tools.py

import tempfile
import requests
import os

from time import sleep
from urllib.parse import urlparse
from typing import Optional, List
import yt_dlp
import imageio

from PIL import Image
from dotenv import load_dotenv
from smolagents import tool
import whisper

load_dotenv()

@tool
def use_vision_model(question: str, images: List[Image.Image]) -> str:
    """
    Use a Vision Model to answer a question about a set of images. 
    This stub exists for potential future Gemini or Mistral multimodal integration.
    """
    return "Vision model is not available for Mistral. Please integrate a separate endpoint for image analysis."

@tool
def review_youtube_video(url: str, question: str) -> str:
    return "This tool is currently unsupported with Mistral. Please remove or replace."

@tool
def youtube_frames_to_images(url: str, sample_interval_seconds: int = 5) -> List[Image.Image]:
    with tempfile.TemporaryDirectory() as tmpdir:
        ydl_opts = {
            'format': 'bestvideo[height<=1080]+bestaudio/best[height<=1080]/best',
            'outtmpl': os.path.join(tmpdir, 'video.%(ext)s'),
            'quiet': True,
            'noplaylist': True,
            'merge_output_format': 'mp4',
            'force_ipv4': True,
        }
        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            info = ydl.extract_info(url, download=True)

        video_path = next((os.path.join(tmpdir, f) for f in os.listdir(tmpdir) if f.endswith('.mp4')), None)
        reader = imageio.get_reader(video_path)
        fps = reader.get_meta_data().get('fps')
        frame_interval = int(fps * sample_interval_seconds)
        images = [Image.fromarray(frame) for idx, frame in enumerate(reader) if idx % frame_interval == 0]
        reader.close()
        return images

@tool
def read_file(filepath: str) -> str:
    try:
        with open(filepath, 'r', encoding='utf-8') as file:
            return file.read()
    except Exception as e:
        return f"Error reading file: {str(e)}"

@tool
def download_file_from_url(url: str, filename: Optional[str] = None) -> str:
    try:
        if not filename:
            filename = os.path.basename(urlparse(url).path) or f"download_{os.urandom(4).hex()}"
        filepath = os.path.join(tempfile.gettempdir(), filename)
        response = requests.get(url)
        with open(filepath, 'wb') as f:
            f.write(response.content)
        return filepath
    except Exception as e:
        return f"Error downloading file: {str(e)}"

@tool
def extract_text_from_image(image_path: str) -> str:
    try:
        import pytesseract
        return pytesseract.image_to_string(Image.open(image_path))
    except Exception as e:
        return f"Error extracting text: {str(e)}"

@tool
def analyze_csv_file(file_path: str, query: str) -> str:
    try:
        import pandas as pd
        df = pd.read_csv(file_path)
        return f"Loaded CSV with shape {df.shape} and columns: {df.columns.tolist()}"
    except Exception as e:
        return f"CSV error: {str(e)}"

@tool
def analyze_excel_file(file_path: str, query: str) -> str:
    try:
        import pandas as pd
        df = pd.read_excel(file_path)
        return f"Loaded Excel with shape {df.shape} and columns: {df.columns.tolist()}"
    except Exception as e:
        return f"Excel error: {str(e)}"

@tool
def youtube_transcribe(url: str) -> str:
    model = whisper.load_model("small")
    with tempfile.TemporaryDirectory() as tmpdir:
        ydl_opts = {
            'format': 'bestaudio/best',
            'outtmpl': os.path.join(tmpdir, 'audio.%(ext)s'),
            'quiet': True,
            'noplaylist': True,
            'postprocessors': [{
                'key': 'FFmpegExtractAudio',
                'preferredcodec': 'wav',
            }],
            'force_ipv4': True,
        }
        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            ydl.extract_info(url, download=True)
        audio_path = next((os.path.join(tmpdir, f) for f in os.listdir(tmpdir) if f.endswith('.wav')), None)
        return whisper.load_model("small").transcribe(audio_path)['text']

@tool
def transcribe_audio(audio_file_path: str) -> str:
    return whisper.load_model("small").transcribe(audio_file_path)['text']