import PyPDF2
import nltk
from nltk.tokenize import sent_tokenize
import random
import requests
import streamlit as st

# Download NLTK data (if not already downloaded)
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')

# ChatGPT API endpoint
CHATGPT_API_ENDPOINT = "https://api.openai.com/v1/chat/completions"
OPENAI_API_KEY = "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"  # Replace with your ChatGPT API key

def extract_text_from_pdf(pdf_file):
    pdf_reader = PyPDF2.PdfReader(pdf_file)
    text = ""
    for page_num in range(len(pdf_reader.pages)):
        text += pdf_reader.pages[page_num].extract_text()
    return text

def generate_mcqs_on_topic(text, topic, num_mcqs=5):
    # Tokenize the text into sentences
    sentences = nltk.sent_tokenize(text)

    # Randomly select sentences to create Questions
    selected_sentences = random.sample(sentences, min(num_mcqs, len(sentences)))

    mcqs = []
    for sentence in selected_sentences:
        # Use ChatGPT for interactive question generation
        chatgpt_question = generate_question_with_chatgpt(sentence)
        mcqs.append(chatgpt_question)

        # Use LLM for nuanced language modeling
        llm_question = generate_question_with_llm(sentence, topic)
        mcqs.append(llm_question)

    return mcqs

def generate_question_with_chatgpt(context):
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {OPENAI_API_KEY}",
    }

    data = {
        "model": "gpt-3.5-turbo",
        "messages": [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": f"What is the question for the following? {context}"},
        ],
    }

    response = requests.post(CHATGPT_API_ENDPOINT, json=data, headers=headers)
    result = response.json()

    # Extract the generated question from the response
    generated_question = result["choices"][0]["message"]["content"]
    return generated_question


    response = requests.post(LLM_API_ENDPOINT, json=data, headers=headers)
    result = response.json()

    # Extract the generated question from the response
    generated_question = result["generated_question"]
    return generated_question

def main(): 
    # Title of the Application
    st.title("🤖CB Quiz Generator🧠")
    st.subheader("☕CoffeeBeans☕")

    # User input 
    pdf_file = st.file_uploader("Upload PDF Document:", type=["pdf"])
    num_mcqs = st.number_input("Enter Number of MCQs to Generate:", min_value=1, step=1, value=5)
    topic = st.text_input("Enter the Topic in which the quiz has to be generated")

    # Button to trigger QUIZ generation
    if st.button("Generate MCQs"):
        if pdf_file:
            text = extract_text_from_pdf(pdf_file)
            mcqs = generate_mcqs_on_topic(text, topic, num_mcqs)

            # Display the generated Questions
            st.success(f"Generated {num_mcqs} Questions:")
            for i, question in enumerate(mcqs, start=1):
                st.write(f"\nQuestion {i}: {question}")
        else:
            st.error("Please upload a PDF document.")

if __name__ == "__main__":
    main()