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import os
import logging
import gradio as gr
import requests
import pandas as pd
import openai
from openai import OpenAI
from smolagents import CodeAgent, DuckDuckGoSearchTool, tool
from smolagents.models import OpenAIServerModel
# --- Logging ---
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
logger = logging.getLogger(__name__)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
OPENAI_MODEL_ID = os.getenv("OPENAI_MODEL_ID", "gpt-4.1")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
if not OPENAI_API_KEY:
raise RuntimeError("Please set OPENAI_API_KEY in your Space secrets.")
# --- Configure OpenAI SDK (for tools if needed) ---
openai.api_key = OPENAI_API_KEY
client = OpenAI()
# --- Tools ---
@tool
def summarize_query(query: str) -> str:
"""
Reframes an unclear search query to improve relevance.
Args:
query (str): The original search query.
Returns:
str: A concise, improved version.
"""
return f"Summarize and reframe: {query}"
@tool
def wikipedia_search(page: str) -> str:
"""
Fetches the summary extract of an English Wikipedia page.
Args:
page (str): e.g. 'Mercedes_Sosa_discography'
Returns:
str: The page’s extract text.
"""
try:
url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{page}"
r = requests.get(url, timeout=10)
r.raise_for_status()
return r.json().get("extract", "")
except Exception as e:
logger.exception("Wikipedia lookup failed")
return f"Wikipedia error: {e}"
search_tool = DuckDuckGoSearchTool()
wiki_tool = wikipedia_search
summarize_tool = summarize_query
# --- ReACT Prompt ---
instruction_prompt = """
You are a ReACT agent with three tools:
• DuckDuckGoSearchTool(query: str)
• wikipedia_search(page: str)
• summarize_query(query: str)
Internally, for each question:
1. Thought: decide which tool to call.
2. Action: call the chosen tool.
3. Observation: record the result.
4. If empty/irrelevant:
Thought: retry with summarize_query + DuckDuckGoSearchTool.
Record new Observation.
5. Thought: integrate observations.
Finally, output your answer with the following template:
FINAL ANSWER: [YOUR FINAL ANSWER].
YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings.
If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise.
If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise.
If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
"""
# --- Build the Agent with OpenAIServerModel ---
model = OpenAIServerModel(
model_id=OPENAI_MODEL_ID,
api_key=OPENAI_API_KEY
)
smart_agent = CodeAgent(
tools=[search_tool, wiki_tool, summarize_tool],
model=model
)
# --- Gradio Wrapper ---
class BasicAgent:
def __init__(self):
logger.info("Initialized SmolAgent with OpenAI GPT-4.1")
def __call__(self, question: str) -> str:
if not question.strip():
return "AGENT ERROR: empty question"
prompt = instruction_prompt.strip() + "\n\nQUESTION: " + question.strip()
try:
return smart_agent.run(prompt)
except Exception as e:
logger.exception("Agent run error")
return f"AGENT ERROR: {e}"
# --- Submission Logic ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
if not profile:
return "Please log in to Hugging Face.", None
username = profile.username
space_id = os.getenv("SPACE_ID", "")
agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
agent = BasicAgent()
# fetch
try:
resp = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
resp.raise_for_status()
questions = resp.json() or []
except Exception as e:
logger.exception("Failed fetch")
return f"Error fetching questions: {e}", None
logs, payload = [], []
for item in questions:
tid = item.get("task_id")
q = item.get("question")
if not tid or not q:
continue
ans = agent(q)
logs.append({"Task ID": tid, "Question": q, "Submitted Answer": ans})
payload.append({"task_id": tid, "submitted_answer": ans})
if not payload:
return "Agent did not produce any answers.", pd.DataFrame(logs)
# submit
try:
post = requests.post(
f"{DEFAULT_API_URL}/submit",
json={"username": username, "agent_code": agent_code, "answers": payload},
timeout=60
)
post.raise_for_status()
result = post.json()
status = (
f"Submission Successful!\n"
f"User: {result.get('username')}\n"
f"Score: {result.get('score','N/A')}%\n"
f"({result.get('correct_count','?')}/"
f"{result.get('total_attempted','?')})\n"
f"Message: {result.get('message','')}"
)
return status, pd.DataFrame(logs)
except Exception as e:
logger.exception("Submit failed")
return f"Submission Failed: {e}", pd.DataFrame(logs)
# --- Gradio App ---
with gr.Blocks() as demo:
gr.Markdown("# SmolAgent GAIA Runner 🚀")
gr.Markdown("""
**Instructions:**
1. Clone this space.
2. In Settings → Secrets, add `OPENAI_API_KEY` and (optionally) `OPENAI_MODEL_ID`.
3. Log in to Hugging Face.
4. Click **Run Evaluation & Submit All Answers**.
""")
gr.LoginButton()
btn = gr.Button("Run Evaluation & Submit All Answers")
out_status = gr.Textbox(label="Status", lines=5, interactive=False)
out_table = gr.DataFrame(label="Questions & Answers", wrap=True)
btn.click(run_and_submit_all, outputs=[out_status, out_table])
if __name__ == "__main__":
demo.launch(debug=True, share=False)
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