| import json |
| import os |
| import tempfile |
| from pathlib import Path |
|
|
| import gradio as gr |
| import pandas as pd |
| import requests |
|
|
| from agent import GaiaAgent |
| from answer_normalize import normalize_answer |
|
|
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" |
| CACHE_FILENAME = "gaia_answers_cache.json" |
|
|
|
|
| def _cache_path() -> Path: |
| return Path(__file__).resolve().parent / CACHE_FILENAME |
|
|
|
|
| def _load_cache() -> dict: |
| p = _cache_path() |
| if not p.is_file(): |
| return {} |
| try: |
| return json.loads(p.read_text(encoding="utf-8")) |
| except json.JSONDecodeError: |
| return {} |
|
|
|
|
| def _save_cache(cache: dict) -> None: |
| _cache_path().write_text(json.dumps(cache, indent=2), encoding="utf-8") |
|
|
|
|
| def _download_attachment(api_url: str, task_id: str, file_name: str) -> str | None: |
| """Save task attachment to a temp file; return path or None.""" |
| if not file_name or not str(file_name).strip(): |
| return None |
| url = f"{api_url}/files/{task_id}" |
| try: |
| r = requests.get(url, timeout=120) |
| except requests.RequestException: |
| return None |
| if r.status_code != 200: |
| return None |
| ctype = (r.headers.get("Content-Type") or "").lower() |
| if "application/json" in ctype: |
| try: |
| data = r.json() |
| if isinstance(data, dict) and data.get("detail"): |
| return None |
| except json.JSONDecodeError: |
| pass |
| suffix = Path(file_name).suffix or "" |
| fd, path = tempfile.mkstemp(suffix=suffix, prefix=f"gaia_{task_id[:8]}_") |
| try: |
| with os.fdopen(fd, "wb") as f: |
| f.write(r.content) |
| except OSError: |
| return None |
| return path |
|
|
|
|
| def run_and_submit_all(profile: gr.OAuthProfile | None): |
| space_id = os.getenv("SPACE_ID") |
| use_cache = os.getenv("GAIA_USE_CACHE", "1").lower() in ("1", "true", "yes") |
|
|
| 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 = os.getenv("GAIA_API_URL", DEFAULT_API_URL) |
| questions_url = f"{api_url}/questions" |
| submit_url = f"{api_url}/submit" |
|
|
| try: |
| agent = GaiaAgent() |
| 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) |
|
|
| print(f"Fetching questions from: {questions_url}") |
| try: |
| response = requests.get(questions_url, timeout=60) |
| 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 e: |
| return f"Error fetching questions: {e}", None |
| except json.JSONDecodeError as e: |
| return f"Error decoding server response for questions: {e}", None |
|
|
| cache = _load_cache() if use_cache else {} |
| 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") |
| file_name = item.get("file_name") or "" |
|
|
| if not task_id or question_text is None: |
| print(f"Skipping item with missing task_id or question: {item}") |
| continue |
|
|
| cache_key = str(task_id) |
| if use_cache and cache_key in cache: |
| submitted_answer = normalize_answer(cache[cache_key]) |
| print(f"Cache hit for {task_id}") |
| else: |
| local_path: str | None = None |
| try: |
| if file_name and str(file_name).strip(): |
| local_path = _download_attachment(api_url, str(task_id), str(file_name)) |
| if local_path: |
| print(f"Downloaded attachment for {task_id} -> {local_path}") |
| submitted_answer = agent( |
| str(question_text), |
| attachment_path=local_path, |
| task_id=str(task_id), |
| ) |
| submitted_answer = normalize_answer(submitted_answer) |
| if use_cache: |
| cache[cache_key] = ( |
| submitted_answer |
| if isinstance(submitted_answer, str) |
| else str(submitted_answer) |
| ) |
| _save_cache(cache) |
| except Exception as e: |
| print(f"Error running agent on task {task_id}: {e}") |
| submitted_answer = f"AGENT ERROR: {e}" |
| finally: |
| if local_path and Path(local_path).is_file(): |
| try: |
| Path(local_path).unlink(missing_ok=True) |
| except OSError: |
| pass |
|
|
| 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, |
| } |
| ) |
|
|
| if not answers_payload: |
| return "Agent did not produce any answers to submit.", pd.DataFrame(results_log) |
|
|
| 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) |
|
|
| print(f"Submitting {len(answers_payload)} answers to: {submit_url}") |
| try: |
| response = requests.post(submit_url, json=submission_data, timeout=600) |
| 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 json.JSONDecodeError: |
| error_detail += f" Response: {e.response.text[:500]}" |
| status_message = f"Submission Failed: {error_detail}" |
| print(status_message) |
| return status_message, pd.DataFrame(results_log) |
| except requests.exceptions.Timeout: |
| status_message = "Submission Failed: The request timed out." |
| print(status_message) |
| return status_message, pd.DataFrame(results_log) |
| except requests.exceptions.RequestException as e: |
| status_message = f"Submission Failed: Network error - {e}" |
| print(status_message) |
| return status_message, pd.DataFrame(results_log) |
| except Exception as e: |
| status_message = f"An unexpected error occurred during submission: {e}" |
| print(status_message) |
| return status_message, pd.DataFrame(results_log) |
|
|
|
|
| def crypto_btc_price() -> str: |
| """Optional demo: live BTC/USD (not used for GAIA scoring).""" |
| try: |
| r = requests.get( |
| "https://api.coingecko.com/api/v3/simple/price", |
| params={"ids": "bitcoin", "vs_currencies": "usd"}, |
| timeout=20, |
| ) |
| r.raise_for_status() |
| data = r.json() |
| usd = data.get("bitcoin", {}).get("usd") |
| return f"Bitcoin (BTC) ~ ${usd:,.2f} USD (CoinGecko public API)." |
| except Exception as e: |
| return f"Could not fetch price: {e}" |
|
|
|
|
| with gr.Blocks() as demo: |
| gr.Markdown("# GAIA Unit 4 — Agent Evaluation Runner") |
| gr.Markdown( |
| """ |
| **Instructions** |
| |
| 1. Duplicate this Space from the course template (or push this repo) and set **Secrets**: `HF_TOKEN` (read access to Inference). |
| 2. Optional env vars: `GAIA_TEXT_MODEL`, `GAIA_ASR_MODEL`, `GAIA_VISION_MODEL`, `GAIA_API_URL`, `GAIA_USE_CACHE` (default `1`). |
| 3. Log in with Hugging Face below (username is used for the leaderboard). |
| 4. Run **Evaluate & Submit** to answer all questions and post scores. |
| |
| Attachment tasks download `GET /files/{task_id}` automatically when `file_name` is set. |
| |
| --- |
| **Crypto demo (optional):** unrelated to GAIA; quick BTC spot check. |
| """ |
| ) |
|
|
| gr.LoginButton() |
|
|
| with gr.Tab("GAIA evaluation"): |
| run_button = gr.Button("Run Evaluation & Submit All Answers") |
| status_output = gr.Textbox( |
| label="Run Status / Submission Result", lines=6, 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], |
| ) |
|
|
| with gr.Tab("Crypto intelligence (demo)"): |
| gr.Markdown( |
| "This tab does not affect GAIA scores. It demonstrates a simple public market data fetch." |
| ) |
| cp_btn = gr.Button("Fetch BTC / USD") |
| cp_out = gr.Textbox(label="Output", interactive=False) |
| cp_btn.click(fn=crypto_btc_price, outputs=cp_out) |
|
|
| 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}") |
| else: |
| print("SPACE_HOST not set (local run?).") |
|
|
| 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("SPACE_ID not set (local run?).") |
|
|
| print("-" * 62 + "\n") |
| demo.launch(debug=True, share=False) |
|
|
