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import streamlit as st
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain_google_genai import ChatGoogleGenerativeAI
import fitz
import json
# Title
st.title("📄 PDF-based MCQ Generator")
# Sidebar
st.sidebar.title("Upload & Settings")
# Upload PDF
pdf_file = st.sidebar.file_uploader("Upload a PDF file", type=["pdf"])
# Number of questions
number_of_questions = st.sidebar.slider("Number of questions", min_value=1, max_value=20, value=5)
# Session states
if "mcqs" not in st.session_state:
st.session_state.mcqs = []
if "current_q" not in st.session_state:
st.session_state.current_q = 0
if "user_answers" not in st.session_state:
st.session_state.user_answers = {}
if "quiz_finished" not in st.session_state:
st.session_state.quiz_finished = False
# Gemini setup
GOOGLE_API_KEY = "AIzaSyCB5NLx39vOAlfRQBDmnEG3uLBgLraGvH4"
llm = ChatGoogleGenerativeAI(
model="gemini-2.0-flash",
google_api_key=GOOGLE_API_KEY,
temperature=0.7
)
template = """
You are an expert MCQ generator. Generate {number} unique multiple-choice questions from the given text.
Each question must have exactly 1 correct answer and 3 incorrect options.
Strictly return output in the following JSON format (no explanations, no markdown):
[
{{
"question": "What is ...?",
"options": ["Option A", "Option B", "Option C", "Option D"],
"answer": "Option D"
}},
...
]
TEXT:
{text}
"""
prompt = PromptTemplate(
input_variables=["text", "number"],
template=template
)
mcq_chain = LLMChain(llm=llm, prompt=prompt)
# PDF text extractor
def extract_text_from_pdf(pdf):
doc = fitz.open(stream=pdf.read(), filetype="pdf")
full_text = ""
for page in doc:
full_text += page.get_text()
doc.close()
return full_text
# Generate MCQs
if st.sidebar.button("Generate MCQs"):
if pdf_file is None:
st.error("Please upload a PDF file.")
else:
with st.spinner("Extracting text and generating MCQs..."):
text = extract_text_from_pdf(pdf_file)
try:
response = mcq_chain.run(text=text, number=str(number_of_questions))
# st.subheader("🔍 Raw Output (Debugging)")
# st.code(response)
mcqs_json = json.loads(response.strip())
st.session_state.mcqs = mcqs_json
st.session_state.current_q = 0
st.session_state.user_answers = {}
st.session_state.quiz_finished = False
st.success("✅ MCQs generated successfully!")
except Exception as e:
st.error(f"Error generating MCQs: {e}")
# Display question
if st.session_state.mcqs and not st.session_state.quiz_finished:
idx = st.session_state.current_q
q_data = st.session_state.mcqs[idx]
st.subheader(f"Question {idx + 1}: {q_data['question']}")
selected_option = st.radio(
"Choose an answer:",
q_data["options"],
key=f"radio_{idx}"
)
if st.button("Next"):
st.session_state.user_answers[idx] = selected_option
if st.session_state.current_q < len(st.session_state.mcqs) - 1:
st.session_state.current_q += 1
else:
st.session_state.quiz_finished = True
st.success("🎉 Quiz completed!")
# Show result
if st.session_state.quiz_finished:
st.header("📊 Quiz Results")
score = 0
total = len(st.session_state.mcqs)
for i, q in enumerate(st.session_state.mcqs):
user_ans = st.session_state.user_answers.get(i)
correct_ans = q["answer"]
if user_ans == correct_ans:
score += 1
st.markdown(f"**Q{i+1}: {q['question']}**")
st.markdown(f"- Your answer: {user_ans}")
st.markdown(f"- Correct answer: {correct_ans}")
st.markdown("---")
st.success(f"✅ You scored {score} out of {total}")
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