app.py

import os
import streamlit as st
from PyPDF2 import PdfReader
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
from groq import Groq

# Initialize Groq Client
GROQ_API_KEY = os.environ.get('GroqApi')
client = Groq(api_key=GROQ_API_KEY)

# Initialize Embedding Model
embedding_model = SentenceTransformer('distilbert-base-uncased')

# Streamlit UI
st.title("RAG-based Quiz App")

# File Upload
uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
if uploaded_file is not None:
    # Extract Text from PDF
    pdf_reader = PdfReader(uploaded_file)
    text = " ".join([page.extract_text() for page in pdf_reader.pages])
    
    # Chunking Text
    st.write("Processing the PDF...")
    chunk_size = 500
    chunks = [text[i:i + chunk_size] for i in range(0, len(text), chunk_size)]
    
    # Create Embeddings
    embeddings = embedding_model.encode(chunks)
    embeddings = np.array(embeddings, dtype="float32")
    
    # FAISS Index
    index = faiss.IndexFlatL2(embeddings.shape[1])
    index.add(embeddings)
    
    st.success("PDF Processed! Embeddings Created.")
    
    # Generate Questions
    st.write("Generating Quiz Questions...")
    questions = []
    for chunk in chunks[:3]:  # Process fewer chunks to improve performance
        response = client.chat.completions.create(
            messages=[{"role": "user", "content": f"Create a multiple-choice quiz question from this text: {chunk}"}],
            model="llama3-8b-8192"
        )
        question = response.choices[0].message.content
        questions.append(question)
    
    st.success("Quiz Questions Generated!")
    
    # Display Quiz
    for idx, question in enumerate(questions):
        st.write(f"**Question {idx+1}:** {question}")
        
        # Parse Question to Extract Correct Answer (Assuming the API formats it consistently)
        # Example format: "Question: ... Options: A) ..., B) ..., C) ..., D) ... Correct: A"
        lines = question.split("\n")
        options = [line.split(") ")[1] for line in lines if line.strip().startswith(("A", "B", "C", "D"))]
        correct_option_line = [line for line in lines if "Correct:" in line]
        correct_option = correct_option_line[0].split(": ")[1].strip() if correct_option_line else None
        
        selected_option = st.radio(f"Select your answer for Question {idx+1}", options, key=idx)
        
        if st.button(f"Submit Answer for Question {idx+1}", key=f"submit_{idx}"):
            if selected_option == correct_option:
                st.success("Correct Answer!")
            else:
                st.error(f"Wrong Answer! Correct Answer: {correct_option}")
            
            # Highlight Correct and Selected Options
            st.write(f"**Correct Option:** {correct_option}")
            st.write(f"**Your Selection:** {selected_option}")

# Footer
st.write("App developed and deployed using Hugging Face Spaces.")

# Initialize Embedding Model
embedding_model = SentenceTransformer('distilbert-base-uncased')

# Streamlit UI
st.title("RAG-based Quiz App")

# File Upload
uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
if uploaded_file is not None:
    # Extract Text from PDF
    pdf_reader = PdfReader(uploaded_file)
    text = " ".join([page.extract_text() for page in pdf_reader.pages])
    
    # Chunking Text
    st.write("Processing the PDF...")
    chunks = [text[i:i+500] for i in range(0, len(text), 500)]
    
    # Create Embeddings
    embeddings = embedding_model.encode(chunks)
    embeddings = np.array(embeddings, dtype="float32")
    
    # FAISS Index
    index = faiss.IndexFlatL2(embeddings.shape[1])
    index.add(embeddings)
    
    st.success("PDF Processed! Embeddings Created.")
    
    # Generate Questions
    st.write("Generating Quiz Questions...")
    questions = []
    for chunk in chunks[:5]:  # Generate questions for the first few chunks
        response = client.chat.completions.create(
            messages=[{"role": "user", "content": f"Create a multiple-choice quiz question from this text: {chunk}"}],
            model="llama3-8b-8192"
        )
        question = response.choices[0].message.content
        questions.append(question)
    
    st.success("Quiz Questions Generated!")
    
    # Display Quiz
    for idx, question in enumerate(questions):
        st.write(f"**Question {idx+1}:** {question}")
        options = ["Option A", "Option B", "Option C", "Option D"]  # Placeholder
        selected_option = st.radio(f"Select your answer for Question {idx+1}", options, key=idx)
        if st.button(f"Submit Answer for Question {idx+1}", key=f"submit_{idx}"):
            # Dummy Logic: Assume Option A is correct for demonstration
            correct_option = "Option A"
            if selected_option == correct_option:
                st.success("Correct Answer!")
            else:
                st.error(f"Wrong Answer! Correct Answer: {correct_option}")

# Footer
st.write("App developed and deployed using Hugging Face Spaces.")

# Initialize embedding model
embedding_model = SentenceTransformer('all-MiniLM-L6-v2')

# Upload PDF
st.title("PDF to Quiz Generator")
uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])

if uploaded_file:
    # Extract text from PDF
    st.write("Processing PDF...")
    reader = PdfReader(uploaded_file)
    pdf_text = ""
    for page in reader.pages:
        pdf_text += page.extract_text()

    # Split text into chunks
    chunk_size = 512  # Adjust as needed
    text_chunks = [pdf_text[i:i + chunk_size] for i in range(0, len(pdf_text), chunk_size)]

    # Generate embeddings
    st.write("Generating embeddings...")
    embeddings = embedding_model.encode(text_chunks)

    # Store embeddings in FAISS
    dimension = embeddings.shape[1]
    index = faiss.IndexFlatL2(dimension)
    index.add(embeddings)

    # Generate questions using Groq API
    def generate_question(content):
        response = client.chat.completions.create(
            messages=[{"role": "user", "content": f"Generate a multiple-choice question from: {content}"}],
            model="llama3-8b-8192",
        )
        return response.choices[0].message.content

    # Generate quiz
    st.write("Generating quiz...")
    quiz = []
    for chunk in text_chunks:
        question = generate_question(chunk)
        quiz.append(question)

    # Display the quiz
    st.write("Here is your quiz:")
    for i, q in enumerate(quiz, 1):
        st.markdown(f"**Question {i}:** {q}")