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| import os | |
| import gradio as gr | |
| import requests | |
| import string | |
| import warnings | |
| import pandas as pd | |
| from huggingface_hub import login | |
| import re | |
| import json | |
| 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["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 "FINAL ANSWER: RIGHT" | |
| 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 | |
| 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) | |
| # --- Answer Scoring --- | |
| def question_scorer(model_answer: str, ground_truth: str) -> bool: | |
| def normalize_str(input_str, remove_punct=True) -> str: | |
| no_spaces = re.sub(r"\s", "", input_str) | |
| if remove_punct: | |
| translator = str.maketrans("", "", string.punctuation) | |
| return no_spaces.lower().translate(translator) | |
| else: | |
| return no_spaces.lower() | |
| def normalize_number_str(number_str: str) -> float | None: | |
| for char in ["$", "%", ","]: | |
| number_str = number_str.replace(char, "") | |
| try: | |
| return float(number_str) | |
| except ValueError: | |
| print(f"String '{number_str}' cannot be normalized to number.") | |
| return None | |
| def split_string(s: str, char_list: list[str] = [",", ";"]) -> list[str]: | |
| pattern = f"[{''.join(map(re.escape, char_list))}]" | |
| return [elem.strip() for elem in re.split(pattern, s)] | |
| def is_float(val) -> bool: | |
| try: | |
| float(val) | |
| return True | |
| except ValueError: | |
| return False | |
| if model_answer is None: | |
| model_answer = "None" | |
| if is_float(ground_truth): | |
| print(f"Evaluating '{model_answer}' as a number.") | |
| normalized = normalize_number_str(model_answer) | |
| return normalized == float(ground_truth) if normalized is not None else False | |
| elif any(char in ground_truth for char in [",", ";"]): | |
| print(f"Evaluating '{model_answer}' as a comma/semicolon-separated list.") | |
| gt_elems = split_string(ground_truth) | |
| ma_elems = split_string(model_answer) | |
| if len(gt_elems) != len(ma_elems): | |
| warnings.warn("Answer lists have different lengths, returning False.", UserWarning) | |
| return False | |
| for ma_elem, gt_elem in zip(ma_elems, gt_elems): | |
| if is_float(gt_elem): | |
| normalized = normalize_number_str(ma_elem) | |
| if normalized != float(gt_elem): | |
| return False | |
| else: | |
| if normalize_str(ma_elem, remove_punct=False) != normalize_str(gt_elem, remove_punct=False): | |
| return False | |
| return True | |
| else: | |
| print(f"Evaluating '{model_answer}' as a string.") | |
| return normalize_str(model_answer) == normalize_str(ground_truth) | |
| # --- Run and Submit All --- | |
| 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 = [] | |
| correct_count = 0 | |
| total_with_gold = 0 | |
| for item in questions_data: | |
| task_id = item.get("task_id") | |
| question_text = item.get("question") | |
| gold_answer = item.get("gold_answer") | |
| if not task_id or question_text is None: | |
| continue | |
| try: | |
| submitted_answer = agent(question_text) | |
| is_correct = question_scorer(submitted_answer, gold_answer) if gold_answer else None | |
| if is_correct is not None: | |
| total_with_gold += 1 | |
| if is_correct: | |
| correct_count += 1 | |
| 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, | |
| "Gold Answer": gold_answer, | |
| "Correct?": "✅" if is_correct else "❌" if is_correct is not None else "N/A" | |
| }) | |
| except Exception as e: | |
| results_log.append({ | |
| "Task ID": task_id, | |
| "Question": question_text, | |
| "Submitted Answer": f"AGENT ERROR: {e}", | |
| "Gold Answer": gold_answer, | |
| "Correct?": "❌" | |
| }) | |
| 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) | |
| accuracy_text = "" | |
| if total_with_gold > 0: | |
| accuracy = (correct_count / total_with_gold) * 100 | |
| accuracy_text = f"\nLocal Accuracy: {accuracy:.2f}% ({correct_count}/{total_with_gold} correct)" | |
| final_status = ( | |
| f"Submission Successful!\n" | |
| f"User: {result_data.get('username')}\n" | |
| f"Overall Score (from server): {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.')}" | |
| f"{accuracy_text}" | |
| ) | |
| 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) |