app.py

import argparse
import json
import os
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed
from datetime import datetime
from pathlib import Path
from typing import List, Optional

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

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,
    VisitTool,
    SimpleTextBrowser,
)
from scripts.visual_qa import visualizer
from tqdm import tqdm

from smolagents import (
    CodeAgent,
    HfApiModel,
    LiteLLMModel,
    Model,
    ToolCallingAgent,
)
from smolagents.agent_types import AgentText, AgentImage, AgentAudio
from smolagents.gradio_ui import pull_messages_from_step, handle_agent_output_types

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

load_dotenv(override=True)
login(os.getenv("HF_TOKEN"))

append_answer_lock = threading.Lock()

SET = "validation"

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

### LOAD EVALUATION DATASET

eval_ds = datasets.load_dataset("gaia-benchmark/GAIA", "2023_all")[SET]
eval_ds = eval_ds.rename_columns({"Question": "question", "Final answer": "true_answer", "Level": "task"})

def preprocess_file_paths(row):
    if len(row["file_name"]) > 0:
        row["file_name"] = f"data/gaia/{SET}/" + row["file_name"]
    return row

eval_ds = eval_ds.map(preprocess_file_paths)
eval_df = pd.DataFrame(eval_ds)
print("Loaded evaluation dataset:")
print(eval_df["task"].value_counts())

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,
    },
}

os.makedirs(f"./{BROWSER_CONFIG['downloads_folder']}", exist_ok=True)

# Custom OpenAI configuration
model = LiteLLMModel(
    "openai/custom-gpt",
    custom_role_conversions=custom_role_conversions,
    api_key=os.getenv("OPENAI_API_KEY"),
    api_base=os.getenv("CUSTOM_OPENAI_API_BASE"),
    temperature=0.1,
    frequency_penalty=0.2
)

text_limit = 20000
ti_tool = TextInspectorTool(model, text_limit)
browser = SimpleTextBrowser(**BROWSER_CONFIG)

class DuckDuckGoSearchTool:
    """Search tool using DuckDuckGo"""
    name = "web_search"
    description = "Search the web using DuckDuckGo (current information)"
    
    def __init__(self, max_results: int = 5):
        self.max_results = max_results
        
    def run(self, query: str) -> str:
        """Return formatted search results (snippets from webpages)"""
        try:
            web_results = []
            with DDGS() as ddgs:
                for result in ddgs.text(query, max_results=self.max_results):
                    web_results.append({
                        'title': result['title'],
                        'url': result['href'],
                        'content': result['body']
                    })
            
            formatted_results = []
            for idx, res in enumerate(web_results[:self.max_results], 1):
                formatted_results.append(
                    f"[{idx}] {res['title']}\n"
                    f"URL: {res['url']}\n"
                    f"Content: {res['content'][:500]}{'...' if len(res['content']) > 500 else ''}"
                )
            return "\n\n".join(formatted_results)
        except Exception as e:
            return f"Search error: {str(e)}"

WEB_TOOLS = [
    DuckDuckGoSearchTool(max_results=5),
    VisitTool(browser),
    PageUpTool(browser),
    PageDownTool(browser),
    FinderTool(browser),
    FindNextTool(browser),
    ArchiveSearchTool(browser),
    TextInspectorTool(model, text_limit),
]

# Agent creation in a factory function
def create_agent():
    """Creates a fresh agent instance for each session"""
    return CodeAgent(
        model=model,
        tools=[visualizer] + WEB_TOOLS,
        max_steps=5,
        verbosity_level=2,
        additional_authorized_imports=AUTHORIZED_IMPORTS,
        planning_interval=4,
    )

document_inspection_tool = TextInspectorTool(model, 20000)

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."""
    for step_log in agent.run(task, stream=True, reset=reset_agent_memory, additional_args=additional_args):
        for message in pull_messages_from_step(
            step_log,
        ):
            yield message

    final_answer = 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):
        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):
        if 'agent' not in session_state:
            session_state['agent'] = create_agent()
            
        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

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

        original_name = os.path.basename(file.name)
        sanitized_name = re.sub(r"[^\w\-.]", "_", original_name)

        type_to_ext = {}
        for ext, t in mimetypes.types_map.items():
            if t not in type_to_ext:
                type_to_ext[t] = ext

        sanitized_name = sanitized_name.split(".")[:-1]
        sanitized_name.append("" + type_to_ext[mime_type])
        sanitized_name = "".join(sanitized_name)

        file_path = os.path.join(self.file_upload_folder, os.path.basename(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):
        return (
            text_input
            + (
                f"\nYou have been provided with these files, which might be helpful or not: {file_uploads_log}"
                if len(file_uploads_log) > 0
                else ""
            ),
            "",
        )

    def launch(self, **kwargs):
        with gr.Blocks(theme="ocean", fill_height=True) as demo:
            gr.Markdown("""# Open Deep Research - AI Agent Interface
Advanced question answering using DuckDuckGo search and custom AI models""")
            
            session_state = gr.State({})
            stored_messages = gr.State([])
            file_uploads_log = gr.State([])
            chatbot = gr.Chatbot(
                label="Research Agent",
                type="messages",
                avatar_images=(
                    None,
                    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolagents/mascot_smol.png",
                ),
                resizeable=True,
                scale=1,
            )
            
            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="Enter your question")
            text_input.submit(
                self.log_user_message,
                [text_input, file_uploads_log],
                [stored_messages, text_input],
            ).then(
                self.interact_with_agent,
                [stored_messages, chatbot, session_state],
                [chatbot]
            )

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

if __name__ == "__main__":
    GradioUI().launch()