Create app.py

app.py

@@ -0,0 +1,91 @@
+import os
+import streamlit as st
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain_openai import OpenAI, OpenAIEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain_community.vectorstores import FAISS
+
+# Load environment variables
+load_dotenv()
+openai_api_key = os.getenv('OPENAI_API_KEY')
+
+# Initialize Streamlit session states
+if 'vectorDB' not in st.session_state:
+    st.session_state.vectorDB = None
+
+# Function to extract text from a PDF file
+def get_pdf_text(pdf):
+    text = ""
+    pdf_reader = PdfReader(pdf)
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+# Function to create a vector database
+def get_vectorstore(text_chunks):
+    embeddings = OpenAIEmbeddings(api_key=openai_api_key)
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+# Function to split text into chunks
+def get_text_chunks(text):
+    text_chunks = text.split('\n\n')  # Modify this based on your text splitting requirements
+    return text_chunks
+
+# Function to process PDF and create vector database
+def processing(pdf):
+    raw_text = get_pdf_text(pdf)
+    text_chunks = get_text_chunks(raw_text)
+    vectorDB = get_vectorstore(text_chunks)
+    return vectorDB
+
+# Function to generate quiz questions
+def generate_quiz(quiz_name, quiz_topic, num_questions, pdf_content):
+    st.header(f"Quiz Generator: {quiz_name}")
+    st.subheader(f"Topic: {quiz_topic}")
+
+    # Process PDF and create vector database
+    if st.button('Process PDF'):
+        st.session_state['vectorDB'] = processing(pdf_content)
+        st.success('PDF Processed and Vector Database Created')
+
+    # Generate Quiz Questions
+    for i in range(1, num_questions + 1):
+        st.subheader(f"Question {i}")
+        question = st.text_input(f"Enter Question {i}:", key=f"question_{i}")
+        options = []
+        for j in range(1, 5):
+            option = st.text_input(f"Option {j}:", key=f"option_{i}_{j}")
+            options.append(option)
+
+        correct_answer = st.selectbox(f"Correct Answer for Question {i}:", options=options, key=f"correct_answer_{i}")
+
+        # Save question, options, and correct answer in vector database
+        if st.session_state.vectorDB:
+            # Create a prompt template for question and options
+            template = f"Quiz: {quiz_name}\nTopic: {quiz_topic}\nQuestion: {question}\nOptions: {', '.join(options)}\nCorrect Answer: {correct_answer}"
+            prompt = PromptTemplate(template=template)
+
+            # Store question data in vector database
+            st.session_state.vectorDB.add(prompt.generate(), embedding=None)
+
+    # Save button to store vector database
+    if st.session_state.vectorDB:
+        if st.button('Save Vector Database'):
+            st.success('Vector Database Saved')
+
+if __name__ =='__main__':
+    st.set_page_config(page_title="Quiz Generator", page_icon="📝")
+    st.title('Quiz Generator')
+
+    # User inputs
+    quiz_name = st.text_input('Enter Quiz Name:')
+    quiz_topic = st.text_input('Enter Quiz Topic:')
+    num_questions = st.number_input('Enter Number of Questions:', min_value=1, value=1, step=1)
+    pdf_content = st.file_uploader("Upload PDF Content for Questions:", type='pdf')
+
+    # Generate quiz if all inputs are provided
+    if quiz_name and quiz_topic and num_questions and pdf_content:
+        generate_quiz(quiz_name, quiz_topic, num_questions, pdf_content)