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import os
import gradio as gr
import requests
import inspect
import pandas as pd
import asyncio
from smolagents import ToolCallingAgent, InferenceClientModel, HfApiModel
from smolagents import DuckDuckGoSearchTool, Tool, CodeAgent
from huggingface_hub import login
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
login(token=os.environ["HUGGINGFACEHUB_API_TOKEN"])
search_tool = DuckDuckGoSearchTool()
async def run_and_submit_all(profile: gr.OAuthProfile | None):
try:
agent = CodeAgent(
tools=[search_tool],
model=InferenceClientModel(model="mistralai/Magistral-Small-2506"),
max_steps=5,
verbosity_level=2
)
except Exception as e:
return f"Error initializing agent: {e}", None
space_id = os.getenv("SPACE_ID")
agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
questions_url = f"{DEFAULT_API_URL}/questions"
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
except Exception as e:
return f"Error fetching questions: {e}", None
results_log = []
answers_payload = []
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:
continue
try:
loop = asyncio.get_event_loop()
submitted_answer = await loop.run_in_executor(None, 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:
results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
if not answers_payload:
return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
username = profile.username if profile else "unknown"
submit_url = f"{DEFAULT_API_URL}/submit"
submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
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 Exception as e:
status_message = f"Submission Failed: {e}"
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. Clone this space and define your agent logic.
2. Log in to your Hugging Face account.
3. Click 'Run Evaluation & Submit All Answers'.
---
**Note:**
The run may take time. Async is now used to improve responsiveness.
""")
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: https://{space_host_startup}.hf.space")
if space_id_startup:
print(f"✅ SPACE_ID: https://huggingface.co/spaces/{space_id_startup}")
print("Launching Gradio Interface...")
demo.launch(debug=True, share=False)
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