enhanced function calls

lollms/functions/youtube/download_transcript.py

@@ -10,7 +10,7 @@ from lollms.utilities import PackageManager
 from ascii_colors import trace_exception
 
 # Installing necessary packages
-if not PackageManager.check_package_installed("youtube-transcript-api"):
+if not PackageManager.check_package_installed("youtube_transcript_api"):
     PackageManager.install_package("youtube-transcript-api")
 
 # Importing the package after installation

lollms/functions/youtube/download_transcript_by_channel.py

@@ -0,0 +1,74 @@
+# Lollms function call definition file
+# File Name: download_channel_transcripts.py
+# Author: ParisNeo
+# Description: This function takes a YouTube channel name, scans all their videos using web scraping, and downloads the transcripts. Each transcript is saved in a folder as a text file.
+
+# Importing necessary libraries
+from functools import partial
+from typing import List
+from lollms.utilities import PackageManager
+from ascii_colors import trace_exception
+import pathlib
+import requests
+from bs4 import BeautifulSoup
+
+# Installing necessary packages
+if not PackageManager.check_package_installed("youtube_transcript_api"):
+    PackageManager.install_package("youtube-transcript-api")
+
+# Importing the package after installation
+from youtube_transcript_api import YouTubeTranscriptApi
+
+def download_channel_transcripts(channel_url: str, output_folder: str) -> str:
+    """
+    This function takes a YouTube channel URL, scans all their videos using web scraping, and downloads the transcripts.
+    Each transcript is saved in a folder as a text file.
+    
+    Parameters:
+    channel_url (str): The URL of the YouTube channel.
+    output_folder (str): The folder where transcripts will be saved.
+    
+    Returns:
+    str: A message indicating the status of the download process.
+    """
+    try:
+        # Create output folder if it doesn't exist
+        output_folder_path = pathlib.Path(output_folder)
+        output_folder_path.mkdir(parents=True, exist_ok=True)
+        
+        # Get channel page content
+        response = requests.get(channel_url)
+        response.raise_for_status()
+        soup = BeautifulSoup(response.content, "html.parser")
+        
+        # Find all video links
+        video_links = soup.find_all("a", href=True)
+        video_ids = [link['href'].split('v=')[1] for link in video_links if "watch?v=" in link['href']]
+        
+        # Remove duplicates
+        video_ids = list(set(video_ids))
+        
+        # Download transcripts and save to files
+        for video_id in video_ids:
+            try:
+                transcript = YouTubeTranscriptApi.get_transcript(video_id)
+                transcript_text = " ".join([entry['text'] for entry in transcript])
+                
+                output_file_path = output_folder_path / f"{video_id}.txt"
+                output_file_path.write_text(transcript_text, encoding='utf-8')
+            except Exception as e:
+                trace_exception(e)
+        
+        return "Transcripts downloaded successfully!"
+    except Exception as e:
+        return trace_exception(e)
+
+def download_channel_transcripts_function(output_folder:str):
+    return {
+        "function_name": "download_channel_transcripts",
+        "function": partial(download_channel_transcripts, output_folder=output_folder),
+        "function_description": "This function takes a YouTube channel name, scans all their videos using web scraping, and downloads the transcripts. Each transcript is saved in a folder as a text file.",
+        "function_parameters": [
+            {"name": "channel_url", "type": "str"},
+        ]
+    }

lollms/services/comfyui/lollms_comfyui.py

@@ -33,6 +33,7 @@ from lollms.tti import LollmsTTI
 import subprocess
 import shutil
 from tqdm import tqdm
+import threading
 
 if not PackageManager.check_package_installed("websocket"):
     PackageManager.install_or_update("websocket-client")
@@ -224,10 +225,14 @@ class LollmsComfyUI(LollmsTTI):
 
         # Wait until the service is available at http://127.0.0.1:8188//
         if wait_for_service:
-            self.wait_for_service(max_retries=max_retries)
+            self.wait_for_service()
         else:
-            ASCIIColors.warning("We are not waiting for the SD service to be up.\nThis means that you may need to wait a bit before you can use it.")
+            self.wait_for_service_in_another_thread(max_retries=max_retries)
 
+    def wait_for_service_in_another_thread(self, max_retries=150, show_warning=True):
+        thread = threading.Thread(target=self.wait_for_service, args=(max_retries, show_warning))
+        thread.start()
+        return thread
 
     def wait_for_service(self, max_retries = 50, show_warning=True):
         url = f"{self.comfyui_base_url}"

lollms/services/diffusers/lollms_diffusers.py

@@ -127,11 +127,20 @@ class LollmsDiffusers(LollmsTTI):
             PackageManager.install_or_update("sentencepiece")
             PackageManager.install_or_update("accelerate")
         try:
-            from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image#PixArtSigmaPipeline
-            self.model = AutoPipelineForText2Image.from_pretrained(
-                app.config.diffusers_model, torch_dtype=torch.float16, cache_dir=self.models_dir,
-                use_safetensors=True,
-            )
+            if "stable-diffusion-3" in app.config.diffusers_model:
+                from diffusers import StableDiffusion3Pipeline # AutoPipelineForImage2Image#PixArtSigmaPipeline
+                self.model = StableDiffusion3Pipeline.from_pretrained(
+                    app.config.diffusers_model, torch_dtype=torch.float16, cache_dir=self.models_dir,
+                    use_safetensors=True,
+                )
+            else:
+                from diffusers import AutoPipelineForText2Image # AutoPipelineForImage2Image#PixArtSigmaPipeline
+                self.model = AutoPipelineForText2Image.from_pretrained(
+                    app.config.diffusers_model, torch_dtype=torch.float16, cache_dir=self.models_dir,
+                    use_safetensors=True,
+                )
+            
+            # AutoPipelineForText2Image
             # self.model = StableDiffusionPipeline.from_pretrained(
             #     "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16, cache_dir=self.models_dir,
             #     use_safetensors=True,