app.py

# import gradio as gr
# import google.generativeai as genai

# genai.configure(api_key="AIzaSyBPQF0g5EfEPzEiGRzA3iNzJZK4jDukMvE")

# model = genai.GenerativeModel('gemini-pro')

# def generate_summary_and_quiz(transcript, num_questions):
#     """Generate a summary and quiz questions based on the video transcript."""
#     prompt = f"""
#     Based on the following video lecture transcript, please provide:
#     1. A concise summary of the main points (about 100 words)
#     2. {num_questions} multiple-choice quiz questions to test understanding of key concepts

#     Transcript:
#     {transcript}

#     Format your response as follows:
#     Summary:
#     [Your summary here]

#     Quiz Questions:
#     1. [Question]
#     a) [Option A]
#     b) [Option B]
#     c) [Option C]
#     d) [Option D]
#     Correct Answer: [Correct option letter]

#     2. [Next question and options...]

#     Ensure the questions cover different aspects of the lecture and vary in difficulty.
#     """

#     try:
#         response = model.generate_content(prompt)
#         return response.text
#     except Exception as e:
#         return f"Error generating summary and quiz: {str(e)}"

# def process_lecture(transcript, num_questions):
#     with gr.Row():
#         gr.Markdown("Generating summary and quiz...")
#     result = generate_summary_and_quiz(transcript, num_questions)
#     return result

# with gr.Blocks() as demo:
#     gr.Markdown("# Video Lecture Summarizer and Quiz Generator")
    
#     transcript_input = gr.Textbox(label="Video Lecture Transcript", lines=10, placeholder="Paste the video transcript or a detailed description of the lecture content here...")
#     num_questions = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="Number of Quiz Questions")
    
#     generate_btn = gr.Button("Generate Summary and Quiz")
#     output = gr.Textbox(label="Summary and Quiz", lines=20)
    
#     generate_btn.click(process_lecture, inputs=[transcript_input, num_questions], outputs=output)

# if __name__ == "__main__":
#     demo.launch()




import gradio as gr
import google.generativeai as genai
import speech_recognition as sr
from pydub import AudioSegment
import os
import tempfile
import moviepy.editor as mp

# Initialize Gemini API
genai.configure(api_key="AIzaSyBPQF0g5EfEPzEiGRzA3iNzJZK4jDukMvE")

# Initialize the Gemini model
model = genai.GenerativeModel('gemini-pro')

def transcribe_video(video_path):
    """Transcribe the audio from a video file."""
    try:
        # Extract audio from video
        video = mp.VideoFileClip(video_path)
        audio_path = tempfile.mktemp(suffix=".wav")
        video.audio.write_audiofile(audio_path, codec='pcm_s16le')
        
        # Load audio file
        audio = AudioSegment.from_wav(audio_path)
        
        # Initialize recognizer
        r = sr.Recognizer()
        
        # Split audio into chunks to handle long audio
        chunk_length_ms = 30000  # 30 seconds
        chunks = [audio[i:i+chunk_length_ms] for i in range(0, len(audio), chunk_length_ms)]
        
        transcript = ""
        for i, chunk in enumerate(chunks):
            # Export chunk to a temporary file
            chunk_path = tempfile.mktemp(suffix=".wav")
            chunk.export(chunk_path, format="wav")
            
            # Recognize speech in the chunk
            with sr.AudioFile(chunk_path) as source:
                audio_listened = r.record(source)
                try:
                    text = r.recognize_google(audio_listened)
                    transcript += text + " "
                except sr.UnknownValueError:
                    print(f"Could not understand audio in chunk {i+1}")
                except sr.RequestError:
                    print(f"Could not request results from Google Speech Recognition service for chunk {i+1}")
            
            # Clean up temporary chunk file
            os.remove(chunk_path)
        
        # Clean up temporary audio file
        os.remove(audio_path)
        
        return transcript.strip()
    except Exception as e:
        return f"Error transcribing video: {str(e)}"

def generate_summary_and_quiz(transcript, num_questions):
    """Generate a summary and quiz questions based on the video transcript."""
    prompt = f"""
    Based on the following video lecture transcript, please provide:
    1. A concise summary of the main points (about 100 words)
    2. {num_questions} multiple-choice quiz questions to test understanding of key concepts

    Transcript:
    {transcript}

    Format your response as follows:
    Summary:
    [Your summary here]

    Quiz Questions:
    1. [Question]
    a) [Option A]
    b) [Option B]
    c) [Option C]
    d) [Option D]
    Correct Answer: [Correct option letter]

    2. [Next question and options...]

    Ensure the questions cover different aspects of the lecture and vary in difficulty.
    """

    try:
        response = model.generate_content(prompt)
        return response.text
    except Exception as e:
        return f"Error generating summary and quiz: {str(e)}"

def process_video(video, num_questions):
    with gr.Row():
        gr.Markdown("Processing video and generating summary and quiz...")
    
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video:
        video.save(temp_video.name)
        video_path = temp_video.name

    transcript = transcribe_video(video_path)
    result = generate_summary_and_quiz(transcript, num_questions)

    os.unlink(video_path)

    return transcript, result

with gr.Blocks() as demo:
    gr.Markdown("# Video Lecture Summarizer and Quiz Generator")
    
    video_input = gr.Video(label="Upload Video Lecture")
    num_questions = gr.Slider(minimum=1, maximum=10, value=3, step=1, label="Number of Quiz Questions")
    
    generate_btn = gr.Button("Process Video and Generate Summary and Quiz")
    
    transcript_output = gr.Textbox(label="Video Transcript", lines=10)
    summary_quiz_output = gr.Textbox(label="Summary and Quiz", lines=20)
    
    generate_btn.click(
        process_video, 
        inputs=[video_input, num_questions], 
        outputs=[transcript_output, summary_quiz_output]
    )

if __name__ == "__main__":
    demo.launch()