|
|
import os |
|
|
import gradio as gr |
|
|
import requests |
|
|
import inspect |
|
|
import pandas as pd |
|
|
from agent import run_agent_on_question |
|
|
|
|
|
|
|
|
|
|
|
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" |
|
|
|
|
|
|
|
|
def run_and_submit_all(profile: gr.OAuthProfile | None): |
|
|
""" |
|
|
Fetches all questions, runs your custom agent on them, submits all answers, |
|
|
and displays the results. |
|
|
""" |
|
|
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 |
|
|
questions_url = f"{api_url}/questions" |
|
|
submit_url = f"{api_url}/submit" |
|
|
|
|
|
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: |
|
|
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}") |
|
|
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 |
|
|
|
|
|
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", None) |
|
|
if not task_id or question_text is None: |
|
|
print(f"Skipping item with missing task_id or question: {item}") |
|
|
continue |
|
|
try: |
|
|
submitted_answer = run_agent_on_question({ |
|
|
"task_id": task_id, |
|
|
"question": question_text, |
|
|
"file_name": file_name |
|
|
}) |
|
|
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.") |
|
|
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=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 |
|
|
|
|
|
|
|
|
|
|
|
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. |
|
|
""" |
|
|
) |
|
|
|
|
|
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?). 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) |
|
|
|
