import os import gradio as gr import requests import pandas as pd import time import json # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" class BasicAgent: def __init__(self): # Load metadata.jsonl self.metadata = self._load_metadata() print("BasicAgent initialized with metadata") def _load_metadata(self): """Load metadata.jsonl, parsing each line as a JSON object.""" data = [] try: with open("metadata.jsonl", 'r', encoding='utf-8') as f: for line_number, line in enumerate(f, 1): line = line.strip() if not line: continue try: obj = json.loads(line) if isinstance(obj, dict): data.append(obj) else: print(f"Skipping line {line_number}: not a dictionary") except json.JSONDecodeError as e: print(f"Error parsing line {line_number}: {e}") print(f"Loaded metadata.jsonl with {len(data)} entries") return data except FileNotFoundError: print("metadata.jsonl not found. Proceeding without metadata.") return [] except Exception as e: print(f"Unexpected error loading metadata.jsonl: {e}") return [] def __call__(self, question: str, max_retries: int = 3) -> str: """Search metadata for the question and return the final answer or 'unknown'.""" print(f"Agent received question (first 50 chars): {question[:50]}...") # Search metadata.jsonl for the question for item in self.metadata: if item.get("Question") == question: final_answer = item.get("Final answer") if final_answer: print(f"Found answer in metadata.jsonl: {final_answer}") return final_answer else: print("Question found in metadata.jsonl, but no final answer provided.") # Fallback if question not found print("Question not found in metadata.jsonl. Returning 'unknown'.") return "unknown" def run_and_submit_all(profile: gr.OAuthProfile | None, progress=gr.Progress()): """ Fetches all questions, runs the BasicAgent on them, submits all answers, and displays the results with progress tracking. """ space_id = os.getenv("SPACE_ID") 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 progress(0, desc="Initializing agent...") try: agent = BasicAgent() except Exception as e: print(f"Error instantiating agent: {e}") return f"Error initializing agent: {e}", None agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" print(agent_code) # 2. Fetch Questions progress(0.1, desc="Fetching 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}") 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 = [] total_questions = len(questions_data) print(f"Running agent on {total_questions} questions...") for i, item in enumerate(questions_data): progress((0.1 + 0.8 * i / total_questions), desc=f"Processing question {i+1}/{total_questions}") task_id = item.get("task_id") question_text = item.get("question") requires_file = item.get("requires_file", False) if not task_id or question_text is None: print(f"Skipping item with missing task_id or question: {item}") continue print(f"Processing task {task_id} ({i+1}/{total_questions})") try: # Skip file handling since agent doesn't use files if requires_file: print(f"Task {task_id} requires file, but agent doesn't support file handling. Using question as is.") 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}) # Add small delay between requests time.sleep(0.1) except Exception as e: error_msg = f"PROCESSING_ERROR: {e}" print(f"Error running agent on task {task_id}: {e}") results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": error_msg}) if not answers_payload: print("Agent did not produce any valid answers to submit.") return "Agent did not produce any valid answers to submit. Check the results table for errors.", pd.DataFrame(results_log) # 4. Prepare Submission progress(0.9, desc="Submitting answers...") 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"Processed: {len(results_log)} questions\n" f"Successfully submitted: {len(answers_payload)} answers\n" f"Model used: Metadata-based lookup\n" f"Message: {result_data.get('message', 'No message received.')}" ) print("Submission successful.") progress(1.0, desc="Complete!") 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("# Agent Evaluation Runner") gr.Markdown( """ **Instructions:** 1. Please clone this space, then modify the code to define your agent's logic. 2. Ensure metadata.jsonl is available with question-answer pairs. 3. Log in to your Hugging Face account using the button below. This uses your HF username for submission. 4. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score. --- **Agent Configuration:** - 📄 Uses metadata.jsonl for answer lookup - ❓ Returns 'unknown' for unmatched questions """ ) gr.LoginButton() run_button = gr.Button("Run Evaluation & Submit All Answers") status_output = gr.Textbox(label="Run Status / Submission Result", lines=8, interactive=False) results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True) run_button.click( fn=run_and_submit_all, outputs=[status_output, results_table], show_progress=True ) if __name__ == "__main__": print("\n" + "-"*30 + " App Starting " + "-"*30) space_host_startup = os.getenv("SPACE_HOST") space_id_startup = os.getenv("SPACE_ID") 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("ℹī¸ SPACE_HOST environment variable not found (running locally?).") if space_id_startup: 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("ℹī¸ SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.") print("-"*(60 + len(" App Starting ")) + "\n") print("Launching Gradio Interface for Agent Evaluation...") demo.launch(debug=True, share=False)