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| import streamlit as st | |
| import os | |
| import subprocess | |
| def main(): | |
| st.title("Smart Edit Assistant") | |
| uploaded_file = st.file_uploader("Upload your video", type=["mp4", "mov", "mkv"]) | |
| if uploaded_file: | |
| with open("temp_input.mp4", "wb") as f: | |
| f.write(uploaded_file.getbuffer()) | |
| st.video("temp_input.mp4") | |
| if st.button("Process Video"): | |
| # 1. Extract audio using FFmpeg (example) | |
| with st.spinner("Extracting audio..."): | |
| audio_path = extract_audio_ffmpeg("temp_input.mp4", "temp_audio.wav") | |
| # 2. Transcribe audio (placeholder function; use openai-whisper or local) | |
| with st.spinner("Transcribing..."): | |
| transcript_text = transcribe_audio(audio_path) | |
| st.text_area("Transcript", transcript_text, height=200) | |
| # 3. Generate instructions (placeholder function; calls GPT or open-source LLM) | |
| with st.spinner("Generating edit instructions..."): | |
| edit_instructions = generate_edit_instructions(transcript_text) | |
| st.write("AI Edit Instructions:", edit_instructions) | |
| # 4. Apply Edits with FFmpeg | |
| with st.spinner("Applying edits..."): | |
| edited_video_path = apply_edits("temp_input.mp4", edit_instructions) | |
| # 5. Verify output file | |
| abs_edited_path = os.path.join(os.getcwd(), edited_video_path) | |
| if not os.path.exists(abs_edited_path): | |
| st.error(f"Edited video file not found at '{abs_edited_path}'. Check logs.") | |
| return | |
| file_size = os.path.getsize(abs_edited_path) | |
| if file_size == 0: | |
| st.error(f"Edited video file is empty (0 bytes). Check ffmpeg or editing logic.") | |
| return | |
| st.success("Edit complete! Now previewing the edited video.") | |
| st.video(abs_edited_path) # pass the absolute path | |
| with open(abs_edited_path, "rb") as f_out: | |
| st.download_button( | |
| label="Download Edited Video", | |
| data=f_out, | |
| file_name="edited_result.mp4", | |
| mime="video/mp4", | |
| ) | |
| def extract_audio_ffmpeg(input_video, output_audio): | |
| """ | |
| Calls ffmpeg to extract audio. Returns path if successful; raises if not. | |
| """ | |
| cmd = [ | |
| "ffmpeg", "-y", | |
| "-i", input_video, | |
| "-vn", # no video | |
| "-acodec", "pcm_s16le", | |
| "-ar", "16000", | |
| "-ac", "1", | |
| output_audio | |
| ] | |
| result = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) | |
| if result.returncode != 0: | |
| raise RuntimeError(f"ffmpeg error: {result.stderr.decode()}") | |
| return output_audio | |
| def transcribe_audio(audio_path): | |
| """ | |
| Placeholder for your transcription logic (whisper / openai-whisper). | |
| """ | |
| return "This is a mock transcript." | |
| def generate_edit_instructions(transcript_text): | |
| """ | |
| Placeholder for GPT/LLM-based instructions. | |
| Return string or structured instructions (like JSON). | |
| """ | |
| return "Keep everything; no major edits." | |
| def apply_edits(input_video, edit_instructions): | |
| """ | |
| Demo function: We'll just copy input to output to show a valid flow. | |
| In practice, you'd parse instructions & cut the video with ffmpeg or moviepy. | |
| """ | |
| output_video = "edited_video.mp4" | |
| # For demonstration, let's do a direct copy with ffmpeg: | |
| cmd = ["ffmpeg", "-y", "-i", input_video, "-c", "copy", output_video] | |
| result = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) | |
| if result.returncode != 0: | |
| raise RuntimeError(f"ffmpeg editing error: {result.stderr.decode()}") | |
| return output_video | |
| if __name__ == "__main__": | |
| main() | |