Update config.py

config.py

@@ -1,4 +1,4 @@
-import fitz  # PyMuPDF
+import fitz
 import easyocr
 import whisper
 import tempfile
@@ -11,6 +11,8 @@ import csv
 from transformers import pipeline
 import streamlit as st
 
+# === Input Processing ===
+
 def process_pdf(path):
     text = ""
     try:
@@ -67,13 +69,15 @@ def process_youtube(url):
         ydl.download([url])
     return process_audio(audio_path)
 
+# === AI Swarm (CPU-Optimized) ===
+
 def load_llm_swarm():
     return {
-        "fast": pipeline("text2text-generation", model="google/flan-t5-small", max_length=64),
-        "bio": pipeline("text2text-generation", model="microsoft/BioGPT-Large", tokenizer="microsoft/BioGPT-Large"),
-        "deep": pipeline("text2text-generation", model="tiiuae/falcon-7b-instruct"),
-        "mistral": pipeline("text2text-generation", model="mistralai/Mistral-7B-Instruct"),
-        "fallback": pipeline("text2text-generation", model="MBZUAI/LaMini-Flan-T5-783M")
+        "fast": pipeline("text2text-generation", model="google/flan-t5-small"),
+        "bio": pipeline("text2text-generation", model="mrm8488/t5-base-finetuned-question-generation-ap"),
+        "deep": pipeline("text2text-generation", model="google/flan-t5-base"),
+        "mistral": pipeline("text2text-generation", model="google/flan-t5-large"),
+        "fallback": pipeline("text2text-generation", model="MBZUAI/LaMini-Flan-T5-248M")
     }
 
 llm_swarm = load_llm_swarm()
@@ -123,6 +127,8 @@ def generate_flashcards(text, types=["Q&A"], max_cards=100):
 
     return cards
 
+# === Export Functions ===
+
 def export_to_csv(cards, filename="batanki_cards.csv"):
     with open(filename, "w", newline="", encoding="utf-8") as f:
         writer = csv.writer(f)