Delete tools.py

tools.py

@@ -1,121 +0,0 @@
-import tempfile
-import requests
-import os
-from urllib.parse import urlparse
-from typing import Optional, List
-import yt_dlp
-import imageio
-from PIL import Image
-import whisper
-from dotenv import load_dotenv
-
-# Fallback tool decorator if gaia_benchmark.tools is not available
-try:
-    from gaia_benchmark.tools import tool
-except ImportError:
-    def tool(func):
-        return func
-
-load_dotenv()
-
-@tool
-def use_vision_model(question: str, images: List[Image.Image]) -> str:
-    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:
-            ydl.download([url])
-
-        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', 25)
-        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.download([url])
-
-        audio_path = next((os.path.join(tmpdir, f) for f in os.listdir(tmpdir) if f.endswith('.wav')), None)
-        return model.transcribe(audio_path)['text']
-
-@tool
-def transcribe_audio(audio_file_path: str) -> str:
-    return whisper.load_model("small").transcribe(audio_file_path)['text']