app.py

import os
import re
import shutil
import datetime
import mimetypes
from typing import Optional, List, Dict, Tuple  # More specific typing

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 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 ------------------------
AUTHORIZED_IMPORTS = [
    "requests",
    "zipfile",
    "pandas",
    "numpy",
    "sympy",
    "json",
    "bs4",
    "pubchempy",
    "yaml",
    "xml",
    "yahoo_finance",
    "Bio",
    "sklearn",
    "scipy",
    "pydub",
    "PIL",
    "chess",
    "PyPDF2",
    "pptx",
    "torch",
    "datetime",
    "fractions",
    "csv",
    "cleantext",
    "os",
    "re",
    "collections",
    "math",
    "random",
    "io",
    "urllib.parse",
    "typing",
    "concurrent.futures",
    "time",
    "tempfile",
    "matplotlib",
    "seaborn",
    "lxml",
    "selenium",
    "sqlite3",
    "schedule",
]

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 authenticate with Hugging Face Hub.
    """
    load_dotenv(override=True)
    hf_token = os.getenv("HF_TOKEN")  # Get token once
    if hf_token:
        login(hf_token)
        print("HF_TOKEN (last 10 characters):", 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.

        Args:
            chosen_inference (str): The type of inference to use (e.g., "hf_api", "openai").
            model_id (str): The ID of the model to load.
            key_manager:  (Optional) Key manager for API keys. Required for OpenAI.

        Returns:
            An instance of the specified model class.

        Raises:
            ValueError: If an invalid inference type is specified or if the key manager
                is missing for OpenAI models.
            Exception: If the model fails to load.
        """
        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) -> List[Tool]:
        """
        Initialize and return web-related tools.

        Args:
            model: The language model to use.
            browser: The web browser instance.
            text_limit (int): The maximum text length for the text inspector tool.

        Returns:
            A list of 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() -> List[Tool]:
        """
        Initialize and return document processing tools.

        Returns:
            List of document tools.
        """
        return [FrontmatterGeneratorTool(), TextCleanerTool()]

    @staticmethod
    def load_image_generation_tools() -> Optional[Tool]:
        """
        Initialize and return image generation tools.

        Returns:
            The image generation tool or None if initialization fails.
        """
        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}")
            return None


# ------------------------ Agent Creation and Execution ------------------------
def create_agent() -> CodeAgent:
    """
    Creates a fresh agent instance with 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-chat-v3-0324:free",
        )

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

        # Create tool instances with proper error handling
        web_tools = ToolRegistry.load_web_tools(model, browser, text_limit)
        doc_tools = []  # Initialize as empty list
        image_generator = None  # Initialize as None

        try:
            doc_tools = ToolRegistry.load_document_tools()
        except AssertionError as e:
            print(f"Warning: Error loading document tools: {str(e)}")
            print("Attempting to continue with available tools...")

        image_generator = ToolRegistry.load_image_generation_tools()

        # Combine available tools (filter out None values)
        all_tools = [visualizer] + web_tools + doc_tools
        if image_generator:  # Add only if it's not None
            all_tools.append(image_generator)

        # Log available tools
        print(f"Loaded {len(all_tools)} tools successfully")
        for tool in all_tools:
            print(f"- {tool.name}: {tool.description[:50]}...")

        return CodeAgent(
            model=model,
            tools=all_tools,
            max_steps=12,
            verbosity_level=2,
            additional_authorized_imports=AUTHORIZED_IMPORTS,
            planning_interval=4,
        )
    except Exception 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=None
):
    """Runs an agent with the given task and streams messages as Gradio ChatMessages."""
    try:
        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 with comprehensive media output
        final_answer = step_log  # Last log is the run's final_answer
        final_answer = handle_agent_output_types(final_answer)

        # Output handling based on type
        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"},
            )
        elif isinstance(final_answer, AgentAudio):
            yield gr.ChatMessage(
                role="assistant",
                content={"audio": final_answer.to_string(), "type": "file"},
            )
        else:
            yield gr.ChatMessage(
                role="assistant", content=f"Final answer: {str(final_answer)}"
            )
    except Exception as e:
        yield gr.ChatMessage(
            role="assistant",
            content=f"Error occurred during processing: {str(e)}\n\nPlease try again with a different query or check your inputs.",
        )


# ------------------------ 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:
            os.makedirs(self.file_upload_folder, exist_ok=True)

    def interact_with_agent(
        self, prompt: str, messages: List[Dict], session_state: Dict
    ) -> List[Dict]:  # Type hints
        """Main interaction handler with the agent."""

        # Get or create session-specific agent with cache persistence
        if "agent" not in session_state:
            try:
                session_state["agent"] = create_agent()
                session_state["creation_time"] = datetime.datetime.now()
                session_state["request_count"] = 0
            except Exception as e:
                messages.append(
                    gr.ChatMessage(
                        role="assistant",
                        content=f"Error initializing agent: {str(e)}\n\nPlease refresh the page and try again.",
                    )
                )
                yield messages
                return

        session_state["request_count"] += 1

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

        try:
            # Check if agent should be reset (e.g., if too many requests)
            reset_needed = session_state["request_count"] > 15

            for msg in stream_to_gradio(
                session_state["agent"], task=prompt, reset_agent_memory=reset_needed
            ):
                messages.append(msg)
                yield messages

            # If we reset the agent memory, update the request count
            if reset_needed:
                session_state["request_count"] = 1

        except Exception as e:
            messages.append(
                gr.ChatMessage(
                    role="assistant",
                    content=f"Error processing your request: {str(e)}\n\nPlease try again with a different query.",
                )
            )
            yield messages

    def upload_file(self, file, file_uploads_log):
        """Handle file uploads with validation, security, and clear feedback."""
        if file is None:
            return gr.Textbox("No file uploaded", visible=True), file_uploads_log

        try:
            # Get file size and check limit before processing
            file_size_mb = os.path.getsize(file.name) / (1024 * 1024)  # Size in MB
            max_file_size_mb = 50  # Define the limit

            if file_size_mb > max_file_size_mb:
                return (
                    gr.Textbox(
                        f"❌ File size ({file_size_mb:.1f} MB) exceeds {max_file_size_mb} MB limit.",
                        visible=True,
                    ),
                    file_uploads_log,
                )

            # Check MIME type
            mime_type, _ = mimetypes.guess_type(file.name)  # Correct unpacking
            if mime_type not in ALLOWED_FILE_TYPES:
                return (
                    gr.Textbox(
                        f"❌ File type '{mime_type or 'unknown'}' is not allowed. Supported types: {', '.join(t.split('/')[-1] for t in ALLOWED_FILE_TYPES)}",
                        visible=True,
                    ),
                    file_uploads_log,
                )

            # Sanitize file name with better pattern
            original_name = os.path.basename(file.name)
            sanitized_name = re.sub(r"[^\w\-.]", "", original_name)

            # Save the uploaded file
            file_path = os.path.join(self.file_upload_folder, sanitized_name)
            shutil.copy(file.name, file_path)

            return gr.Textbox(
                f"✓ File uploaded successfully: {os.path.basename(file_path)} ({file_size_mb:.1f} MB)",
                visible=True,
            ), file_uploads_log + [file_path]

        except Exception as e:
            return (
                gr.Textbox(f"❌ Upload error: {str(e)}", visible=True),
                file_uploads_log,
            )

    def log_user_message(self, text_input, file_uploads_log):
        """Process user message and handle file references with proper agent types."""
        message = text_input

        if len(file_uploads_log) > 0:
            # Group files by type for better agent processing
            file_info = {}
            for file_path in file_uploads_log:
                ext = os.path.splitext(file_path)[1].lower()
                if ext in [".jpg", ".jpeg", ".png", ".gif", ".webp"]:
                    category = "images"
                elif ext in [".mp3", ".wav", ".ogg"]:
                    category = "audio"
                else:
                    category = "documents"

                if category not in file_info:
                    file_info[category] = []
                file_info[category].append(os.path.basename(file_path))

            # Format file information for the agent
            file_message = "\nYou have been provided with these files:\n"
            for category, files in file_info.items():
                file_message += f"- {category.capitalize()}: {', '.join(files)}\n"

            message += file_message
            message += "\nUse inspect_file_as_text for documents, visualizer for images, and the appropriate tools for audio files."

        return (
            message,
            gr.Textbox(value="", interactive=False, placeholder="Processing..."),
            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 and enhanced styling."""
        with gr.Blocks(fill_height=True) as sidebar_demo:
            with gr.Sidebar():
                gr.Markdown(
                    """# 🔍 OpenDeepResearch
                    ### Smolagents + Document Tools
                    """
                )
                with gr.Group():
                    gr.Markdown("What can I help you with today?", container=True)
                    text_input = gr.Textbox(
                        lines=4,
                        label="Your request",
                        container=False,
                        placeholder="Enter your question or task here...",
                        show_label=False,
                    )

                    with gr.Row():
                        clear_btn = gr.Button("Clear", variant="secondary")
                        launch_research_btn = gr.Button("Run", variant="primary")

                # File upload section with better labeling
                if self.file_upload_folder is not None:
                    with gr.Group():
                        gr.Markdown("📎 Upload Documents")
                        upload_file = gr.File(
                            label="Upload files for analysis",
                            file_types=[
                                "pdf",
                                "docx",
                                "txt",
                                "md",
                                "csv",
                                "xlsx",
                                "jpg",
                                "png",
                            ],
                            file_count="multiple",
                        )
                        upload_status = gr.Textbox(
                            label="Upload Status", interactive=False, visible=False
                        )
                        file_uploads_log = gr.State([])

                        # Show uploaded files list
                        uploaded_files_display = gr.Markdown("No files uploaded yet")

                        upload_file.change(
                            self.upload_file,
                            [upload_file, file_uploads_log],
                            [upload_status, file_uploads_log],
                        ).then(
                            lambda files: (
                                "Uploaded Files:\n"
                                + "\n".join([f"- {os.path.basename(f)}" for f in files])
                                if files
                                else "No files uploaded yet"
                            ),
                            [file_uploads_log],
                            [uploaded_files_display],
                        )

                gr.HTML("<br><hr><h4><center>Powered by:</center></h4>")
                with gr.Row():
                    gr.HTML(
                        """
                        <div style="display: flex; align-items: center; justify-content: 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>
                        """
                    )

            # Main chat area with improved styling
            session_state = gr.State({})
            stored_messages = gr.State([])
            if "file_uploads_log" not in locals():
                file_uploads_log = gr.State([])

            chatbot = gr.Chatbot(
                label="OpenDeepResearch Assistant",
                type="messages",
                avatar_images=(
                    None,
                    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolagents/mascot_smol.png",
                ),
                resizeable=True,
                show_copy_button=True,
                scale=1,
                elem_id="my-chatbot",
                height=700,
            )

            # Connect clear button
            clear_btn.click(
                lambda: ([], [], {"agent": session_state.get("agent")}),
                None,
                [chatbot, stored_messages, session_state],
            )

            # Connect event handlers
            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()