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import streamlit as st
import openai
from openai import OpenAI
import time
import gspread
from oauth2client.service_account import ServiceAccountCredentials
# Set up OpenAI client
client = OpenAI(api_key=st.secrets["OPENAI_API_KEY"])
# Google Sheets setup remains the same
scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
creds = ServiceAccountCredentials.from_json_keyfile_name("genexam-2c8c645ecc0d.json", scope)
client_gs = gspread.authorize(creds)
sheet = client_gs.open("GeneXam user").sheet1
def check_user_in_sheet(username):
try:
users_list = sheet.col_values(1)
if username in users_list:
return True
return False
except Exception as e:
st.error(f"Error checking user: {str(e)}")
return False
def update_api_usage(username):
try:
users_list = sheet.col_values(1)
row_number = users_list.index(username) + 1
api_usage = int(sheet.cell(row_number, 2).value)
api_usage += 1
sheet.update_cell(row_number, 2, api_usage)
except Exception as e:
st.error(f"Error updating API usage: {str(e)}")
def generate_questions_with_retry(knowledge_material, question_type, cognitive_level, extra_instructions, case_based, num_choices=None, max_retries=3):
# Adjust number of questions based on type
if question_type == "Multiple Choice":
num_questions = 3
elif question_type == "Fill in the Blank":
num_questions = 10
elif question_type == "True/False":
num_questions = 5
else: # Open-ended
num_questions = 3
# Base prompt
prompt = f"Generate {num_questions} {question_type.lower()} exam questions based on {cognitive_level.lower()} level from the following material: {knowledge_material}. {extra_instructions}"
# Modify prompt for case-based medical situations
if case_based:
prompt = f"Generate {num_questions} {question_type.lower()} exam questions based on {cognitive_level.lower()} level from the following medical material: {knowledge_material}. The questions should be based on case-based medical situations, such as patient scenarios. {extra_instructions}"
if question_type != "Fill in the Blank":
prompt += " Provide answers with short explanations."
# Add specific handling for Multiple Choice and True/False
if question_type == "Multiple Choice" and num_choices:
prompt += f" Each multiple choice question should have {num_choices} choices."
if question_type == "True/False":
prompt += " Provide short explanations for each question based on the given material, without stating True or False explicitly."
retries = 0
while retries < max_retries:
try:
response = client.chat.completions.create(
model="gpt-4o-mini", # or your preferred model
messages=[
{"role": "system", "content": "You are a helpful assistant for generating exam questions."},
{"role": "user", "content": prompt}
]
)
return response.choices[0].message.content
except Exception as e:
retries += 1
if retries == max_retries:
st.error(f"Failed to generate questions after {max_retries} attempts. Error: {str(e)}")
return None
time.sleep(2) # Wait before retrying
# ระบบ login
if 'username' not in st.session_state:
st.title("Login")
username_input = st.text_input("Enter your username:")
if st.button("Login"):
if username_input:
if check_user_in_sheet(username_input):
st.session_state['username'] = username_input
st.success(f"Welcome, {username_input}!")
update_api_usage(username_input)
else:
st.warning("Username not found. Please try again.")
else:
st.warning("Please enter a valid username.")
else:
# Main App after login
st.title(f"Welcome, {st.session_state['username']}! Generate your exam questions")
# Input field for knowledge material (text) with 3,000-word limit
knowledge_material = st.text_area("Enter knowledge material to generate exam questions:")
# Word count check
if len(knowledge_material.split()) > 3000:
st.warning("Please limit the knowledge material to 3,000 words or fewer.")
# File uploader for PDFs (limited to 5 MB)
uploaded_file = st.file_uploader("Upload a file (PDF)", type="pdf")
if uploaded_file is not None:
if uploaded_file.size > 5 * 1024 * 1024: # 5 MB limit
st.warning("File size exceeds 5 MB. Please upload a smaller file.")
else:
st.success("File uploaded successfully! (Text extraction not implemented yet.)")
# Select question type
question_type = st.selectbox("Select question type:",
["Multiple Choice", "Fill in the Blank", "Open-ended", "True/False"])
# For multiple choice, let users select the number of choices
num_choices = None
if question_type == "Multiple Choice":
num_choices = st.selectbox("Select the number of choices for each question:", [3, 4, 5])
# Select cognitive level
cognitive_level = st.selectbox("Select cognitive level:",
["Recall", "Understanding", "Application", "Analysis", "Synthesis", "Evaluation"])
# Checkbox for Case-Based Medical Situations
case_based = st.checkbox("Generate case-based medical exam questions")
# Extra input field for additional instructions (placed below cognitive level)
extra_instructions = st.text_area("Enter additional instructions (e.g., how you want the questions to be phrased):")
# Generate questions button
if 'previous_questions' not in st.session_state:
st.session_state['previous_questions'] = []
if st.button("Generate Questions"):
if len(knowledge_material.split()) <= 3000:
# Generate questions with retry logic
questions = generate_questions_with_retry(
knowledge_material,
question_type,
cognitive_level,
extra_instructions,
case_based,
num_choices
)
if questions:
st.write("Generated Exam Questions:")
st.write(questions)
# Avoid showing repeated content in future requests
st.session_state['previous_questions'].append(questions)
# Option to download the questions as a text file
st.download_button(
label="Download Questions",
data=questions,
file_name='generated_questions.txt',
mime='text/plain'
)
else:
st.warning("Please reduce the word count to 3,000 or fewer.")
# Button to generate more questions based on the same material
if st.button("Generate More Questions"):
if len(knowledge_material.split()) <= 3000:
# Regenerate new questions, trying to avoid repeated content
questions = generate_questions_with_retry(
knowledge_material,
question_type,
cognitive_level,
extra_instructions,
case_based,
num_choices
)
# Check if the new set of questions is not the same as the previous set
if questions and questions not in st.session_state['previous_questions']:
st.write("Generated More Exam Questions:")
st.write(questions)
# Append the new questions to the session state
st.session_state['previous_questions'].append(questions)
# Option to download the new set of questions
st.download_button(
label="Download More Questions",
data=questions,
file_name='more_generated_questions.txt',
mime='text/plain'
)
else:
st.warning("New questions seem to overlap with the previous ones. Try adjusting the instructions.")
else:
st.warning("Please reduce the word count to 3,000 or fewer.")
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