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| import os | |
| import sys | |
| import json | |
| import tempfile | |
| import gradio as gr | |
| import requests | |
| import inspect | |
| import pandas as pd | |
| from typing import List, Dict, Any, Optional | |
| import traceback | |
| # vimport dotenv | |
| # Load environment variables from .env file | |
| # dotenv.load_dotenv() | |
| # Import our agent | |
| from agent import QAgent | |
| from answer_data_manager import AnswerDataManager | |
| # (Keep Constants as is) | |
| # --- Constants --- | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| # Simulation of GAIA benchmark questions | |
| SAMPLE_QUESTIONS = [ | |
| { | |
| "task_id": "task_002", | |
| "question": "What is the square root of 144?", | |
| "expected_answer": "12", | |
| "has_file": False, | |
| "file_content": None | |
| } | |
| ] | |
| SAMPLE_QUESTIONS_OUT = [ | |
| { | |
| "task_id": "task_001", | |
| "question": "What is the capital of France?", | |
| "expected_answer": "Paris", | |
| "has_file": False, | |
| "file_content": None | |
| }, | |
| { | |
| "task_id": "task_003", | |
| "question": "If a train travels at 60 miles per hour, how far will it travel in 2.5 hours?", | |
| "expected_answer": "150 miles", | |
| "has_file": False, | |
| "file_content": None | |
| }, | |
| { | |
| "task_id": "task_004", | |
| "question": ".rewsna eht sa 'thgir' drow eht etirw ,tfel fo etisoppo eht si tahW", | |
| "expected_answer": "right", | |
| "has_file": False, | |
| "file_content": None | |
| }, | |
| { | |
| "task_id": "task_005", | |
| "question": "Analyze the data in the attached CSV file and tell me the total sales for the month of January.", | |
| "expected_answer": "$10,250.75", | |
| "has_file": True, | |
| "file_content": """Date,Product,Quantity,Price,Total | |
| 2023-01-05,Widget A,10,25.99,259.90 | |
| 2023-01-12,Widget B,5,45.50,227.50 | |
| 2023-01-15,Widget C,20,50.25,1005.00 | |
| 2023-01-20,Widget A,15,25.99,389.85 | |
| 2023-01-25,Widget B,8,45.50,364.00 | |
| 2023-01-28,Widget D,100,80.04,8004.50""" | |
| }, | |
| { | |
| "task_id": "task_006", | |
| "question": "I'm making a grocery list for my mom, but she's a picky eater. She only eats foods that don't contain the letter 'e'. List 5 common fruits and vegetables she can eat.", | |
| "expected_answer": "Banana, Kiwi, Corn, Fig, Taro", | |
| "has_file": False, | |
| "file_content": None | |
| }, | |
| { | |
| "task_id": "task_007", | |
| "question": "How many studio albums were published by Mercedes Sosa between 1972 and 1985?", | |
| "expected_answer": "12", | |
| "has_file": False, | |
| "file_content": None | |
| }, | |
| { | |
| "task_id": "task_008", | |
| "question": "In the video https://www.youtube.com/watch?v=L1vXC1KMRd0, what color is primarily associated with the main character?", | |
| "expected_answer": "Blue", | |
| "has_file": False, | |
| "file_content": None | |
| } | |
| ] | |
| def init_agent(): | |
| """Initialize the QAgent.""" | |
| print("Initializing QAgent...") | |
| try: | |
| agent = QAgent() | |
| return agent | |
| except Exception as e: | |
| print(f"Error instantiating agent for GAIA simulation: {e}") | |
| return None | |
| def save_test_file(task_id: str, content: str) -> str: | |
| """Save a test file to a temporary location.""" | |
| temp_dir = tempfile.gettempdir() | |
| file_path = os.path.join(temp_dir, f"test_file_{task_id}.csv") | |
| with open(file_path, 'w') as f: | |
| f.write(content) | |
| return file_path | |
| def run_GAIA_questions_simu(): | |
| """ | |
| Used only during development for test that simulate GAIA questions. | |
| """ | |
| # 1. Instantiate Agent | |
| agent = init_agent() | |
| results = [] | |
| correct_count = 0 | |
| total_count = len(SAMPLE_QUESTIONS) | |
| for idx, question_data in enumerate(SAMPLE_QUESTIONS): | |
| task_id = question_data["task_id"] | |
| question = question_data["question"] | |
| expected = question_data["expected_answer"] | |
| print(f"\n{'='*80}") | |
| print(f"Question {idx+1}/{total_count}: {question}") | |
| print(f"Expected: {expected}") | |
| # Process any attached file | |
| # file_path = None | |
| # if question_data["has_file"] and question_data["file_content"]: | |
| # file_path = save_test_file(task_id, question_data["file_content"]) | |
| # print(f"Created test file: {file_path}") | |
| # Get answer from agent | |
| try: | |
| answer = agent.invoke(question) # , file_path) | |
| print(f"Agent answer: {answer}") | |
| # Check if answer matches expected | |
| is_correct = answer.lower() == expected.lower() | |
| if is_correct: | |
| correct_count += 1 | |
| print(f"✅ CORRECT") | |
| else: | |
| print(f"❌ INCORRECT - Expected: {expected}") | |
| results.append({ | |
| "task_id": task_id, | |
| "question": question, | |
| "expected": expected, | |
| "answer": answer, | |
| "is_correct": is_correct | |
| }) | |
| except Exception as e: | |
| error_details = traceback.format_exc() | |
| print(f"Error processing question: {e}\n{error_details}") | |
| results.append({ | |
| "task_id": task_id, | |
| "question": question, | |
| "expected": expected, | |
| "answer": f"ERROR: {str(e)}", | |
| "is_correct": False | |
| }) | |
| # Print summary | |
| accuracy = (correct_count / total_count) * 100 | |
| print(f"\n{'='*80}") | |
| print(f"Test Results: {correct_count}/{total_count} correct ({accuracy:.1f}%)") | |
| return results | |
| def run_simuGAIA_all( profile: gr.OAuthProfile | None, submit: Optional[bool] = False): | |
| """ | |
| Fetches all questions, runs the QAgent on them, | |
| and displays the results. | |
| """ | |
| # --- Determine HF Space Runtime URL and Repo URL for submission --- | |
| 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 and init Agent ( modify this part to create your agent) | |
| agent = init_agent() | |
| # 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 | |
| # 2.5 Awaken the AnswerDataManager to get and store already answered questions | |
| manager = AnswerDataManager("already_answered.json") | |
| data = manager.load_data() | |
| print(data.__str__) | |
| # 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") | |
| submitted_answer = "NO ANSWER YET" | |
| if not task_id or question_text is None: | |
| print(f"Skipping item with missing task_id or question: {item}") | |
| continue | |
| try: | |
| existing_answer = manager.get_answer_by_task_id(task_id) | |
| if not existing_answer: | |
| # then we call the agent | |
| if question_text.startswith(".rewsna eht sa"): # ("How many studio albums"): <--- REMOVE THAT FOR ALL QUESTIONS | |
| print(f"First question detected. INVOKING AGENT! Be careful!") | |
| submitted_answer = agent.invoke(question_text) | |
| # Save answer, task_id, and question_text to already_answered.json | |
| # manager.add_answer(task_id, question_text, submitted_answer) | |
| success = manager.add_answer( | |
| task_id=task_id, | |
| question=question_text, | |
| submitted_answer=submitted_answer | |
| ) | |
| if not success: | |
| print("Error saving answer to archive.") | |
| answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer}) | |
| else: | |
| submitted_answer = "NO AGENT INVOKED" | |
| else: | |
| # then we get answer already found from archive | |
| submitted_answer = existing_answer['submitted_answer'] | |
| 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) | |
| if not submit: | |
| return "Run finished. No submission done, as asked.", 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 | |