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| import os | |
| import tempfile | |
| from pathlib import Path | |
| import gradio as gr | |
| import pandas as pd | |
| import requests | |
| from dotenv import load_dotenv | |
| from agent import GaiaAgent | |
| from eval.progress import log_batch_done, log_batch_start, log_question_done, log_question_start | |
| from file_resolver import resolve_task_attachment | |
| load_dotenv() | |
| # --- Constants --- | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| IS_HF_SPACE = bool(os.getenv("SPACE_ID")) | |
| DEFAULT_SPACE_ID = os.getenv("HF_SPACE_ID", "ken2ki/Final_Assignment_Template") | |
| def resolve_username(profile: gr.OAuthProfile | None) -> str | None: | |
| if profile: | |
| return profile.username.strip() | |
| local_username = os.getenv("HF_USERNAME") | |
| if local_username: | |
| return local_username.strip() | |
| return None | |
| def run_and_submit_all(profile: gr.OAuthProfile | None = None): | |
| """Fetch all questions, run the GAIA agent, submit answers, and display results.""" | |
| username = resolve_username(profile) | |
| if not username: | |
| if IS_HF_SPACE: | |
| return "Please log in to Hugging Face with the button above.", None | |
| return ( | |
| "Local mode: add HF_USERNAME=your_hf_username to your .env file, then retry.", | |
| None, | |
| ) | |
| print(f"Running as user: {username}") | |
| space_id = os.getenv("SPACE_ID") or DEFAULT_SPACE_ID | |
| api_url = DEFAULT_API_URL | |
| questions_url = f"{api_url}/questions" | |
| submit_url = f"{api_url}/submit" | |
| try: | |
| agent = GaiaAgent() | |
| except Exception as error: | |
| print(f"Error instantiating agent: {error}") | |
| return f"Error initializing agent: {error}", None | |
| agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" | |
| print(agent_code) | |
| 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: | |
| return "Fetched questions list is empty or invalid format.", None | |
| print(f"Fetched {len(questions_data)} questions.") | |
| except requests.exceptions.RequestException as error: | |
| return f"Error fetching questions: {error}", None | |
| except requests.exceptions.JSONDecodeError as error: | |
| return f"Error decoding server response for questions: {error}", None | |
| except Exception as error: | |
| return f"An unexpected error occurred fetching questions: {error}", None | |
| valid_questions = [ | |
| item | |
| for item in questions_data | |
| if item.get("task_id") and item.get("question") is not None | |
| ] | |
| total = len(valid_questions) | |
| log_batch_start(total) | |
| results_log = [] | |
| answers_payload = [] | |
| succeeded = 0 | |
| with tempfile.TemporaryDirectory(prefix="gaia_files_") as temp_dir: | |
| download_dir = Path(temp_dir) | |
| for index, item in enumerate(valid_questions, start=1): | |
| task_id = item["task_id"] | |
| question_text = item["question"] | |
| file_name = item.get("file_name") or "" | |
| log_question_start(index, total, question_text, task_id) | |
| file_path = None | |
| file_error = None | |
| try: | |
| if file_name: | |
| file_path, file_error = resolve_task_attachment( | |
| api_url, task_id, file_name, download_dir | |
| ) | |
| submitted_answer = agent( | |
| question_text, | |
| file_path=file_path, | |
| file_error=file_error, | |
| task_id=task_id, | |
| ) | |
| 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, | |
| } | |
| ) | |
| log_question_done(index, total, submitted_answer) | |
| succeeded += 1 | |
| except Exception as error: | |
| print(f"Error running agent on task {task_id}: {error}") | |
| results_log.append( | |
| { | |
| "Task ID": task_id, | |
| "Question": question_text, | |
| "Submitted Answer": f"AGENT ERROR: {error}", | |
| } | |
| ) | |
| log_question_done(index, total, "", error=str(error)) | |
| log_batch_done(total, succeeded) | |
| if not answers_payload: | |
| return "Agent did not produce any answers to submit.", pd.DataFrame(results_log) | |
| submission_data = { | |
| "username": username, | |
| "agent_code": agent_code, | |
| "answers": answers_payload, | |
| } | |
| print( | |
| f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..." | |
| ) | |
| 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.')}" | |
| ) | |
| results_df = pd.DataFrame(results_log) | |
| return final_status, results_df | |
| except requests.exceptions.HTTPError as error: | |
| error_detail = f"Server responded with status {error.response.status_code}." | |
| try: | |
| error_json = error.response.json() | |
| error_detail += f" Detail: {error_json.get('detail', error.response.text)}" | |
| except requests.exceptions.JSONDecodeError: | |
| error_detail += f" Response: {error.response.text[:500]}" | |
| return f"Submission Failed: {error_detail}", pd.DataFrame(results_log) | |
| except requests.exceptions.Timeout: | |
| return "Submission Failed: The request timed out.", pd.DataFrame(results_log) | |
| except requests.exceptions.RequestException as error: | |
| return f"Submission Failed: Network error - {error}", pd.DataFrame(results_log) | |
| except Exception as error: | |
| return f"An unexpected error occurred during submission: {error}", pd.DataFrame( | |
| results_log | |
| ) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# GAIA Agent Evaluation Runner") | |
| if IS_HF_SPACE: | |
| instructions = """ | |
| **Instructions (HF Space):** | |
| 1. Add **`GROQ_API_KEY`** in Space Settings → Secrets (free at [console.groq.com](https://console.groq.com)). | |
| 2. Optional **`CEREBRAS_API_KEY`** and **`GOOGLE_API_KEY`** — agent rotates to them when Groq limits hit. | |
| 3. Optional **`GROQ_MODEL`** — if unset, Space uses Scout 17B. On hard limits the agent rotates through models and providers automatically. | |
| 4. Optional: **`GROQ_MIN_REQUEST_INTERVAL=5`** — pause between API calls to reduce 429 errors. | |
| 5. Log in with Hugging Face, then click **Run Evaluation & Submit All Answers**. | |
| **No Groq key?** Run locally instead: `python run_local.py --mode score` with Ollama. | |
| """ | |
| else: | |
| instructions = """ | |
| **Local mode** | |
| 1. Create a `.env` file with `HF_TOKEN` and `HF_USERNAME=ken2ki`. | |
| 2. Activate the project venv: `source .venv/bin/activate` | |
| 3. Click **Run Evaluation & Submit All Answers** (no HF login needed locally). | |
| """ | |
| gr.Markdown( | |
| instructions | |
| + """ | |
| Your agent uses `smolagents` with web search, Wikipedia, file reading, audio transcription, | |
| image analysis, and Python code execution. | |
| """ | |
| ) | |
| if IS_HF_SPACE: | |
| 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}") | |
| if space_id_startup: | |
| print(f"SPACE_ID found: {space_id_startup}") | |
| print(f"Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main") | |
| else: | |
| print("Running locally (SPACE_ID not set).") | |
| print("Use HF_TOKEN and HF_USERNAME in .env for evaluation runs.") | |
| demo.launch(debug=True, share=False) | |