flake8 linting

Author: NamanVer02

llm_quiz_generator/main.py

@@ -1,28 +1,3 @@
-"""
-___________________________________________________________________________________________________________________________________________________
-|                                                                                                                                                 |
-|   To use this script, please check the README.md file in the directory. A quick start to get the project running is described here.             |
-|                                                                                                                                                 |
-|   1. Create a Groq account and get your API key at https://console.groq.com/login.                                                              |
-|                                                                                                                                                 |
-|   2. Either:                                                                                                                                    |
-|      - Add your API key directly to line 38: API_KEY = "your_groq_api_key_here", or                                                             |
-|      - Create a .env file in the same directory, and add GROQ_API_KEY=your_groq_api_key_here.                                                   |
-|                                                                                                                                                 |
-|   3. Place all your PDFs in a folder named 'Source' in the same directory as this script.                                                       |
-|                                                                                                                                                 |
-|   4. Run the script:                                                                                                                            |
-|      python quiz_generator.py                                                                                                                   |
-|                                                                                                                                                 |
-|   The generated MCQ quiz will be saved in a file called 'generated_mcq_quiz.txt'.                                                               |
-|_________________________________________________________________________________________________________________________________________________|
-"""
-
-
-# Change this if you want to set the number of MCQ's
-num_questions = 5
-
-
 import os
 from PyPDF2 import PdfReader
 from datetime import datetime
@@ -33,20 +8,23 @@
 from langchain_huggingface import HuggingFaceEmbeddings
 from langchain.text_splitter import CharacterTextSplitter
 
-
 load_dotenv(find_dotenv())
 API_KEY = os.environ["GROQ_API_KEY"]
 
+# Change this if you want to set the number of MCQs
+num_questions = 5
+
 
 def extract_text_from_pdfs():
-    print(f"Extracting text from PDF files in the folder: 'Source'...")
+    """Extracts text from PDF files in the 'Source' folder."""
+    print("Extracting text from PDF files in the folder: 'Source'...")
     all_text = []
-    
-    if not os.path.exists('Source') or not os.listdir('Source'):
-        print("Folder 'Source' is empty or not found!")
+
+    if len(os.listdir('Source')) == 0:
+        print("Source Folder Empty!")
         print("Process exiting...")
         exit(0)
-    
+
     for file_name in os.listdir('Source'):
         if file_name.endswith(".pdf"):
             file_path = os.path.join('Source', file_name)
@@ -57,8 +35,10 @@ def extract_text_from_pdfs():
     print("Text extraction completed.")
     return " ".join(all_text)
 
+
 def generate_unique_mcq(text, num_questions=5):
-    print(f"Splitting text into chunks and creating embeddings for LLM processing...")
+    """Generates unique multiple choice questions from text."""
+    print("LLM processing...")
     text_splitter = CharacterTextSplitter(
         chunk_size=1000,
         chunk_overlap=0
@@ -82,42 +62,48 @@ def generate_unique_mcq(text, num_questions=5):
     )
 
     quiz = []
-    query = f"Generate {num_questions} unique multiple choice questions from the following text: {text} " \
-            f"Provide 4 answer options and also the correct answer in plaintext."
-    
+    query = (
+        f"Generate {num_questions} unique multiple choice questions"
+        "from the text: {text}"
+        "Provide 4 answer options and also the correct answer in plaintext."
+    )
+
     response = retrieval_chain.invoke(query)
     question_and_options = response['result']
     quiz.append(question_and_options)
 
     print("MCQ generation completed.")
     return quiz
 
+
 def save_mcq_to_file(quiz, file_name="generated_mcq_quiz.txt"):
+    """Saves generated MCQs to a text file."""
     output_folder = "Generated_Quizes"
-    
+
     if not os.path.exists(output_folder):
         os.makedirs(output_folder)
         print(f"Folder '{output_folder}' created.")
-    
+
     current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
     file_name = f"generated_mcq_quiz_{current_time}.txt"
     file_path = os.path.join(output_folder, file_name)
-    
+
     print(f"Saving the generated MCQs to file: '{file_path}'...")
     with open(file_path, "w") as f:
         for i, question in enumerate(quiz, 1):
             f.write(f"Question {i}:\n{question}\n\n")
-    
+
     print(f"MCQ Quiz saved to {file_path}")
 
+
 if __name__ == "__main__":
     if not os.path.exists('Source'):
-        print(f"Folder 'Source' not found.")
+        print("Folder 'Source' not found.")
     else:
-        print(f"Folder 'Source' found. Starting process...")
+        print("Folder 'Source' found. Starting process...")
         text = extract_text_from_pdfs()
         print("Text extracted from PDFs.")
-        
+
         mcq_quiz = generate_unique_mcq(text, num_questions=num_questions)
         save_mcq_to_file(mcq_quiz)
         print("Process completed successfully.")