app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from dotenv import load_dotenv
import os

from langchain.tools import Tool
from langchain_openai import ChatOpenAI
from typing import Annotated, Optional, TypedDict
from langchain_core.messages import AnyMessage, HumanMessage, SystemMessage
from langgraph.graph.message import add_messages
from langgraph.graph import StateGraph, START
from langgraph.prebuilt import tools_condition, ToolNode

from tavily_searcher import TavilySearcher
from youtube_video_question_answer_tool import YoutubeVideoQuestionAnswerTool







# Load environment variables from .env file
load_dotenv()

print(f'os.getenv("HUGGINGFACE_TOKEN"): {os.getenv("HUGGINGFACE_TOKEN")}')

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
ASSOCIATED_FILE_ENDPOINT = f"{DEFAULT_API_URL}/files/"


def retrieve_task_file(task_id: str) -> Optional[bytes]:
    """
    Retrieve the task file for a given task ID.
    """
    try:
        response = requests.get(ASSOCIATED_FILE_ENDPOINT + task_id, timeout=15)
        response.raise_for_status()
        if response.status_code != 200:
            print(f"Error fetching file: {response.status_code}")
            return None
        #print(f"Fetched file: {response.content}")
        return response.content
    except requests.exceptions.RequestException as e:
        print(f"Error fetching file: {e}")
        return None
    except Exception as e:
        print(f"An unexpected error occurred fetching file: {e}")
        return None
    

tavily_search_tool = Tool(
    name="tavily_search",
    func=TavilySearcher().search,
    description="Search the web for a given query."
)

# Initialize the tool
answer_youtube_video_question_tool = YoutubeVideoQuestionAnswerTool()




def format_gaia_answer(answer: str) -> str:
    llm = ChatOpenAI(model="o3-mini", openai_api_key=os.getenv("OPENAI_API_KEY"))
    prompt = f"""
    You are formatting answers for the GAIA benchmark, which requires responses to be concise and unambiguous.
    Given the answer: {answer}
    Return the answer in the correct GAIA format:
    - If the answer is a single word or number, return it without any additional text or formatting.
    - If the answer is a list, return a comma-separated list without any additional text or formatting.
    - If the answer is a string, return it without any additional text or formatting.
    Do not include any prefixes, dots, enumerations, explanations, or quotation marks.
    Do not include any additional text or formatting.
    """
    response = llm.invoke(prompt)
    # Delete double quotes
    return response.content.strip().replace('"', '')





# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------

class AgentState(TypedDict):
    messages: Annotated[list[AnyMessage], add_messages]
    file_path: Optional[str]


class BasicAgent:
    def __init__(self):
        tools = [
            tavily_search_tool, 
            # arxiv_search_tool,
            # wikipedia_search_tool, 
            # transcribe_audio_tool, 
            # answer_python_code_tool, 
            # answer_image_question_tool, 
            answer_youtube_video_question_tool,
            # read_excel_file_tool
        ]
        llm = ChatOpenAI(model="o3-mini", openai_api_key=os.getenv("OPENAI_API_KEY"))
        self.llm_with_tools = llm.bind_tools(tools)
        builder = StateGraph(AgentState)

        # Define nodes: these do the work
        builder.add_node("assistant", self.assistant)
        builder.add_node("tools", ToolNode(tools))

        # Define edges: these determine how the control flow moves
        builder.add_edge(START, "assistant")
        builder.add_conditional_edges(
            "assistant",
            # If the latest message requires a tool, route to tools
            # Otherwise, provide a direct response
            tools_condition,
        )
        builder.add_edge("tools", "assistant")
        self.agent = builder.compile()

        print("BasicAgent initialized.")

    def assistant(self, state: AgentState):
        # System message
        textual_description_of_tools="""
            tavily_search(query: str) -> str:
                Search the web for a given query.
                Args:
                    query: Query to search the web for (string).
                Returns:
                    A single string containing the information found on the web.

            answer_youtube_video_question(file_path: str, question: str) -> str:
                Answer the question based on file path of the downloaded youtube video

                Args:
                    file_path: File path of the downloaded youtube video (string).
                    question: Question to answer (string).

                Returns:
                    A single string containing the answer to the question.

        """
        
        file_path=state["file_path"]
        prompt = f"""
            You are a helpful assistant that can analyse images, videos, excel files and Python scripts and run computations with provided tools:
            {textual_description_of_tools} 
            You have access to the file path of the attached file in case it's informed. Currently the file path is: {file_path}
            Be direct and specific. GAIA benchmark requires exact matching answers.
            For example, if asked "What is the capital of France?", respond simply with "Paris".
            Do not include any prefixes, dots, enumerations, explanations, or quotation marks.
            Do not include any additional text or formatting.
            If you are required a number, return a number, not the items.
        """
        sys_msg = SystemMessage(content=prompt)

        return {
            "messages": [self.llm_with_tools.invoke([sys_msg] + state["messages"], config={"configurable": {"file_path": state["file_path"]}})],
            "file_path": state["file_path"]
        }

    
    def __call__(self, question: str, task_id: str, file_name: str) -> str:
        print(f"######################### Agent received question (first 50 chars): {question[:50]}... with file_name: {file_name}")

        # Get the file path
        tmp_file_path = None
        if file_name is not None and file_name != "":
            file_content = retrieve_task_file(task_id)
            if file_content is not None:
                print(f"Saving file {file_name} to tmp folder")
                tmp_file_path = f"tmp/{file_name}"
                with open(tmp_file_path, "wb") as f:
                    f.write(file_content)
        # Show the file path
        print(f"File path: {tmp_file_path}")

        messages = self.agent.invoke({"messages": [HumanMessage(question)], "file_path": tmp_file_path})
        print(f"messages: {messages}")
        # Show the messages
        for m in messages['messages']:
            m.pretty_print()
        answer = messages["messages"][-1].content
        answer = format_gaia_answer(answer)
        print(f"######################### Agent returning answer: {answer}\n")
        # Delete the file
        if tmp_file_path is not None:
            os.remove(tmp_file_path)
        return answer




def run_and_submit_all( profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code

    if profile:
        username= f"{profile.username}"
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    # 1. Instantiate Agent ( modify this part to create your agent)
    try:
        agent = BasicAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None
    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)

    # 2. Fetch Questions
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
             print("Fetched questions list is empty.")
             return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None
    except requests.exceptions.JSONDecodeError as e:
         print(f"Error decoding JSON response from questions endpoint: {e}")
         print(f"Response text: {response.text[:500]}")
         return f"Error decoding server response for questions: {e}", None
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred fetching questions: {e}", None

    # 3. Run your Agent
    results_log = []
    answers_payload = []
    print(f"Running agent on {len(questions_data)} questions...")
    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question")
        file_name = item.get("file_name")           #!
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue
        try:
            # submitted_answer = agent(question_text)
            submitted_answer = agent(question_text, task_id, file_name)
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
        except Exception as e:
             print(f"Error running agent on task {task_id}: {e}")
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})

    if not answers_payload:
        print("Agent did not produce any answers to submit.")
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # 4. Prepare Submission 
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
    print(status_update)

    # 5. Submit
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Overall Score: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        print("Submission successful.")
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except requests.exceptions.HTTPError as e:
        error_detail = f"Server responded with status {e.response.status_code}."
        try:
            error_json = e.response.json()
            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
        except requests.exceptions.JSONDecodeError:
            error_detail += f" Response: {e.response.text[:500]}"
        status_message = f"Submission Failed: {error_detail}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.Timeout:
        status_message = "Submission Failed: The request timed out."
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.RequestException as e:
        status_message = f"Submission Failed: Network error - {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except Exception as e:
        status_message = f"An unexpected error occurred during submission: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**

        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.

        ---
        **Disclaimers:**
        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
        """
    )

    gr.LoginButton()

    run_button = gr.Button("Run Evaluation & Submit All Answers")

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    # Removed max_rows=10 from DataFrame constructor
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    # Check for SPACE_HOST and SPACE_ID at startup for information
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup

    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")

    if space_id_startup: # Print repo URLs if SPACE_ID is found
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")

    print("-"*(60 + len(" App Starting ")) + "\n")

    print("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False)