app.py

import streamlit as st
import os
from pipelines.video_process import extract_audio_ffmpeg, apply_edits
from pipelines.ai_inference import transcribe_audio, generate_edit_instructions
# from pipelines.auth_utils import check_auth_status  # If using custom
import openai

def main():
    st.title("Smart Edit Assistant 🎬")
    # Check if user is logged in if using custom auth
    # user_info = check_auth_status()
    # if not user_info: 
    #     st.stop()

    openai_api_key = os.getenv("OPENAI_API_KEY", "")
    if openai_api_key:
        openai.api_key = openai_api_key
    else:
        st.warning("No OpenAI API key found in environment.")

    uploaded_file = st.file_uploader("Upload your video", type=["mp4", "mov", "mkv"])
    if uploaded_file is not None:
        with open("temp_input.mp4", "wb") as f:
            f.write(uploaded_file.getbuffer())

        st.video("temp_input.mp4")
        
        if st.button("Process Video"):
            with st.spinner("Extracting audio..."):
                audio_path = extract_audio_ffmpeg("temp_input.mp4", "temp_audio.wav")

            with st.spinner("Transcribing..."):
                transcript_text = transcribe_audio(audio_path)  # calls either local or API-based Whisper

            st.text_area("Transcript", transcript_text, height=200)

            with st.spinner("Generating edit instructions with GPT..."):
                edit_instructions = generate_edit_instructions(transcript_text)

            st.write(edit_instructions)

            with st.spinner("Applying edits..."):
                edited_video_path = apply_edits("temp_input.mp4", edit_instructions)
            
            st.success("Done!")
            st.video(edited_video_path)
            with open(edited_video_path, "rb") as f_out:
                st.download_button("Download Edited Video", data=f_out, file_name="edited_result.mp4", mime="video/mp4")

if __name__ == "__main__":
    main()