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| import os | |
| from dotenv import load_dotenv | |
| from groq import Groq | |
| import streamlit as st | |
| # Load environment variables | |
| load_dotenv() | |
| # Initialize the Groq client with API key | |
| client = Groq(api_key=os.getenv("GROQ_API_KEY")) | |
| # Function to generate MCQs as a 35-year experienced educator | |
| def generate_mcqs_from_text(user_text): | |
| prompt = f""" | |
| You are a 35-year experienced educator specializing in crafting challenging and insightful MCQs. | |
| Based on the following text, generate between 30 to 50 multiple-choice questions (MCQs). | |
| Each question should: | |
| 1. Test critical thinking and understanding of the content. | |
| 2. Include four options (A, B, C, D), with one correct answer and three well-designed distractors. | |
| 3. Provide clear, concise language and avoid ambiguity. | |
| Format the output as: | |
| Question: [Your question here] | |
| A. [Option 1] | |
| B. [Option 2] | |
| C. [Option 3] | |
| D. [Option 4] | |
| Correct Answer: [Correct Option] | |
| Text: {user_text} | |
| """ | |
| chat_completion = client.chat.completions.create( | |
| messages=[{"role": "user", "content": prompt}], | |
| model="gemma2-9b-it", # Use a valid Groq-supported model | |
| ) | |
| return chat_completion.choices[0].message.content | |
| # Function to evaluate MCQ answers like an experienced teacher | |
| def evaluate_answers(mcqs, user_answers): | |
| prompt = f""" | |
| You are a 35-year experienced educator evaluating student answers. | |
| The student has completed the following MCQs. Please: | |
| 1. Check the correctness of each answer. | |
| 2. Provide detailed feedback for each question, explaining why the student's choice is correct or incorrect. | |
| 3. Assign an overall score and include advice for improvement. | |
| MCQs: {mcqs} | |
| User Answers: {user_answers} | |
| """ | |
| chat_completion = client.chat.completions.create( | |
| messages=[{"role": "user", "content": prompt}], | |
| model="gemma2-9b-it", # Use a valid Groq-supported model | |
| ) | |
| return chat_completion.choices[0].message.content | |
| # Streamlit App | |
| st.title("Experienced Educator MCQ Generator & Evaluator") | |
| st.sidebar.header("Input Settings") | |
| # Input text from user | |
| user_text = st.sidebar.text_area("Enter the text for generating MCQs:", height=200) | |
| if st.sidebar.button("Generate MCQs"): | |
| if user_text.strip(): | |
| with st.spinner("Generating MCQs..."): | |
| mcqs = generate_mcqs_from_text(user_text) | |
| st.subheader("Generated MCQs:") | |
| st.text_area("MCQs", value=mcqs, height=400) | |
| st.session_state["mcqs"] = mcqs # Store MCQs for evaluation | |
| else: | |
| st.error("Please enter text for generating MCQs.") | |
| # Input user answers for evaluation | |
| if "mcqs" in st.session_state: | |
| st.subheader("Solve the MCQs") | |
| user_answers = st.text_area("Enter your answers (e.g., A, B, C, D for each question):", height=100) | |
| if st.button("Evaluate Answers"): | |
| if user_answers.strip(): | |
| with st.spinner("Evaluating answers..."): | |
| evaluation_result = evaluate_answers(st.session_state["mcqs"], user_answers) | |
| st.subheader("Evaluation Result:") | |
| st.text_area("Evaluation", value=evaluation_result, height=400) | |
| else: | |
| st.error("Please enter your answers for evaluation.") | |