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| import openai | |
| import streamlit as st | |
| from youtube_transcript_api import YouTubeTranscriptApi | |
| import re | |
| import tempfile | |
| import os | |
| from transformers import WhisperProcessor, WhisperForConditionalGeneration | |
| import torch | |
| import librosa | |
| # Load the Whisper model and processor from Hugging Face | |
| processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2") | |
| model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2") | |
| def transcribe_audio(file_path): | |
| # Load audio file | |
| audio, _ = librosa.load(file_path, sr=16000) | |
| # Tokenize the audio | |
| inputs = processor(audio, return_tensors="pt", sampling_rate=16000) | |
| # Perform the transcription | |
| with torch.no_grad(): | |
| generated_ids = model.generate(inputs["input_features"]) | |
| # Decode the transcription | |
| transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] | |
| return transcription | |
| def get_transcript(url): | |
| try: | |
| video_id_match = re.search(r"(?:v=|\/)([0-9A-Za-z_-]{11}).*", url) | |
| if video_id_match: | |
| video_id = video_id_match.group(1) | |
| else: | |
| return "Error: Invalid YouTube URL" | |
| transcript = YouTubeTranscriptApi.get_transcript(video_id) | |
| transcript_text = ' '.join([entry['text'] for entry in transcript]) | |
| return transcript_text | |
| except Exception as e: | |
| return str(e) | |
| def summarize_text(api_key, text): | |
| openai.api_key = api_key | |
| response = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": f"Summarize the following text:\n\n{text}"} | |
| ], | |
| max_tokens=150 | |
| ) | |
| summary = response.choices[0]['message']['content'].strip() | |
| return summary | |
| def generate_quiz_questions(api_key, text): | |
| openai.api_key = api_key | |
| response = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": f"Generate ten quiz questions and four multiple choice answers for each question from the following text. Mark the correct answer with an asterisk (*) at the beginning:\n\n{text}"} | |
| ], | |
| max_tokens=300 | |
| ) | |
| quiz_questions = response.choices[0]['message']['content'].strip() | |
| return quiz_questions | |
| def parse_quiz_questions(quiz_text): | |
| questions = [] | |
| question_blocks = quiz_text.split("\n\n") | |
| for block in question_blocks: | |
| lines = block.strip().split("\n") | |
| if len(lines) >= 5: | |
| question = lines[0] | |
| choices = [line.replace('*', '').strip() for line in lines[1:5]] | |
| correct_answer_lines = [line for line in lines[1:5] if '*' in line] | |
| if correct_answer_lines: | |
| correct_answer = correct_answer_lines[0].replace('*', '').strip() | |
| else: | |
| correct_answer = "No correct answer provided" | |
| questions.append({"question": question, "choices": choices, "correct_answer": correct_answer}) | |
| return questions | |
| def generate_explanation(api_key, question, correct_answer, user_answer): | |
| openai.api_key = api_key | |
| prompt = f"Explain why the correct answer to the following question is '{correct_answer}' and not '{user_answer}':\n\n{question}" | |
| response = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", | |
| messages=[ | |
| {"role": "system", "content": "You are a helpful assistant."}, | |
| {"role": "user", "content": prompt} | |
| ], | |
| max_tokens=150 | |
| ) | |
| explanation = response.choices[0]['message']['content'].strip() | |
| return explanation | |
| def check_answers(api_key, questions, user_answers): | |
| feedback = [] | |
| correct_count = 0 | |
| for i, question in enumerate(questions): | |
| correct_answer = question['correct_answer'] | |
| user_answer = user_answers.get(f"question_{i+1}", "") | |
| if user_answer == correct_answer: | |
| feedback.append({ | |
| "question": question['question'], | |
| "user_answer": user_answer, | |
| "correct_answer": correct_answer, | |
| "status": "Correct" | |
| }) | |
| correct_count += 1 | |
| else: | |
| explanation = generate_explanation(api_key, question['question'], correct_answer, user_answer) | |
| feedback.append({ | |
| "question": question['question'], | |
| "user_answer": user_answer, | |
| "correct_answer": correct_answer, | |
| "status": "Incorrect", | |
| "explanation": explanation | |
| }) | |
| return feedback | |
| def handle_uploaded_file(uploaded_file): | |
| with tempfile.NamedTemporaryFile(delete=False) as tmp_file: | |
| tmp_file.write(uploaded_file.read()) | |
| tmp_file_path = tmp_file.name | |
| return tmp_file_path | |
| st.title("YouTube Transcript Quiz Generator") | |
| st.markdown("**Instructions:** Enter your OpenAI API key and paste a YouTube link or upload a media file to generate a quiz.") | |
| api_key = st.text_input("Enter your OpenAI API Key", type="password") | |
| option = st.selectbox("Choose input type", ("YouTube URL", "Upload audio/video file")) | |
| if "generated_quiz" not in st.session_state: | |
| st.session_state.generated_quiz = False | |
| if option == "YouTube URL": | |
| url = st.text_input("YouTube URL", value="") | |
| if api_key and url: | |
| if st.button("Generate Quiz"): | |
| transcript_text = get_transcript(url) | |
| if "Error" not in transcript_text: | |
| summary = summarize_text(api_key, transcript_text) | |
| quiz_text = generate_quiz_questions(api_key, transcript_text) | |
| questions = parse_quiz_questions(quiz_text) | |
| st.write("## Summary") | |
| st.write(summary) | |
| st.write("## Quiz Questions") | |
| st.session_state.questions = questions | |
| st.session_state.user_answers = {} | |
| st.session_state.generated_quiz = True | |
| for i, question in enumerate(questions): | |
| st.write(f"### Question {i+1}") | |
| st.write(question['question']) | |
| st.session_state.user_answers[f"question_{i+1}"] = st.radio( | |
| label="", | |
| options=question['choices'], | |
| key=f"question_{i+1}" | |
| ) | |
| elif option == "Upload audio/video file": | |
| uploaded_file = st.file_uploader("Choose an audio or video file", type=["mp3", "wav", "mp4", "mov"]) | |
| if uploaded_file and api_key: | |
| tmp_file_path = handle_uploaded_file(uploaded_file) | |
| transcript_text = transcribe_audio(tmp_file_path) | |
| os.remove(tmp_file_path) | |
| if "Error" not in transcript_text: | |
| summary = summarize_text(api_key, transcript_text) | |
| quiz_text = generate_quiz_questions(api_key, transcript_text) | |
| questions = parse_quiz_questions(quiz_text) | |
| st.write("## Summary") | |
| st.write(summary) | |
| st.write("## Quiz Questions") | |
| st.session_state.questions = questions | |
| st.session_state.user_answers = {} | |
| st.session_state.generated_quiz = True | |
| for i, question in enumerate(questions): | |
| st.write(f"### Question {i+1}") | |
| st.write(question['question']) | |
| st.session_state.user_answers[f"question_{i+1}"] = st.radio( | |
| label="", | |
| options=question['choices'], | |
| key=f"question_{i+1}" | |
| ) | |
| if st.session_state.generated_quiz: | |
| if st.button("Submit Answers"): | |
| if "questions" in st.session_state and st.session_state.questions: | |
| with st.spinner('Processing your answers...'): | |
| feedback = check_answers(api_key, st.session_state.questions, st.session_state.user_answers) | |
| st.write("## Feedback") | |
| for i, item in enumerate(feedback): | |
| with st.expander(f"Question {i+1} Feedback"): | |
| st.write(f"### {item['question']}") | |
| st.write(f"**Your answer:** {item['user_answer']}") | |
| st.write(f"**Correct answer:** {item['correct_answer']}") | |
| if item['status'] == "Incorrect": | |
| st.write(f"**Explanation:** {item['explanation']}") | |
| else: | |
| st.write("Please generate the quiz first.") | |