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# app.py
import os
import gradio as gr
import requests
import pandas as pd
from tools import AnswerTool
from smolagents import CodeAgent, OpenAIServerModel
from smolagents import DuckDuckGoSearchTool, WikipediaSearchTool
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
class BasicAgent:
def __init__(self):
# Initialize CodeAgent with GPT-4o, Wikipedia, DuckDuckGo, and AnswerTool
model = OpenAIServerModel(model_id="gpt-4o")
wiki_tool = WikipediaSearchTool()
web_tool = DuckDuckGoSearchTool()
answer_tool = AnswerTool()
self.agent = CodeAgent(
model=model,
# try wiki first, then web, then direct answer
tools=[wiki_tool, web_tool, answer_tool],
add_base_tools=True, # include python_eval, image_ocr, etc.
max_steps=3, # allow up to 3 planning steps
verbosity_level=0
)
def __call__(self, question: str) -> str:
return self.agent.run(question)
def run_and_submit_all(username):
if not username:
return "Please enter your Hugging Face username.", None
# 1. Fetch questions
try:
resp = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
if resp.status_code == 429:
return "Server rate limited the requests. Please wait a moment and try again.", None
resp.raise_for_status()
questions = resp.json()
except Exception as e:
return f"Error fetching questions: {e}", None
# 2. Run agent on all questions
agent = BasicAgent()
results = []
payload = []
for q in questions:
tid = q.get("task_id")
text = q.get("question")
if not (tid and text):
continue
try:
ans = agent(text)
except Exception as e:
ans = f"ERROR: {e}"
results.append({"Task ID": tid, "Question": text, "Answer": ans})
payload.append({"task_id": tid, "submitted_answer": ans})
if not payload:
return "Agent returned no answers.", pd.DataFrame(results)
# 3. Submit answers
submission = {
"username": username,
"agent_code": f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}/tree/main",
"answers": payload
}
try:
sub_resp = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=60)
sub_resp.raise_for_status()
data = sub_resp.json()
status = (
f"Submission Successful!\n"
f"User: {data.get('username')}\n"
f"Score: {data.get('score')}% ({data.get('correct_count')}/{data.get('total_attempted')})\n"
f"Message: {data.get('message')}"
)
except Exception as e:
status = f"Submission Failed: {e}"
return status, pd.DataFrame(results)
def test_random_question(username):
if not username:
return "Please enter your Hugging Face username.", ""
try:
q = requests.get(f"{DEFAULT_API_URL}/random-question", timeout=15).json()
question = q.get("question", "")
ans = BasicAgent()(question)
return question, ans
except Exception as e:
return f"Error during test: {e}", ""
# --- Gradio UI ---
with gr.Blocks() as demo:
gr.Markdown("# Basic Agent Evaluation Runner")
gr.Markdown(
"""
**Instructions:**
1. Enter your Hugging Face username.
2. Use **Test Random Question** to check a single question.
3. Use **Run Evaluation & Submit All Answers** to evaluate on all questions.
"""
)
username_input = gr.Textbox(label="Hugging Face Username", placeholder="your-username")
run_btn = gr.Button("Run Evaluation & Submit All Answers")
test_btn = gr.Button("Test Random Question")
status_out = gr.Textbox(label="Status / Result", lines=5, interactive=False)
table_out = gr.DataFrame(label="Full Results Table", wrap=True)
question_out = gr.Textbox(label="Random Question", lines=3, interactive=False)
answer_out = gr.Textbox(label="Agent Answer", lines=3, interactive=False)
run_btn.click(fn=run_and_submit_all, inputs=[username_input], outputs=[status_out, table_out])
test_btn.click(fn=test_random_question, inputs=[username_input], outputs=[question_out, answer_out])
if __name__ == "__main__":
demo.launch(debug=True, share=False)