Spaces:
Sleeping
Sleeping
| import os | |
| import gradio as gr | |
| import requests | |
| import pandas as pd | |
| import time | |
| import subprocess | |
| import sys | |
| from dotenv import load_dotenv | |
| from agent import LangGraphAgent | |
| load_dotenv() | |
| def install_playwright(): | |
| try: | |
| subprocess.run(["playwright", "--version"], check=True) | |
| except (subprocess.CalledProcessError, FileNotFoundError): | |
| print("Installing Playwright browsers...") | |
| try: | |
| subprocess.run([sys.executable, "-m", "playwright", "install", "chromium"], check=True) | |
| print("Playwright browsers installed.") | |
| except Exception as e: | |
| print(f"Failed to install Playwright browsers: {e}") | |
| # (Keep Constants as is) | |
| # --- Constants --- | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| def run_and_submit_all(profile: gr.OAuthProfile | None, *args): | |
| """ | |
| Fetches all questions, runs the SimpleAgent 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 = LangGraphAgent() | |
| 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}") | |
| response = None # Initialize response to None | |
| 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}") | |
| # Try to get more specific error information | |
| if isinstance(e, requests.exceptions.ConnectionError): | |
| return "Error fetching questions: Connection Error. Please check the API URL and your network connection.", None | |
| if isinstance(e, requests.exceptions.Timeout): | |
| return "Error fetching questions: Request timed out.", None | |
| if response: | |
| try: | |
| error_json = response.json() | |
| error_detail = error_json.get('detail', response.text) | |
| return f"Error fetching questions: {e} - {error_detail}", None | |
| except requests.exceptions.JSONDecodeError: | |
| return f"Error fetching questions: {e} - Could not decode JSON from response: {response.text[:500]}", None | |
| return f"Error fetching 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...") | |
| for item in questions_data: | |
| time.sleep(2) # Rate limit to avoid 429 errors | |
| 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, task_id=task_id) | |
| # Clean answer if agent included "FINAL ANSWER:" | |
| clean_answer = submitted_answer.replace("FINAL ANSWER:", "").strip() | |
| answers_payload.append({"task_id": task_id, "submitted_answer": clean_answer}) | |
| results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer}) # Log original | |
| 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 | |
| def test_agent(question: str): | |
| """ | |
| Runs the agent on a single question and returns the answer. | |
| """ | |
| if not question: | |
| return "Please enter a question." | |
| try: | |
| agent = LangGraphAgent() | |
| answer = agent(question) | |
| return answer | |
| except Exception as e: | |
| return f"Error running agent: {e}" | |
| # --- 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. | |
| """ | |
| ) | |
| login_button = 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, | |
| inputs=[login_button], | |
| outputs=[status_output, results_table] | |
| ) | |
| gr.Markdown("---") | |
| gr.Markdown("## Test the Agent") | |
| with gr.Row(): | |
| question_textbox = gr.Textbox(label="Enter your question") | |
| answer_textbox = gr.Textbox(label="Agent's Answer") | |
| test_button = gr.Button("Test Agent") | |
| test_button.click( | |
| fn=test_agent, | |
| inputs=[question_textbox], | |
| outputs=[answer_textbox] | |
| ) | |
| def export_results(df): | |
| if df is None or df.empty: | |
| return None | |
| file_path = "results.txt" | |
| with open(file_path, "w", encoding="utf-8") as f: | |
| for _, row in df.iterrows(): | |
| f.write(f"Task ID: {row.get('Task ID', 'N/A')}\n") | |
| f.write(f"Question: {row.get('Question', 'N/A')}\n") | |
| f.write(f"Answer: {row.get('Submitted Answer', 'N/A')}\n") | |
| f.write("-" * 40 + "\n") | |
| return file_path | |
| gr.Markdown("---") | |
| gr.Markdown("## Tools") | |
| export_button = gr.Button("Export Results to Text") | |
| file_output = gr.File(label="Download Results") | |
| export_button.click( | |
| fn=export_results, | |
| inputs=[results_table], | |
| outputs=[file_output] | |
| ) | |
| with gr.Tab("Diagnostics"): | |
| gr.Markdown("### Check Playwright") | |
| pw_btn = gr.Button("Test Playwright") | |
| pw_out = gr.Textbox(label="Result") | |
| def test_playwright_btn(): | |
| try: | |
| from langchain_community.tools.playwright.utils import create_sync_playwright_browser | |
| browser = create_sync_playwright_browser(headless=True) | |
| page = browser.new_page() | |
| page.goto("https://example.com") | |
| t = page.title() | |
| browser.close() | |
| return f"Success! Title: {t}" | |
| except ImportError: | |
| return "Playwright not installed/importable." | |
| except Exception as e: | |
| return f"Playwright Failed: {e}" | |
| pw_btn.click(test_playwright_btn, outputs=pw_out) | |
| if __name__ == "__main__": | |
| install_playwright() | |
| 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("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("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) | |