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| import os | |
| # Disable Hugging Face login for local execution | |
| os.environ["DISABLE_HF_LOGIN"] = "1" | |
| import gradio as gr | |
| import pandas as pd | |
| import requests | |
| from dotenv import load_dotenv | |
| from agent import BasicAgent, agent_graph_mermaid | |
| from agent.graph import agent_graph_png_base64 | |
| load_dotenv() | |
| # Generate the agent graph visualizations | |
| try: | |
| GRAPH_MERMAID = agent_graph_mermaid() | |
| except Exception as exc: | |
| GRAPH_MERMAID = f"Error generating graph diagram: {exc}" | |
| GRAPH_PNG_BASE64 = agent_graph_png_base64() | |
| def _env_flag(name: str, default: bool = False) -> bool: | |
| value = os.getenv(name) | |
| if value is None: | |
| return default | |
| return value.strip().lower() in {"1", "true", "yes", "on"} | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| _SPACE_ENV_CONFIGURED = bool(os.getenv("SPACE_ID") or os.getenv("SPACE_HOST")) | |
| FORCE_LOCAL_MODE = _env_flag("FORCE_LOCAL_MODE") or _env_flag("DISABLE_HF_LOGIN") or _env_flag("GRADIO_FORCE_LOCAL") | |
| RUNNING_IN_SPACE = _SPACE_ENV_CONFIGURED and not FORCE_LOCAL_MODE | |
| def run_and_submit_all(profile: gr.OAuthProfile | None = None, username: str | None = 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 and getattr(profile, "username", None): | |
| username = f"{profile.username}".strip() | |
| print(f"User logged in via OAuth: {username}") | |
| elif username: | |
| username = username.strip() | |
| print(f"Using provided username: {username}") | |
| else: | |
| env_username = (os.getenv("HF_USERNAME") or os.getenv("LOCAL_HF_USERNAME") or "").strip() | |
| if env_username: | |
| username = env_username | |
| print(f"Using username from environment: {username}") | |
| else: | |
| print("User not logged in and no username supplied.") | |
| return ( | |
| "Please login to Hugging Face, provide a username locally, or set the `HF_USERNAME`/`LOCAL_HF_USERNAME` environment variable.", | |
| 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.") | |
| # Debug: Print first question structure to understand format | |
| if questions_data and len(questions_data) > 0: | |
| print(f"\n🔍 DEBUG - First question structure:") | |
| print(f"Keys: {list(questions_data[0].keys())}") | |
| if len(questions_data[0].keys()) > 2: | |
| print(f"Sample: {str(questions_data[0])[:300]}...\n") | |
| 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...") | |
| for item in questions_data: | |
| 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 | |
| # Check for attached files and download them | |
| file_name = item.get("file_name", "") | |
| if file_name and file_name.strip(): | |
| try: | |
| from pathlib import Path | |
| # Create downloads directory if it doesn't exist | |
| download_dir = Path("downloads") | |
| download_dir.mkdir(exist_ok=True) | |
| # Construct file download URL | |
| file_url = f"{api_url}/files/{task_id}" # Note: /files/ (plural) | |
| print(f"📥 Downloading file: {file_name} from {file_url}") | |
| # Download the file | |
| file_response = requests.get(file_url, timeout=30) | |
| file_response.raise_for_status() | |
| # Save file to downloads directory | |
| filepath = download_dir / file_name | |
| with open(filepath, 'wb') as f: | |
| f.write(file_response.content) | |
| print(f"✅ Saved file: {filepath} ({len(file_response.content)} bytes)") | |
| except Exception as e: | |
| print(f"⚠️ Error downloading file {file_name}: {e}") | |
| 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.") | |
| results_df = pd.DataFrame(results_log, columns=["Task ID", "Question", "Submitted Answer"]).fillna("") | |
| return "Agent did not produce any answers to submit.", results_df | |
| # 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, columns=["Task ID", "Question", "Submitted Answer"]).fillna("") | |
| 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, columns=["Task ID", "Question", "Submitted Answer"]).fillna("") | |
| 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, columns=["Task ID", "Question", "Submitted Answer"]).fillna("") | |
| 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, columns=["Task ID", "Question", "Submitted Answer"]).fillna("") | |
| 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, columns=["Task ID", "Question", "Submitted Answer"]).fillna("") | |
| return status_message, results_df | |
| # --- Build Gradio Interface using Blocks --- | |
| def run_and_submit_all_local(username_input: str | None): | |
| return run_and_submit_all(profile=None, username=username_input) | |
| 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. | |
| """ | |
| ) | |
| # Display the agent graph | |
| gr.Markdown("## Agent Flow Graph") | |
| gr.Markdown("This diagram shows how your agent processes questions using LangGraph:") | |
| if GRAPH_PNG_BASE64: | |
| gr.HTML( | |
| f'<img src="data:image/png;base64,{GRAPH_PNG_BASE64}" alt="Agent Flow Graph" style="max-width:100%;height:auto;border:1px solid #ddd;border-radius:4px;padding:10px;"/>' | |
| ) | |
| else: | |
| gr.Markdown("*Graph visualization not available. The agent is still functional.*") | |
| login_button = None | |
| username_box = None | |
| if RUNNING_IN_SPACE: | |
| login_button = gr.LoginButton() | |
| else: | |
| username_box = gr.Textbox( | |
| label="Hugging Face Username", | |
| placeholder="Enter the username to associate with your submission", | |
| value=(os.getenv("HF_USERNAME") or os.getenv("LOCAL_HF_USERNAME") or ""), | |
| ) | |
| 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", | |
| value=pd.DataFrame(columns=["Task ID", "Question", "Submitted Answer"]), | |
| interactive=False, | |
| wrap=True, | |
| ) | |
| if RUNNING_IN_SPACE: | |
| run_button.click( | |
| fn=run_and_submit_all, | |
| inputs=[login_button], | |
| outputs=[status_output, results_table], | |
| ) | |
| else: | |
| run_button.click( | |
| fn=run_and_submit_all_local, | |
| inputs=[username_box], | |
| 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) |