diff --git a/diarize.py b/diarize.py
index 0d1fab7..656a9dc 100644
--- a/diarize.py
+++ b/diarize.py
@@ -1,21 +1,36 @@
 #!/usr/bin/env python3
 import datetime
-import unicodedata
-import time
-import os 
-import subprocess
 import json
 import logging
-import torch
-import contextlib
 import platform # used for checking OS version
 import shutil # used for checking existence of ffmpeg
+import time
+import unicodedata
+import os 
+import subprocess
+import contextlib
 import ffmpeg # Used for issuing commands to underlying ffmpeg executable, pip package ffmpeg is from 2018
+import torch
 import yt_dlp
-# idk....
 
+####
+#
+#       TL/DW: Too Long Didn't Watch
+#
+#  Project originally created by https://github.com/the-crypt-keeper
+#  Modifications made by https://github.com/rmusser01
+#  All credit to the original authors, I've just glued shit together.
+#
+###
+
+###
 # To Dos
-# Implement more logging and remove print statements
+#   Implement more logging (add an actual log file)
+#   Add conditional args for whether its ran in batch mode(File supplied) or single use (single url)
+#   Add support for actual summarization
+#   Add benchmarking for summarization results for various LLM usages.
+#   Add option for Whisper model selection/download
+###
 
 os.environ['KMP_DUPLICATE_LIB_OK']='True'
 
@@ -34,9 +49,6 @@ source_language_list = [key[0] for key in source_languages.items()]
 # Configure logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
-# Global variable to store the path of the last downloaded video
-last_video_path = None
-
 
 
 
@@ -80,8 +92,6 @@ def platform_check():
 
 
 
-
-
 # Check for NVIDIA GPU and CUDA availability
 def cuda_check():
     global processing_choice
@@ -124,6 +134,38 @@ def check_ffmpeg():
 
 
 
+def read_paths_from_file(file_path):
+    """ Reads a file containing URLs or local file paths and returns them as a list. """
+    paths = []
+    with open(file_path, 'r') as file:
+        for line in file:
+            line = line.strip()
+            if line and not os.path.exists(os.path.join('Results', normalize_title(line.split('/')[-1].split('.')[0]) + '.json')):
+                paths.append(line)
+    return paths
+
+
+
+def process_path(path):
+    """ Decides whether the path is a URL or a local file and processes accordingly. """
+    if path.startswith('http'):
+        return get_youtube(path)  # For YouTube URLs, modify to download and extract info
+    elif os.path.exists(path):
+        return process_local_file(path)  # For local files, define a function to handle them
+    else:
+        logging.error(f"Path does not exist: {path}")
+        return None
+
+
+
+# FIXME
+def process_local_file(file_path):
+    logging.info(f"Processing local file: {file_path}")
+    # Implement processing logic here
+    return {'title': os.path.basename(file_path)}
+
+
+
 # Ask the user for the URL of the video to be downloaded. Alternatively, ask the user for the location of a local txt file to be read in and parsed to a list to be processed individually
 def get_video_url():
     user_choice = input("Enter '1' to provide a video URL or '2' to specify a local text file path\n\t(the text file may contain both URLs and local file paths: ")
@@ -398,7 +440,7 @@ def speaker_diarize(video_file_path, segments, embedding_model = "pyannote/embed
         raise RuntimeError("Error Running inference with local model", e)
 
 
-
+"""
 def main(youtube_url: str, num_speakers: int = 2, whisper_model: str = "small.en", offset: int = 0, vad_filter : bool = False):
     info_dict = get_youtube(youtube_url)
     download_path = create_download_directory(info_dict['title'])
@@ -410,6 +452,32 @@ def main(youtube_url: str, num_speakers: int = 2, whisper_model: str = "small.en
 #    print("diarize complete:", save_path)
     print("Transcription complete:", audio_file)
 #FIXME
+"""
+
+
+def main(input_path: str, num_speakers: int = 2, whisper_model: str = "small.en", offset: int = 0, vad_filter: bool = False):
+    if os.path.isfile(input_path) and input_path.endswith('.txt'):
+        paths = read_paths_from_file(input_path)
+    elif input_path.startswith('http'):
+        paths = [input_path]
+    else:
+        logging.error("Invalid input: Please provide a valid URL or a text file path.")
+        return
+
+    results = []
+    for path in paths:
+        info_dict = process_path(path)
+        if info_dict:
+            download_path = create_download_directory(info_dict['title'])
+            video_path = download_video(path, download_path, info_dict)
+            audio_file = convert_to_wav(video_path, offset)
+            segments = speech_to_text(audio_file, whisper_model=whisper_model, vad_filter=vad_filter)
+            # Uncomment the next line if diarization is needed
+            # df_results, save_path = speaker_diarize(audio_file, segments, num_speakers=num_speakers)
+            results.append({'video_path': video_path, 'audio_file': audio_file, 'transcription': segments})
+            logging.info("Transcription complete: " + audio_file)
+    return results
+
 
 
 # Main Function - Execution starts here
@@ -419,4 +487,4 @@ if __name__ == "__main__":
     cuda_check()
     decide_cpugpu()
     check_ffmpeg()
-    fire.Fire(main)
\ No newline at end of file
+    fire.Fire(main)