Update app.py

app.py

@@ -2,7 +2,15 @@ import os
 import subprocess
 import sys
 import nltk
-
+import whisper
+import torch
+from transformers import pipeline
+from transformers.utils import logging
+from langdetect import detect
+import gradio as gr
+from gtts import gTTS
+from moviepy.editor import VideoFileClip
+import yt_dlp
 
 # Check if the question_generation directory exists; if not, clone the repository
 if not os.path.exists("question_generation"):
@@ -15,19 +23,6 @@ sys.path.append(os.path.join(os.path.dirname(__file__), 'question_generation'))
 # Download the punkt tokenizer
 nltk.download('punkt')
 
-
-# Continue with the rest of your imports and app logic
-import whisper
-import torch
-from transformers import pipeline
-from transformers.utils import logging
-from langdetect import detect
-import gradio as gr
-from gtts import gTTS
-from moviepy.editor import VideoFileClip
-import yt_dlp
-
-
 # Set logging verbosity
 logging.set_verbosity_error()
 
@@ -51,7 +46,13 @@ qa_pipeline = pipeline(task="question-answering", model="deepset/roberta-base-sq
 
 # Load question generator
 from pipelines import pipeline
-question_generator = pipeline("question-generation", model="valhalla/t5-small-qg-prepend", qg_format="prepend")
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("valhalla/t5-small-qg-prepend", use_fast=False)  # Disable the fast tokenizer
+model = AutoModelForSeq2SeqLM.from_pretrained("valhalla/t5-small-qg-prepend")
+
+question_generator = pipeline("question-generation", model=model, tokenizer=tokenizer, qg_format="prepend")
 
 # Function to download audio from YouTube
 def download_audio_from_youtube(youtube_url, output_path="downloaded_audio.mp3"):