app.py

import os
import streamlit as st
import moviepy.editor as mp
import whisper
from transformers import pipeline

# ✅ Ensure ffmpeg is installed (needed for moviepy)
if not os.path.exists("/usr/bin/ffmpeg"):
    os.system("apt-get update && apt-get install -y ffmpeg")

# ✅ Function to extract audio from a video
def extract_audio(video_path, audio_path="audio.wav"):
    if os.path.exists(audio_path):
        os.remove(audio_path)
    video = mp.VideoFileClip(video_path)  # Use mp.VideoFileClip
    video.audio.write_audiofile(audio_path)
    return audio_path

# ✅ Function to transcribe audio using Whisper
def transcribe_audio(audio_path):
    model = whisper.load_model("base", download_root="./models")  # Ensure model is downloaded
    result = model.transcribe(audio_path)
    return result["text"]

# ✅ Function to summarize text
def summarize_text(text):
    summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
    max_chunk_size = 1000
    chunks = [text[i:i + max_chunk_size] for i in range(0, len(text), max_chunk_size)]
    summaries = [summarizer(chunk, max_length=130, min_length=30, do_sample=False)[0]["summary_text"] for chunk in chunks]
    return " ".join(summaries)

# ✅ Function to generate study notes
def generate_study_notes(summary):
    generator = pipeline("text-generation", model="gpt2")
    prompt = f"Create study notes from the following summary:\n{summary}"
    study_notes = generator(prompt, max_length=400, max_new_tokens=200, num_return_sequences=1, truncation=True)
    return study_notes[0]["generated_text"]

# ✅ Function to answer user questions
def answer_question(question, context):
    qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
    result = qa_pipeline(question=question, context=context)
    return result["answer"]

# ✅ Streamlit UI
st.title("Lecture Video Processor 🎥📝")

# File uploader
uploaded_file = st.file_uploader("📤 Upload a video file", type=["mp4", "mov", "avi", "mkv"])

if uploaded_file:
    video_path = uploaded_file.name
    with open(video_path, "wb") as f:
        f.write(uploaded_file.read())

    st.success("✅ Video uploaded successfully!")

    # Extract audio
    st.info("🔊 Extracting audio...")
    audio_path = extract_audio(video_path)
    st.success("✅ Audio extracted!")

    # Transcribe audio
    st.info("🎙️ Transcribing audio...")
    transcript = transcribe_audio(audio_path)
    st.text_area("📜 Transcript", transcript, height=200)

    # Summarize transcript
    st.info("📄 Summarizing transcript...")
    video_summary = summarize_text(transcript)
    st.text_area("📌 Summary", video_summary, height=150)

    # Generate study notes
    st.info("📝 Generating study notes...")
    study_notes = generate_study_notes(video_summary)
    st.text_area("📖 Study Notes", study_notes, height=150)

    # Q&A Section
    question = st.text_input("❓ Ask a question about the video:")
    if question:
        answer = answer_question(question, video_summary)
        st.write("💡 Answer:", answer)