tools.py

# Custom tools for smolagents GAIA agent
from __future__ import annotations
import contextlib
import io
import os
from typing import Any, Dict, List

from smolagents import Tool

# ---- 1. PythonRunTool ------------------------------------------------------
class PythonRunTool(Tool):
    name = "python_run"
    description = """
        Execute trusted Python code and return printed output + repr() of the last expression (or _result variable).
    """
    inputs = {
        "code": {"type": "string", "description": "Python code to execute", "required": True}
    }
    output_type = "string"

    def forward(self, code: str) -> str:
        buf, ns = io.StringIO(), {}
        last = None
        try:
            with contextlib.redirect_stdout(buf):
                exec(compile(code, "<agent-python>", "exec"), {}, ns)
            last = ns.get("_result", None)
        except Exception as e:
            raise RuntimeError(f"PythonRunTool error: {e}") from e
        out = buf.getvalue()
        return (out + (repr(last) if last is not None else "")).strip()

# ---- 2. ExcelLoaderTool ----------------------------------------------------
class ExcelLoaderTool(Tool):
    name = "load_spreadsheet"
    description = """
        Read .xlsx/.xls/.csv from disk and return rows as a list of dictionaries with string keys.
    """
    inputs = {
        "path": {
            "type": "string",
            "description": "Path to .csv/.xls/.xlsx file",
            "required": True
        },
        "sheet": {
            "type": "string",
            "description": "Sheet name or index (optional, required for Excel files only)",
            "required": False,
            "default": "",
            "nullable": True
        }
    }
    output_type = "array"

    def forward(self, path: str, sheet: str | int | None = None) -> List[Dict[str, Any]]:
        import pandas as pd
        if not os.path.isfile(path):
            raise FileNotFoundError(path)
        ext = os.path.splitext(path)[1].lower()
        # Handle empty string as None for sheet
        if sheet == "":
            sheet = None
        if ext == ".csv":
            df = pd.read_csv(path)
        else:
            df = pd.read_excel(path, sheet_name=sheet)
        records = [{str(k): v for k, v in row.items()} for row in df.to_dict(orient="records")]
        return records

# ---- 3. YouTubeTranscriptTool ---------------------------------------------
class YouTubeTranscriptTool(Tool):
    name = "youtube_transcript"
    description = """
        Return the subtitles of a YouTube URL using youtube-transcript-api.
    """
    inputs = {
        "url": {"type": "string", "description": "YouTube URL", "required": True},
        "lang": {"type": "string", "description": "Transcript language (default: en)", "required": False, "default": "en"}
    }
    output_type = "string"

    def forward(self, url: str, lang: str = "en") -> str:
        from urllib.parse import urlparse, parse_qs
        from youtube_transcript_api._api import YouTubeTranscriptApi
        vid = parse_qs(urlparse(url).query).get("v", [None])[0] or url.split("/")[-1]
        data = YouTubeTranscriptApi.get_transcript(vid, languages=[lang, "en", "en-US", "en-GB"])
        return " ".join(d["text"] for d in data).strip()

# ---- 4. AudioTranscriptionTool --------------------------------------------
class AudioTranscriptionTool(Tool):
    name = "transcribe_audio"
    description = """
        Transcribe an audio file with OpenAI Whisper, returns plain text."
    """
    inputs = {
        "path": {"type": "string", "description": "Path to audio file", "required": True},
        "model": {"type": "string", "description": "Model name for transcription (default: whisper-1)", "required": False, "default": "whisper-1"}
    }
    output_type = "string"

    def forward(self, path: str, model: str = "whisper-1") -> str:
        import openai
        if not os.path.isfile(path):
            raise FileNotFoundError(path)
        client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
        with open(path, "rb") as fp:
            transcript = client.audio.transcriptions.create(model=model, file=fp)
        return transcript.text.strip()

# ---- 5. SimpleOCRTool ------------------------------------------------------
class SimpleOCRTool(Tool):
    name = "image_ocr"
    description = """
        Return any text spotted in an image via pytesseract OCR.
    """
    inputs = {
        "path": {"type": "string", "description": "Path to image file", "required": True}
    }
    output_type = "string"

    def forward(self, path: str) -> str:
        from PIL import Image
        import pytesseract
        if not os.path.isfile(path):
            raise FileNotFoundError(path)
        return pytesseract.image_to_string(Image.open(path)).strip()

# ---------------------------------------------------------------------------
__all__ = [
    "PythonRunTool",
    "ExcelLoaderTool",
    "YouTubeTranscriptTool",
    "AudioTranscriptionTool",
    "SimpleOCRTool",
]