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import os
import gradio as gr
import requests
import string
import warnings
import pandas as pd
import re
from huggingface_hub import login
from groq import Groq
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
# --- Basic Agent Definition ---
class BasicAgent:
def __init__(self):
print("BasicAgent initialized.")
self.client = Groq(api_key=os.environ.get("GROQ_API_KEY", ""))
self.agent_prompt = (
"""You are a general AI assistant. I will ask you a question. Report your thoughts, and
finish 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."""
)
def format_final_answer(self, answer: str) -> str:
cleaned = " ".join(answer.split())
return f"FINAL ANSWER: {cleaned}"
def check_commutativity(self):
S = ['a', 'b', 'c', 'd', 'e']
counter_example_elements = set()
index = {'a': 0, 'b': 1, 'c': 2, 'd': 3, 'e': 4}
self.operation_table = [
['a', 'b', 'c', 'b', 'd'],
['b', 'c', 'a', 'e', 'c'],
['c', 'a', 'b', 'b', 'a'],
['b', 'e', 'b', 'e', 'd'],
['d', 'b', 'a', 'd', 'c']
]
for x in S:
for y in S:
x_idx = index[x]
y_idx = index[y]
if self.operation_table[x_idx][y_idx] != self.operation_table[y_idx][x_idx]:
counter_example_elements.add(x)
counter_example_elements.add(y)
return self.format_final_answer(", ".join(sorted(counter_example_elements)))
def maybe_reversed(self, text: str) -> bool:
words = text.split()
reversed_ratio = sum(
1 for word in words if word[::-1].lower() in {
"if", "you", "understand", "this", "sentence", "write",
"opposite", "of", "the", "word", "left", "answer"
}
) / len(words)
return reversed_ratio > 0.3
def solve_riddle(self, question: str) -> str:
question = question[::-1]
if "opposite of the word" in question:
match = re.search(r"opposite of the word ['\"](\w+)['\"]", question)
if match:
word = match.group(1).lower()
opposites = {
"left": "right", "up": "down", "hot": "cold",
"true": "false", "yes": "no", "black": "white"
}
opposite = opposites.get(word, f"UNKNOWN_OPPOSITE_OF_{word}")
return f"FINAL ANSWER: {opposite.upper()}"
return self.format_final_answer("COULD_NOT_SOLVE")
def query_groq(self, question: str) -> str:
full_prompt = f"{self.agent_prompt}\n\nQuestion: {question}"
try:
response = self.client.chat.completions.create(
model="llama3-8b-8192",
messages=[{"role": "user", "content": full_prompt}]
)
answer = response.choices[0].message.content
print(f"[Groq Raw Response]: {answer}")
if "FINAL ANSWER: " in answer:
return answer.split("FINAL ANSWER: ")[-1].strip().upper()
else:
return self.format_final_answer(answer).upper()
except Exception as e:
print(f"[Groq ERROR]: {e}")
return self.format_final_answer("GROQ_ERROR")
def __call__(self, question: str) -> str:
print(f"Received question: {question[:50]}...")
if "commutative" in question.lower():
return self.check_commutativity()
if self.maybe_reversed(question):
print("Detected likely reversed riddle.")
return self.solve_riddle(question)
return self.query_groq(question)
def run_and_submit_all(profile: gr.OAuthProfile | None):
space_id = os.getenv("SPACE_ID")
if profile:
username = f"{profile.username}"
print("User logged in.")
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"
try:
agent = BasicAgent()
except Exception as e:
return f"Error initializing agent: {e}", None
agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
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 requests.exceptions.RequestException 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")
gold_answer = item.get("answer") or item.get("ground_truth")
if not task_id or question_text is None:
continue
try:
submitted_answer = agent(question_text)
print(f"Q: {question_text}")
print(f"Predicted: {submitted_answer} | Gold: {gold_answer}")
answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
results_log.append({
"Task ID": task_id,
"Question": question_text,
"Gold Answer": gold_answer,
"Submitted Answer": submitted_answer
})
except Exception as e:
results_log.append({
"Task ID": task_id,
"Question": question_text,
"Gold Answer": gold_answer,
"Submitted Answer": f"AGENT ERROR: {e}"
})
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
}
try:
response = requests.post(submit_url, json=submission_data, timeout=60)
response.raise_for_status()
result_data = response.json()
print(result_data)
final_status = (
f"Submission Successful!\n"
f"User: {result_data.get('username')}\n"
f"Overall Score: {result_data.get('score', '?')}% "
f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
f"Message: {result_data.get('message', 'No message received.')}"
)
return final_status, pd.DataFrame(results_log)
except Exception as e:
return f"Submission Failed: {e}", pd.DataFrame(results_log)
# --- Build Gradio Interface ---
with gr.Blocks() as demo:
gr.Markdown("# Basic Agent Evaluation Runner")
gr.LoginButton()
run_button = gr.Button("Run Evaluation & Submit All Answers")
status_output = gr.Textbox(label="Run Status / Submission Result", max_lines=5, interactive=False, max_length=200)
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("Launching Gradio Interface for Basic Agent Evaluation...")
demo.launch(debug=True, share=False)
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