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| import os | |
| import gradio as gr | |
| import requests | |
| import pandas as pd | |
| import json | |
| import time | |
| from pathlib import Path | |
| from langchain_core.messages import HumanMessage | |
| from requests.adapters import HTTPAdapter | |
| from urllib3.util.retry import Retry | |
| from datetime import datetime, timedelta | |
| from agent import AdvancedAgent # Assuming you have an AdvancedAgent class in agent.py | |
| # --- Constants --- | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| CACHE_FILE = "questions_cache.json" | |
| CACHE_EXPIRATION = timedelta(days=1) # Cache expires after 1 day | |
| MAX_RETRIES = 5 | |
| INITIAL_BACKOFF = 5 # Initial backoff time in seconds for retries | |
| def create_retry_session(): | |
| """Create a requests session with retry logic for handling 429 errors.""" | |
| session = requests.Session() | |
| retries = Retry( | |
| total=MAX_RETRIES, | |
| backoff_factor=INITIAL_BACKOFF, | |
| status_forcelist=[429], | |
| allowed_methods=["GET", "POST"] | |
| ) | |
| adapter = HTTPAdapter(max_retries=retries) | |
| session.mount("http://", adapter) | |
| session.mount("https://", adapter) | |
| return session | |
| def load_cached_questions(): | |
| """Load cached questions if the cache is still valid.""" | |
| cache_path = Path(CACHE_FILE) | |
| if cache_path.exists(): | |
| try: | |
| with cache_path.open('r') as f: | |
| cache_data = json.load(f) | |
| timestamp = datetime.fromisoformat(cache_data['timestamp']) | |
| if datetime.now() - timestamp < CACHE_EXPIRATION: | |
| questions = [ | |
| { | |
| "task_id": item["task_id"], | |
| "question": HumanMessage(content=item["question"]) | |
| } | |
| for item in cache_data['questions'] | |
| ] | |
| print(f"Loaded {len(questions)} questions from cache.") | |
| return questions | |
| else: | |
| print("Cache expired.") | |
| except Exception as e: | |
| print(f"Error loading cached questions: {e}") | |
| return None | |
| def cache_questions(questions_data): | |
| """Cache questions with a timestamp.""" | |
| cache_path = Path(CACHE_FILE) | |
| try: | |
| cache_data = { | |
| "timestamp": datetime.now().isoformat(), | |
| "questions": [ | |
| { | |
| "task_id": item["task_id"], | |
| "question": item["question"].content | |
| } | |
| for item in questions_data | |
| ] | |
| } | |
| with cache_path.open('w') as f: | |
| json.dump(cache_data, f, indent=2) | |
| print(f"Cached {len(questions_data)} questions to {CACHE_FILE}.") | |
| except Exception as e: | |
| print(f"Error caching questions: {e}") | |
| def fetch_questions_with_retry(url): | |
| """Fetch questions with retry logic for 429 errors.""" | |
| session = create_retry_session() | |
| try: | |
| response = session.get(url, timeout=15) | |
| response.raise_for_status() | |
| return response.json() | |
| except requests.exceptions.RequestException as e: | |
| print(f"Error fetching questions: {e}") | |
| raise e | |
| def load_questions(): | |
| """Load questions from cache or fetch from server with retries.""" | |
| questions_data = load_cached_questions() | |
| if questions_data is None: | |
| print(f"Fetching questions from: {DEFAULT_API_URL}/questions") | |
| try: | |
| raw_questions = fetch_questions_with_retry(f"{DEFAULT_API_URL}/questions") | |
| if not raw_questions: | |
| raise ValueError("Fetched questions list is empty.") | |
| questions_data = [ | |
| { | |
| "task_id": item["task_id"], | |
| "question": HumanMessage(content=item["question"]) | |
| } | |
| for item in raw_questions | |
| ] | |
| cache_questions(questions_data) | |
| except Exception as e: | |
| print(f"Error fetching questions: {e}") | |
| # Try to load expired cache as fallback | |
| cache_data = load_cached_questions() | |
| if cache_data: | |
| print("Using expired cache due to API failure.") | |
| questions_data = cache_data | |
| else: | |
| raise e | |
| return questions_data | |
| def run_and_submit_all(profile: gr.OAuthProfile | None): | |
| """ | |
| Fetches all questions, runs the AdvancedAgent on them, submits all answers, | |
| and displays the results. | |
| """ | |
| # --- Determine HF Space Runtime URL and Repo URL --- | |
| 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 | |
| submit_url = f"{api_url}/submit" | |
| # 1. Instantiate Agent | |
| try: | |
| agent = AdvancedAgent() | |
| 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. Load Questions (from cache or server) | |
| try: | |
| questions_data = load_questions() | |
| except Exception as e: | |
| print(f"Failed to load questions: {e}") | |
| return f"Failed to load questions: {e}", None | |
| # 3. Run Agent (simplified for this example) | |
| results_log = [] | |
| answers_payload = [] | |
| print(f"Running agent on {len(questions_data)} questions...") | |
| for item in questions_data: | |
| task_id = item["task_id"] | |
| question = item["question"] | |
| try: | |
| submitted_answer = agent(question.content) | |
| answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer}) | |
| results_log.append({ | |
| "Task ID": task_id, | |
| "Question": question.content, | |
| "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.content, | |
| "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 (with retry logic) | |
| print(f"Submitting {len(answers_payload)} answers to: {submit_url}") | |
| try: | |
| session = create_retry_session() | |
| response = session.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("# Advanced Agent Evaluation Runner") | |
| gr.Markdown( | |
| """ | |
| **Instructions:** | |
| 1. Modify the `agent.py` to define your agent's logic, tools, and packages. | |
| 2. Log in to your Hugging Face account using the button below. | |
| 3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score. | |
| --- | |
| **Disclaimers:** | |
| The submission process may take time due to the number of questions. | |
| Questions are cached locally to reduce API calls. | |
| """ | |
| ) | |
| gr.LoginButton() | |
| run_button = gr.Button("Run Evaluation & Submit All Answers") | |
| status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, 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] | |
| ) | |
| 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("ℹ️ 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("ℹ️ SPACE_ID environment variable not found (running locally?).") | |
| print("-"*(60 + len(" App Starting ")) + "\n") | |
| print("Launching Gradio Interface for Advanced Agent Evaluation...") | |
| demo.launch(debug=True, share=False) | |