app.py

import argparse
import json
import mimetypes  # Added missing import
import os
import re  # Added missing import
import shutil  # Added missing import
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Optional, Any

import datasets
import pandas as pd
from dotenv import load_dotenv
from huggingface_hub import login
import gradio as gr

from scripts.reformulator import prepare_response
from scripts.run_agents import (
    get_single_file_description,
    get_zip_description,
)
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.flux_lora_tool import FluxLoRATool
from tqdm import tqdm

from smolagents import (
    CodeAgent,
    HfApiModel,
    LiteLLMModel,
    Model,
    OpenAIServerModel,  # Added missing model
    TransformersModel,  # Added missing model
    ToolCallingAgent,
    Tool,
)
from smolagents.agent_types import AgentText, AgentImage, AgentAudio
from smolagents.gradio_ui import pull_messages_from_step, handle_agent_output_types


class GoogleSearchTool(Tool):
    """Performs Google web searches using the Serper API."""

    name = "web_search"
    description = """Performs a google web search for your query then returns a string of the top search results."""
    inputs = {
        "query": {"type": "string", "description": "The search query to perform."},
        "filter_year": {
            "type": "integer",
            "description": "Optionally restrict results to a certain year",
            "nullable": True,
        },
    }
    output_type = "string"

    def __init__(self):
        """Initialize the tool with API key from environment."""
        super().__init__(self)
        self.serpapi_key = os.getenv("SERPER_API_KEY")
        self._validate_dependencies()

    def _validate_dependencies(self):
        """Ensure API key is available."""
        if not self.serpapi_key:
            raise ValueError(
                "Missing SerpAPI key. Make sure you have 'SERPER_API_KEY' in your env variables."
            )

    def forward(self, query: str, filter_year: Optional[int] = None) -> str:
        """Execute the search query and return formatted results."""
        import requests

        params = {
            "engine": "google",
            "q": query,
            "api_key": self.serpapi_key,
            "google_domain": "google.com",
        }

        headers = {"X-API-KEY": self.serpapi_key, "Content-Type": "application/json"}

        if filter_year is not None:
            params["tbs"] = (
                f"cdr:1,cd_min:01/01/{filter_year},cd_max:12/31/{filter_year}"
            )

        response = requests.request(
            "POST",
            "https://google.serper.dev/search",
            headers=headers,
            data=json.dumps(params),
        )

        if response.status_code == 200:
            results = response.json()
        else:
            raise ValueError(response.json())

        if "organic" not in results.keys() or len(results["organic"]) == 0:
            year_filter_message = (
                f" with filter year={filter_year}" if filter_year is not None else ""
            )
            return f"No results found for '{query}'{year_filter_message}. Try with a more general query, or remove the year filter."

        return self._format_search_results(results["organic"])

    def _format_search_results(self, organic_results: List[Dict[str, Any]]) -> str:
        """Format organic search results into a readable string."""
        web_snippets = []

        for idx, page in enumerate(organic_results):
            date_published = (
                f"\nDate published: {page['date']}" if "date" in page else ""
            )
            source = f"\nSource: {page['source']}" if "source" in page else ""
            snippet = f"\n{page['snippet']}" if "snippet" in page else ""

            formatted_result = f"{idx}. [{page['title']}]({page['link']}){date_published}{source}\n{snippet}"
            formatted_result = formatted_result.replace(
                "Your browser can't play this video.", ""
            )
            web_snippets.append(formatted_result)

        return "## Search Results\n" + "\n\n".join(web_snippets)


# Constants and configurations
AUTHORIZED_IMPORTS = [
    "requests",
    "zipfile",
    "pandas",
    "numpy",
    "sympy",
    "json",
    "bs4",
    "pubchempy",
    "xml",
    "yahoo_finance",
    "Bio",
    "sklearn",
    "scipy",
    "pydub",
    "PIL",
    "chess",
    "PyPDF2",
    "pptx",
    "torch",
    "datetime",
    "fractions",
    "csv",
]


# Configuration setup
def setup_environment():
    """Initialize environment variables and authentication."""
    load_dotenv(override=True)
    login(os.getenv("HF_TOKEN"))
    print("TOKKKK", os.getenv("HF_TOKEN")[-10:])


# Browser configuration
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 for model response handling
custom_role_conversions = {"tool-call": "assistant", "tool-response": "user"}


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 == "ollama":
                if not key_manager:
                    raise ValueError("Key manager required for Ollama model")

                return LiteLLMModel(
                    model_id=model_id,
                    api_base="http://localhost:11434",
                    api_key=key_manager.get_key("ollama_api_key"),
                    num_ctx=8192,
                )

            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(),
            VisitTool(browser),
            PageUpTool(browser),
            PageDownTool(browser),
            FinderTool(browser),
            FindNextTool(browser),
            ArchiveSearchTool(browser),
            TextInspectorTool(model, text_limit),
        ]

    @staticmethod
    def load_vision_tools():
        """Initialize and return vision-related tools."""
        try:
            return Tool.from_space(
                space_id="xkerser/gemma-3-12b-it",
                name="gemma_vision",
                description="Upload an image to extract and analyze text and visual content from images using Gemma 3",
            )
        except Exception as e:
            print(f"✗ Couldn't initialize vision tool: {e}")
            raise

    @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 images 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 in a factory function
def create_agent():
    """Creates a fresh agent instance for each session."""
    # Initialize model
    model = LiteLLMModel(
        custom_role_conversions=custom_role_conversions,
        model_id="openrouter/perplexity/r1-1776",
    )

    # Initialize tools
    text_limit = 20000
    browser = SimpleTextBrowser(**BROWSER_CONFIG)

    web_tools = ToolRegistry.load_web_tools(model, browser, text_limit)
    gemma_vision_tool = ToolRegistry.load_vision_tools()

    return CodeAgent(
        model=model,
        tools=([visualizer] + web_tools, gemma_vision_tool),  # Fixed the missing comma
        max_steps=10,
        verbosity_level=1,
        additional_authorized_imports=AUTHORIZED_IMPORTS,
        planning_interval=4,
    )


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

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

        # 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}"

        return (
            message,
            gr.Textbox(
                value="",
                interactive=False,
                placeholder="Please wait while Steps are getting populated",
            ),
            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"

        # 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"
            elif 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()
                else:
                    return self._create_mobile_layout()

        demo.launch(debug=True, **kwargs)

    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 - free the AI agents!""")
                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 not "file_uploads_log" 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],
        )


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().launch()


if __name__ == "__main__":
    main()