Update tools.py

tools.py

@@ -1 +1,121 @@
-# tools.py - Tool definitions
+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']