JSON data serialization/deserialization
    "bs4",  # Beautiful Soup for HTML/XML parsing
    "pubchempy",  # Accessing PubChem chemical database
    "yaml",
    "xml",  # XML processing
    "yahoo_finance",  # Fetching stock data
    "Bio",  # Bioinformatics tools (e.g., sequence analysis)
    "sklearn",  # Scikit-learn for machine learning
    "scipy",  # Scientific computing (stats, optimization)
    "pydub",  # Audio manipulation
    "PIL",  # Pillow for image processing
    "chess",  # Chess-related functionality
    "PyPDF2",  # PDF manipulation
    "pptx",  # PowerPoint file manipulation
    "torch",  # PyTorch for neural networks
    "datetime",  # Date and time handling
    "fractions",  # Rational number arithmetic
    "csv",  # CSV file reading/writing
    "cleantext",  # Text cleaning and normalization
    "os",  # Operating system interaction (file system, etc.) VERY IMPORTANT
    "re",  # Regular expressions for text processing
    "collections",  # Useful data structures (e.g., defaultdict, Counter)
    "math",  # Basic mathematical functions
    "random",  # Random number generation
    "io",  # Input/output streams
    "urllib.parse",  # URL parsing and manipulation (safe URL handling)
    "typing",  # Support for type hints (improve code clarity)
    "concurrent.futures",  # For parallel execution
    "time",  # Measuring time
    "tempfile",  # Creating temporary files and directories
    # Data Visualization (if needed) - Consider security implications carefully
    "matplotlib",  # Plotting library (basic charts)
    "seaborn",  # Statistical data visualization (more advanced)
    # Web Scraping (more specific/controlled) - Consider ethical implications
    "lxml",  # Faster XML/HTML processing (alternative to bs4)
    "selenium",  # Automated browser control (for dynamic websites)
    # Database interaction (if needed) - Handle credentials securely!
    "sqlite3",  # SQLite database access
    # Task scheduling
    "schedule",  # Allow the agent to schedule tasks
]

USER_AGENT = (
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
    "(KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0"
)
BROWSER_CONFIG = {
    "viewport_size": 1024 * 5,
    "downloads_folder": "downloads_folder",
    "request_kwargs": {
        "headers": {"User-Agent": USER_AGENT},
        "timeout": 300,
    },
    "serpapi_key": os.getenv("SERPAPI_API_KEY"),
}

CUSTOM_ROLE_CONVERSIONS = {"tool-call": "assistant", "tool-response": "user"}


ALLOWED_FILE_TYPES = [
    "application/pdf",
    "application/vnd.openxmlformats-officedocument.wordprocessingml.document",
    "text/plain",
    "text/markdown",
    "application/json",
    "image/png",
    "image/webp",
    "image/jpeg",
    "image/gif",
    "video/mp4",
    "audio/mpeg",
    "audio/wav",
    "audio/ogg",
]


def setup_environment():
    """Initialize environment variables and authentication."""
    load_dotenv(override=True)
    if os.getenv("HF_TOKEN"):  # Check if token is actually set
        login(os.getenv("HF_TOKEN"))
        print("HF_TOKEN (last 10 characters):", os.getenv("HF_TOKEN")[-10:])
    else:
        print("HF_TOKEN not found in environment variables.")


# ------------------------ Model and Tool Management ------------------------
class ModelManager:
    """Manages model loading and initialization."""

    @staticmethod
    def load_model(chosen_inference: str, model_id: str, key_manager=None):
        """Load the specified model with appropriate configuration."""
        try:
            if chosen_inference == "hf_api":
                return HfApiModel(model_id=model_id)

            elif chosen_inference == "hf_api_provider":
                return HfApiModel(provider="together")

            elif chosen_inference == "litellm":
                return LiteLLMModel(model_id=model_id)

            elif chosen_inference == "openai":
                if not key_manager:
                    raise ValueError("Key manager required for OpenAI model")

                return OpenAIServerModel(
                    model_id=model_id, api_key=key_manager.get_key("openai_api_key")
                )

            elif chosen_inference == "transformers":
                return TransformersModel(
                    model_id="HuggingFaceTB/SmolLM2-1.7B-Instruct",
                    device_map="auto",
                    max_new_tokens=1000,
                )

            else:
                raise ValueError(f"Invalid inference type: {chosen_inference}")

        except Exception as e:
            print(f"✗ Couldn't load model: {e}")
            raise


class ToolRegistry:
    """Manages tool initialization and organization."""

    @staticmethod
    def load_web_tools(model, browser, text_limit=20000):
        """Initialize and return web-related tools."""
        return [
            GoogleSearchTool(provider="serper"),
            VisitTool(browser),
            PageUpTool(browser),
            PageDownTool(browser),
            FinderTool(browser),
            FindNextTool(browser),
            ArchiveSearchTool(browser),
            TextInspectorTool(model, text_limit),
        ]

    @staticmethod
    def load_document_tools():
        """
        Initialize and return document processing, i.e. sanitisation and indexing, tools.
        Returns:
            List of document tools
        """
        return [
            TextCleanerTool(),
            LegalDocumentTool(),
        ]

    @staticmethod
    def load_image_generation_tools():
        """Initialize and return image generation tools."""
        try:
            return Tool.from_space(
                space_id="xkerser/FLUX.1-dev",
                name="image_generator",
                description="Generates high-quality AgentImage using the FLUX.1-dev model based on text prompts.",
            )
        except Exception as e:
            print(f"✗ Couldn't initialize image generation tool: {e}")
            raise


# ------------------------ Agent Creation and Execution ------------------------
def create_agent():
    """
    Creates a fresh agent instance with properly configured tools.
    Returns:
        CodeAgent: Configured agent ready for use
    Raises:
        ValueError: If tool validation fails
        RuntimeError: If agent creation fails
    """
    try:
        # Initialize model
        model = LiteLLMModel(
            custom_role_conversions=CUSTOM_ROLE_CONVERSIONS,
            model_id="openrouter/deepseek/deepseek-v3-base:free",
        )

        # Initialize tools
        text_limit = 30000
        browser = SimpleTextBrowser(**BROWSER_CONFIG)

        # Collect all tools in a single list
        web_tools = ToolRegistry.load_web_tools(model, browser, text_limit)
        doc_tools = ToolRegistry.load_document_tools()  # New document tools
        image_generator = ToolRegistry.load_image_generation_tools()

        # Combine all tools into a single list
        all_tools = [visualizer] + web_tools + doc_tools + [image_generator]

        # Validate tools before creating agent
        for tool in all_tools:
            if not isinstance(tool, Tool):
                raise ValueError(
                    f"Invalid tool type: {type(tool)}. "
                    f"All tools must be instances of Tool class."
                )

        return CodeAgent(
            model=model,
            tools=all_tools,
            max_steps=12,
            verbosity_level=2,
            additional_authorized_imports=AUTHORIZED_IMPORTS,
            planning_interval=2,
        )
    except (ValueError, RuntimeError) as e:
        print(f"Failed to create agent: {e}")
        raise RuntimeError(f"Agent creation failed: {e}")


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 messages as Gradio ChatMessages."""
    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

    # Process final answer : Use a more comprehensive media output
    final_answer = step_log  # Last log is the run's final_answer
    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={"image": final_answer.to_string(), "type": "file"},
        )  # Send as Gradio-compatible file object:
    elif isinstance(final_answer, AgentAudio):
        yield gr.ChatMessage(
            role="assistant",
            content={"audio": final_answer.to_string(), "type": "file"},
        )  # Send as Gradio-compatible file object
    else:
        yield gr.ChatMessage(
            role="assistant", content=f"**Final answer:** {str(final_answer)}"
        )


# ------------------------ Gradio UI Components ------------------------
class GradioUI:
    """A one-line interface to launch your agent in Gradio."""

    def __init__(self, file_upload_folder: str | None = None):
        """Initialize the Gradio UI with optional file upload functionality."""
        self.file_upload_folder = file_upload_folder

        if self.file_upload_folder is not None:
            if not os.path.exists(file_upload_folder):
                os.mkdir(file_upload_folder)

    def interact_with_agent(self, prompt, messages, session_state):
        """Main interaction handler with the agent."""

        # Get or create session-specific agent
        if "agent" not in session_state:
            session_state["agent"] = create_agent()

        # Adding monitoring
        try:
            # Log the existence of agent memory
            has_memory = hasattr(session_state["agent"], "memory")
            print(f"Agent has memory: {has_memory}")
            if has_memory:
                print(f"Memory type: {type(session_state['agent'].memory)}")

            messages.append(gr.ChatMessage(role="user", content=prompt))
            yield messages

            for msg in stream_to_gradio(
                session_state["agent"], task=prompt, reset_agent_memory=False
            ):
                messages.append(msg)
                yield messages  # Yield messages after each step
            yield messages  # Yield messages one last time

        except Exception as e:
            print(f"Error in interaction: {str(e)}")
            raise

    def upload_file(
        self,
        file,
        file_uploads_log,
    ):
        """Handle file uploads with proper validation and security."""
        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

        # Sanitize file name
        original_name = os.path.basename(file.name)
        sanitized_name = re.sub(
            r"[^\w\-.]", "_", original_name
        )  # Replace invalid chars with underscores

        # Ensure the extension correlates to the mime type
        type_to_ext = {}
        for ext, t in mimetypes.types_map.items():
            if t not in type_to_ext:
                type_to_ext[t] = ext

        # Build sanitized filename with proper extension
        name_parts = sanitized_name.split(".")[:-1]
        extension = type_to_ext.get(mime_type, "")
        sanitized_name = "".join(name_parts) + extension

        # Limit File Size, and Throw Error
        max_file_size_mb = 50  # Define the limit
        file_size_mb = os.path.getsize(file.name) / (1024 * 1024)  # Size in MB

        if file_size_mb > max_file_size_mb:
            return (
                gr.Textbox(
                    f"File size exceeds {max_file_size_mb} MB limit.", visible=True
                ),
                file_uploads_log,
            )

        # Save the uploaded file to the specified folder
        file_path = os.path.join(self.file_upload_folder, 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):
        """Process user message and handle file references."""
        message = text_input

        if len(file_uploads_log) > 0:
            message += f"\nYou have been provided with these files, which might be helpful or not: {file_uploads_log}"  # Added file list

        return (
            message,
            gr.Textbox(
                value="",
                interactive=False,
                placeholder="Processing...",  # Changed placeholder.
            ),
            gr.Button(interactive=False),
        )

    def detect_device(self, request: gr.Request):
        """Detect whether the user is on mobile or desktop device."""
        if not request:
            return "Unknown device"  # Handle case where request is none.

        # Method 1: Check sec-ch-ua-mobile header
        is_mobile_header = request.headers.get("sec-ch-ua-mobile")
        if is_mobile_header:
            return "Mobile" if "?1" in is_mobile_header else "Desktop"

        # Method 2: Check user-agent string
        user_agent = request.headers.get("user-agent", "").lower()
        mobile_keywords = ["android", "iphone", "ipad", "mobile", "phone"]

        if any(keyword in user_agent for keyword in mobile_keywords):
            return "Mobile"

        # Method 3: Check platform
        platform = request.headers.get("sec-ch-ua-platform", "").lower()
        if platform:
            if platform in ['"android"', '"ios"']:
                return "Mobile"
            if platform in ['"windows"', '"macos"', '"linux"']:
                return "Desktop"

        # Default case if no clear indicators
        return "Desktop"

    def launch(self, **kwargs):
        """Launch the Gradio UI with responsive layout."""
        with gr.Blocks(theme="ocean", fill_height=True) as demo:
            # Different layouts for mobile and computer devices
            @gr.render()
            def layout(request: gr.Request):
                device = self.detect_device(request)
                print(f"device - {device}")
                # Render layout with sidebar
                if device == "Desktop":
                    return self._create_desktop_layout()
                return self._create_mobile_layout()

        demo.queue(max_size=20).launch(
            debug=True, **kwargs
        )  # Add queue with reasonable size

    def _create_desktop_layout(self):
        """Create the desktop layout with sidebar."""
        with gr.Blocks(fill_height=True) as sidebar_demo:
            with gr.Sidebar():
                gr.Markdown(
                    """#OpenDeepResearch - 3theSmolagents!
                Model_id: google/gemini-2.0-flash-001"""
                )
                with gr.Group():
                    gr.Markdown("**What's on your mind mate?**", container=True)
                    text_input = gr.Textbox(
                        lines=3,
                        label="Your request",
                        container=False,
                        placeholder="Enter your prompt here and press Shift+Enter or press the button",
                    )
                    launch_research_btn = gr.Button("Run", variant="primary")

                # If an upload folder is provided, enable the upload feature
                if self.file_upload_folder is not None:
                    upload_file = gr.File(label="Upload a file")
                    upload_status = gr.Textbox(
                        label="Upload Status", interactive=False, visible=False
                    )
                    file_uploads_log = gr.State([])
                    upload_file.change(
                        self.upload_file,
                        [upload_file, file_uploads_log],
                        [upload_status, file_uploads_log],
                    )

                gr.HTML("<br><br><h4><center>Powered by:</center></h4>")
                with gr.Row():
                    gr.HTML(
                        """
                    <div style="display: flex; align-items: center; gap: 8px; font-family: system-ui, -apple-system, sans-serif;">
                    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolagents/mascot_smol.png"
                         style="width: 32px; height: 32px; object-fit: contain;" alt="logo">
                    <a target="_blank" href="https://github.com/huggingface/smolagents">
                        <b>huggingface/smolagents</b>
                    </a>
                    </div>
                    """
                    )

            # Add session state to store session-specific data
            session_state = gr.State({})  # Initialize empty state for each session
            stored_messages = gr.State([])
            if "file_uploads_log" not in locals():
                file_uploads_log = gr.State([])

            chatbot = gr.Chatbot(
                label="open-Deep-Research",
                type="messages",
                avatar_images=(
                    None,
                    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolagents/mascot_smol.png",
                ),
                resizeable=False,
                scale=1,
                elem_id="my-chatbot",
            )

            self._connect_event_handlers(
                text_input,
                launch_research_btn,
                file_uploads_log,
                stored_messages,
                chatbot,
                session_state,
            )

            return sidebar_demo

    def _create_mobile_layout(self):
        """Create the mobile layout (simpler without sidebar)."""
        with gr.Blocks(fill_height=True) as simple_demo:
            gr.Markdown("""#OpenDeepResearch - free the AI agents!""")
            # Add session state to store session-specific data
            session_state = gr.State({})
            stored_messages = gr.State([])
            file_uploads_log = gr.State([])

            chatbot = gr.Chatbot(
                label="open-Deep-Research",
                type="messages",
                avatar_images=(
                    None,
                    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolagents/mascot_smol.png",
                ),
                resizeable=True,
                scale=1,
            )

            # If an upload folder is provided, enable the upload feature
            if self.file_upload_folder is not None:
                upload_file = gr.File(label="Upload a file")
                upload_status = gr.Textbox(
                    label="Upload Status", interactive=False, visible=False
                )
                upload_file.change(
                    self.upload_file,
                    [upload_file, file_uploads_log],
                    [upload_status, file_uploads_log],
                )

            text_input = gr.Textbox(
                lines=1,
                label="What's on your mind mate?",
                placeholder="Chuck in a question and we'll take care of the rest",
            )
            launch_research_btn = gr.Button("Run", variant="primary")

            self._connect_event_handlers(
                text_input,
                launch_research_btn,
                file_uploads_log,
                stored_messages,
                chatbot,
                session_state,
            )

            return simple_demo

    def _connect_event_handlers(
        self,
        text_input,
        launch_research_btn,
        file_uploads_log,
        stored_messages,
        chatbot,
        session_state,
    ):
        """Connect the event handlers for input elements."""
        # Connect text input submit event
        text_input.submit(
            self.log_user_message,
            [text_input, file_uploads_log],
            [stored_messages, text_input, launch_research_btn],
        ).then(
            self.interact_with_agent,
            [stored_messages, chatbot, session_state],
            [chatbot],
        ).then(
            lambda: (
                gr.Textbox(
                    interactive=True,
                    placeholder="Enter your prompt here and press the button",
                ),
                gr.Button(interactive=True),
            ),
            None,
            [text_input, launch_research_btn],
        )

        # Connect button click event
        launch_research_btn.click(
            self.log_user_message,
            [text_input, file_uploads_log],
            [stored_messages, text_input, launch_research_btn],
        ).then(
            self.interact_with_agent,
            [stored_messages, chatbot, session_state],
            [chatbot],
        ).then(
            lambda: (
                gr.Textbox(
                    interactive=True,
                    placeholder="Enter your prompt here and press the button",
                ),
                gr.Button(interactive=True),
            ),
            None,
            [text_input, launch_research_btn],
        )


# ------------------------ Execution ------------------------
def main():
    """Main entry point for the application."""
    # Initialize environment
    setup_environment()

    # Ensure downloads folder exists
    os.makedirs(f"./{BROWSER_CONFIG['downloads_folder']}", exist_ok=True)

    # Launch UI
    GradioUI(file_upload_folder="uploaded_files").launch()


if __name__ == "__main__":
    main()