utils.py

import re
import json
from typing import List, Union, Optional


def extract_final_answer(output: str) -> str:
    """
    Extracts the text after 'FINAL ANSWER:' in the model's output.
    Strips whitespace and ensures clean formatting.
    If the answer is a comma-separated list, ensures a space after each comma.
    """
    output = str(output)
    marker = "FINAL ANSWER:"
    lower_output = output.lower()

    if marker.lower() in lower_output:
        # Find actual case version in original output (for safety)
        idx = lower_output.rfind(marker.lower())
        raw_answer = output[idx + len(marker) :].strip()

        # Normalize comma-separated lists: ensure single space after commas
        cleaned_answer = re.sub(r",\s*", ", ", raw_answer)
        return cleaned_answer

    return output


def replace_tool_mentions(prompt: str) -> str:
    # Replace tool mentions in backticks: `search` -> `web_search`, `wiki` -> `wikipedia_search`
    prompt = re.sub(r"(?<!\w)`search`(?!\w)", "`web_search`", prompt)
    prompt = re.sub(r"(?<!\w)`wiki`(?!\w)", "`wikipedia_search`", prompt)

    # Replace function calls: search(...) -> web_search(...), wiki(...) -> wikipedia_search(...)
    # This ensures we only catch function calls (not words like arxiv_search)
    prompt = re.sub(r"(?<!\w)(?<!_)search\(", "web_search(", prompt)
    prompt = re.sub(r"(?<!\w)(?<!_)wiki\(", "wikipedia_search(", prompt)

    return prompt

def _question_matches(question: str, filters: Union[str, List[str]]) -> bool:
    """Helper: check if question matches any string in filters."""
    if isinstance(filters, str):
        filters = [filters]
    return any(f.lower() in question.lower() for f in filters)

def load_online_qas(
    qa_type: Union[str, List[str]] = "all",
    has_file: Optional[bool] = None,
    file_path = "Final_Assignment_Template/allqas.jsonl"
) -> List[dict]:
    """
    Load online QAs from example_gaiaqa.json.

    Parameters:
    - qa_type: str or List[str], used to match substrings in the Question. Use "all" for no filtering.
    - has_file: bool or None, filters QAs by presence of 'file_name':
        - True: only include QAs with file_name
        - False: only include QAs without file_name
        - None: no file_name filtering
    - file_path: a path

    """
    data = []
    with open(file_path ,"r") as f:
        for line in f:
            entry = json.loads(line)
            data.append(entry)

    # Apply file presence filter
    if has_file is True:
        data = [qa for qa in data if qa.get("file_name", "").strip()]
    elif has_file is False:
        data = [qa for qa in data if not qa.get("file_name", "").strip()]

    # Apply question content filter
    if qa_type == "all":
        return data

    return [qa for qa in data if _question_matches(qa.get("Question", ""), qa_type)]


def load_test_qas(qa_type: Union[str, List[str]] = "all") -> List[dict]:
    """Loads test QAs with no attached files. Optionally filters by topic keywords in questions."""
    test_docs = []
    with open("Final_Assignment_Template/gaia_val.jsonl", "r") as f:
        for line in f:
            entry = json.loads(line)
            if entry.get("file_name", "").strip() == "":
                test_docs.append(entry)

    if qa_type == "all":
        return [
            {
                "Question": e["Question"],
                "Final answer": e.get("Final answer"),
                "task_id": e["task_id"],
                "tools": e.get("Annotator Metadata", {}).get("Tools"),
                "file_name": e.get("file_name", "")
            }
            for e in test_docs
        ]

    return [
        {
            "Question": e["Question"],
            "Final answer": e.get("Final answer"),
            "task_id": e["task_id"],
            "tools": e.get("Annotator Metadata", {}).get("Tools"),
            "file_name": e.get("file_name", "")
        }
        for e in test_docs
        if _question_matches(e["Question"], qa_type)
    ]


def load_val_qas(qa_type: Union[str, List[str]] = "all") -> List[dict]:
    """Loads validation QAs with no attached files. Optionally filters by topic keywords in questions."""
    val_docs = []
    with open("Final_Assignment_Template/gaia_val.jsonl", "r") as f:
        for line in f:
            entry = json.loads(line)
            if entry.get("file_name", "").strip() == "":
                val_docs.append(entry)

    if qa_type == "all":
        return [
            {
                "Question": e["Question"],
                "Final answer": e.get("Final answer"),
                "task_id": e["task_id"],
                "tools": e.get("Annotator Metadata", {}).get("Tools"),
                "file_name": e.get("file_name", "")
            }
            for e in val_docs
        ]

    return [
        {
            "Question": e["Question"],
            "Final answer": e.get("Final answer"),
            "task_id": e["task_id"],
            "tools": e.get("Annotator Metadata", {}).get("Tools"),
            "file_name": e.get("file_name", "")
        }
        for e in val_docs
        if _question_matches(e["Question"], qa_type)
    ]
# import requests
# import json

# def fetch_and_save_questions(api_base_url: str, output_path: str):
#     """
#     Fetch all questions from the Agent Evaluation API and save them as JSONL.
    
#     :param api_base_url: Base URL of the scoring API, e.g. "https://agents-course-unit4-scoring.hf.space"
#     :param output_path:  Path to the output .jsonl file
#     """
#     endpoint = f"{api_base_url}/questions"
#     try:
#         resp = requests.get(endpoint, timeout=30)
#         resp.raise_for_status()
#         questions = resp.json()
#     except Exception as e:
#         print(f"❌ Failed to fetch questions: {e}")
#         return

#     try:
#         with open(output_path, "w", encoding="utf-8") as fout:
#             for q in questions:
#                 fout.write(json.dumps(q, ensure_ascii=False) + "\n")
#         print(f"✅ Saved {len(questions)} questions to {output_path}")
#     except Exception as e:
#         print(f"❌ Failed to write JSONL file: {e}")

# API_BASE = "https://agents-course-unit4-scoring.hf.space"
# OUTPUT_FILE = "questions.jsonl"
# fetch_and_save_questions(API_BASE, OUTPUT_FILE)


# dlf = DownloadFileFromTaskTool()
# for res in results:
#     res = dlf.forward(task_id = res["task_id"])
#     print(res)
# task_id = "cca530fc-4052-43b2-b130-b30968d8aa44"
# file_url = f"https://agents-course-unit4-scoring.hf.space/files/{task_id}"
# response = requests.get(file_url, timeout=15)

# print(response.content)
# print(response.headers.get("content-type", "").lower())
#print(response.headers)