Created main application

Author: NamanVer02

llm_quiz_generator/main.py

@@ -0,0 +1,121 @@
+"""
+___________________________________________________________________________________________________________________________________________________
+|                                                                                                                                                 |
+|   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 langchain_groq import ChatGroq
+from langchain.chains import RetrievalQA
+from dotenv import load_dotenv, find_dotenv
+from langchain_community.vectorstores import FAISS
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+
+
+load_dotenv(find_dotenv())
+API_KEY = os.environ["GROQ_API_KEY"]
+
+
+def extract_text_from_pdfs():
+    print(f"Extracting text from PDF files in the folder: '{'Source'}'...")
+    all_text = []
+    for file_name in os.listdir('Source'):
+        if file_name.endswith(".pdf"):
+            file_path = os.path.join('Source', file_name)
+            print(f"Processing file: {file_name}")
+            reader = PdfReader(file_path)
+            for page in reader.pages:
+                all_text.append(page.extract_text())
+    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...")
+    text_splitter = CharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=0
+    )
+    docs = text_splitter.create_documents([text])
+
+    embeddings = HuggingFaceEmbeddings()
+    store = FAISS.from_documents(docs, embeddings)
+
+    print(f"Connecting to LLM to generate {num_questions} unique MCQs...")
+    llm = ChatGroq(
+        temperature=0.2,
+        model="llama-3.1-70b-versatile",
+        api_key=API_KEY
+    )
+
+    retrieval_chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=store.as_retriever()
+    )
+
+    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."
+    
+    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"):
+    output_folder = "Generated_Quizes"
+    
+    if not os.path.exists(output_folder):
+        os.makedirs(output_folder)
+        print(f"Folder '{output_folder}' created.")
+    
+    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.")
+    else:
+        print(f"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.")