app.py

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)