app copy.py

import os
# import gradio as gr
import requests
import inspect
import pandas as pd
from langchain_core.messages import HumanMessage
from agent import build_graph 
from huggingface_hub import HfApi, hf_hub_download
import logging

logger = logging.getLogger(__name__)

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

# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
        self.graph = build_graph()

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        messages = [HumanMessage(content=question)]
        result = self.graph.invoke({"messages": messages})
        answer = result['messages'][-1].content
        return answer

def file_extract(local_file_path, task_id):
    if not local_file_path:
        return None
        
    token = os.getenv("HUGGINGFACEHUB_API_TOKEN") or os.getenv("HF_TOKEN")
    
    # GAIA files are usually placed in date-based subdirectories
    prefixes = ["2023/validation/", "2023/test/", "2023/train/", ""]
    
    for prefix in prefixes:
        try:
            resolved_path = hf_hub_download(
                repo_id="gaia-benchmark/GAIA",
                filename=f"{prefix}{local_file_path}",
                repo_type="dataset",
                token=token
            )
            return resolved_path
        except Exception:
            continue
            
    logger.warning(f"Could not download file '{local_file_path}' for task_id {task_id}. Make sure you accepted GAIA terms on HF and set HF_TOKEN.")
    return None
        
agent = BasicAgent()
questions_url = f"{DEFAULT_API_URL}/questions"
response = requests.get(questions_url, timeout=15)
response.raise_for_status()
questions_data = response.json()
import time
print(f"Running agent on {len(questions_data)} questions sequentially to avoid 429 errors...")
for item in questions_data[:2]:
    question_text = item.get("question")
    if question_text is None:
        continue
    files_text = item.get("files")
    task_id = item.get("task_id")
    file_name = item.get("file_name")
    
    if file_name:
        # Actually download the file to local cache and get absolute path
        resolved_path = file_extract(file_name, task_id)
        if resolved_path:
            question_text += f"\n\n[Attached File Local Path: {resolved_path}]"
        else:
            question_text += f"\n\n[Attached File: {file_name} (Download Failed)]"
            
    print(f"Processing Task ID: {task_id}")
    output = agent(question_text)
    print("Q:", question_text)
    print("A:", output)
    print("-" * 40)
    # Stagger requests to refill Token bucket and provide space for other concurrent tasks if any
    time.sleep(5)




# 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...")
#     print(f"Running agent on {len(questions_data[:5])} questions temporarily...")
#     for item in questions_data[:5]:
#         task_id = item.get("task_id")
#         question_text = item.get("question")
#         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)
#             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)