app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from src.agent import BasicAgent
from datasets import load_dataset
from huggingface_hub import snapshot_download
from docx import Document

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

agent = BasicAgent()

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

    # 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)
    ACCESS_TOKEN = os.getenv("HF_TOKEN") 

    if not ACCESS_TOKEN:
        raise ValueError("HF_TOKEN environment variable is not set. Please set it in Space Secrets.")
    else:
        print("Key is good")
    data_dir = snapshot_download(
        repo_id="gaia-benchmark/GAIA",
        repo_type="dataset"
    )
    
    dataset = load_dataset(data_dir, "2023_level1", split="validation", cache_dir=data_dir)
    print("Dataset", dataset)
    print("Length is ", len(dataset))
    print(type(dataset))
    id_to_path = {}
    for ex in dataset:
        # Check if the example has an associated file path and name
        if ex.get("file_path") and ex.get("file_name"):
            full_path = os.path.join(data_dir, ex["file_path"])
            
            # Check if the file actually exists on disk
            if os.path.exists(full_path):
                id_to_path[ex["task_id"]] = full_path
                
    # The 'id_to_path' dictionary is essential for your file reading tool.
    print(f"Mapped {len(id_to_path)} {id_to_path} question IDs to resource files.")

    # 3. Run your Agent
    results_log = []
    answers_payload = []
    #files_base = os.path.join(data_dir, "2023", "test")
    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


    """
    # Filter the dataset to include ONLY the target task ID
    # This uses the 'filter' method available on Hugging Face datasets.
    #subset = dataset.filter(lambda example: example['task_id'] in target_task_ids)
    specific_target_ids = [
    'e1fc63a2-da7a-432f-be78-7c4a95598703',
    'a1e91b78-d3d8-4675-bb8d-62741b4b68a6',
    '4fc2f1ae-8625-45b5-ab34-ad4433bc21f8',
    '8e867cd7-cff9-4e6c-867a-ff5ddc2550be',
    'ec09fa32-d03f-4bf8-84b0-1f16922c3ae4',
    '2d83110e-a098-4ebb-9987-066c06fa42d0',
    '5cfb274c-0207-4aa7-9575-6ac0bd95d9b2',
    '27d5d136-8563-469e-92bf-fd103c28b57c',
    'dc28cf18-6431-458b-83ef-64b3ce566c10',
    '42576abe-0deb-4869-8c63-225c2d75a95a'
    ]
    # --- END SPECIFIC TARGET IDS ---
    
    # 1. Get the list of Task IDs from the slice (indices 20 to 50)
    # We must fetch the task_id column data specifically.
    sliced_ids = dataset.select(range(20, 51))['task_id']
    
    # 2. Combine the sliced IDs with the specific IDs into a single set for uniqueness
    # This ensures we don't accidentally duplicate tasks if some specific IDs are in the slice range.
    all_unique_target_ids = set(sliced_ids)
    all_unique_target_ids.update(specific_target_ids)
    all_unique_target_ids_list = list(all_unique_target_ids)
    
    print(f"Total unique tasks to run: {len(all_unique_target_ids_list)}")
    
    # 3. Filter the original dataset using the complete list of unique IDs
    # This replaces the need for complex concatenation.
    """

    target_task_ids = [
    "8e867cd7-cff9-4e6c-867a-ff5ddc2550be",
      "a1e91b78-d3d8-4675-bb8d-62741b4b68a6",
      "2d83110e-a098-4ebb-9987-066c06fa42d0",
      "cca530fc-4052-43b2-b130-b30968d8aa44",
      "4fc2f1ae-8625-45b5-ab34-ad4433bc21f8",
      "6f37996b-2ac7-44b0-8e68-6d28256631b4",
      "9d191bce-651d-4746-be2d-7ef8ecadb9c2",
      "cabe07ed-9eca-40ea-8ead-410ef5e83f91",
      "3cef3a44-215e-4aed-8e3b-b1e3f08063b7",
      "99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3",
      "305ac316-eef6-4446-960a-92d80d542f82",
      "f918266a-b3e0-4914-865d-4faa564f1aef",
      "3f57289b-8c60-48be-bd80-01f8099ca449",
      "1f975693-876d-457b-a649-393859e79bf3",
      "840bfca7-4f7b-481a-8794-c560c340185d",
      "bda648d7-d618-4883-88f4-3466eabd860e",
      "cf106601-ab4f-4af9-b045-5295fe67b37d",
      "a0c07678-e491-4bbc-8f0b-07405144218f",
      "7bd855d8-463d-4ed5-93ca-5fe35145f733",
      "5a0c1adf-205e-4841-a666-7c3ef95def9d"
         ]
    subset = dataset.filter(lambda example: example['task_id'] in target_task_ids)
    subset = subset.to_list() 
    print(subset)

    results_log = []
    answers_payload = []

    for item in subset:
        print(f"ITEMS {item}")
        task_id = item.get("task_id")
        print(f"Task ID is {task_id}")
        question_text = item.get("Question")
        print(f"question_text is {question_text}")
        file_name = item.get("file_name")
        print(f"File Name {file_name}")
        file_path = id_to_path.get(task_id, None) 
        print(f"File path {file_path}")
        file_content = None
        if file_name and file_path:
            exists = os.path.exists(file_path)
            print("Checking file path")
            print(f"Task ID: {task_id}, File Name: {file_name}, Exists: {exists}, Calculated Path: {file_path}")
            print(f"Attempting to load file at: {file_path} (Exists: {exists})")
            
            if exists:
            # Decide binary or text
                if file_name.endswith((".txt", ".py", ".csv", ".json")):
                    try:
                        with open(file_path, "r", encoding="utf-8") as f:
                            file_content = f.read()
                            print(f"File Content is {file_content}, {file_path}")
                    except Exception as e:
                        print(f"Error reading text file {file_path}: {e}")
                        file_content = None
                elif file_name.endswith(".docx"):
                    try:
                        doc = Document(file_path)
                        file_content = "\n".join([p.text for p in doc.paragraphs])
                        print(f"Docx content loaded, {file_path}")
                    except Exception as e:
                        print(f"Error reading docx file {file_path}: {e}")
                        file_content = None
    
                    
                else:  # binary files like images, audio, video
                    try:
                        with open(file_path, "rb") as f:
                            file_content = f.read()
                            print(f"Binary file loaded, {file_path}")
                    except Exception as e:
                        print(f"Error reading binary file {file_path}: {e}")
                        file_content = None
            
    
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue
        try:
            if file_content:
                answer = agent(question_text, file_content=file_content, file_path=file_path)
            else:
                answer = agent(question_text)

            if not answer:
                answer = "I am unable to answer"
            answers_payload.append({"task_id": task_id, "submitted_answer": answer})
            results_log.append({"Task ID": task_id, "Question": question_text, "Answer": 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, "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)
    
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(agent_code)
    
    # 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)

    submit_url = f"{DEFAULT_API_URL}/submit"
    # 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")
    print(space_host_startup)
    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
    print(space_id_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)