Update README.md

README.md

@@ -11,4 +11,131 @@ hf_oauth: true
 hf_oauth_scopes:
 - email
 sdk_version: 1.44.1
----
+---
+
+# Smart Edit Assistant
+
+**Smart Edit Assistant** is an AI-powered web application that **automates video editing tasks** end-to-end. Users can upload video files, let the system **extract audio**, **transcribe** the speech (e.g., via Whisper), **analyze** content with GPT-like models, and **apply automated cuts and edits** using FFmpeg or MoviePy. The end result is a curated, shorter (or otherwise improved) video that can be downloaded, saving creators time on manual post-production.
+
+## Features
+
+- **Video Upload & Preview**: Upload `.mp4`, `.mov`, or `.mkv` files.
+- **Audio Extraction**: Efficiently pulls the audio track for transcription.
+- **AI Transcription**: Uses OpenAI Whisper (API or local) or other STT solutions.
+- **LLM Content Analysis**: GPT-4 or open-source LLM suggests cuts and highlight segments.
+- **Automated Editing**: Uses FFmpeg/MoviePy to cut and reassemble segments, optionally insert transitions.
+- **Result Preview**: Plays the edited video in-browser before download.
+- **(Optional) User Authentication**: Configurable free vs. premium tiers.
+
+## Repository Structure
+
+smart-edit-assistant/ ├── app.py # Main Streamlit app ├── pipelines/ │ ├── video_process.py # Audio extraction & editing logic (MoviePy / FFmpeg) │ ├── ai_inference.py # Whisper/GPT calls for transcription & instructions │ └── auth_utils.py # Optional authentication logic ├── .streamlit/ │ └── config.toml # Streamlit config (upload limit, theming) ├── requirements.txt # Python dependencies ├── apt.txt # (Optional) System-level dependencies if needed └── README.md # Project description (this file)
+
+bash
+Copy code
+
+## Local Development & Setup
+
+1. **Clone this repo**:
+   ```bash
+   git clone https://github.com/YourUsername/smart-edit-assistant.git
+   cd smart-edit-assistant
+Install Python dependencies:
+
+bash
+Copy code
+pip install -r requirements.txt
+If you plan to run open-source Whisper locally, ensure you install openai-whisper or the GitHub repo (git+https://github.com/openai/whisper.git).
+
+If you’re using GPU, make sure your PyTorch install matches your CUDA version.
+
+Run the app:
+
+bash
+Copy code
+streamlit run app.py
+Open http://localhost:8501 in your browser to interact with the UI.
+
+Set Environment Variables (for GPT or Whisper API, if needed):
+
+bash
+Copy code
+export OPENAI_API_KEY="sk-..."
+or store in a local .env file and load with python-dotenv.
+
+Deploying on Hugging Face Spaces
+Create a Space:
+
+Go to Hugging Face Spaces and create a new Space with the Streamlit SDK option.
+
+Upload your files:
+
+Either drag-and-drop via the web interface or push via Git:
+
+bash
+Copy code
+git remote add origin https://huggingface.co/spaces/YourUsername/Smart-Edit-Assistant
+git push origin main
+Set your secrets:
+
+In the Space’s Settings page, add OPENAI_API_KEY or any other API keys under “Secrets”.
+
+If you want GPU, set hardware: "gpu" in the YAML frontmatter (as shown above) or in the Space settings.
+
+Build and Launch:
+
+The Space will automatically install your requirements.txt and run app.py.
+
+Once deployed, your app is live at https://huggingface.co/spaces/YourUsername/Smart-Edit-Assistant.
+
+Usage
+Upload a Video: Click “Browse files” to select a .mp4, .mov, or .mkv file.
+
+Extract & Transcribe: The app automatically pulls the audio, then uses Whisper or another STT method to get a transcript.
+
+Generate Edits: An LLM (GPT-4 or local) analyzes the transcript and suggests where to cut or remove filler content.
+
+Apply Edits: The app runs ffmpeg or MoviePy to create a new edited video file.
+
+Preview & Download: You can watch the edited clip directly in the browser and then download the .mp4.
+
+Configuration
+Streamlit Config:
+
+.streamlit/config.toml can set maxUploadSize (e.g. 10GB) or color theme.
+
+Authentication:
+
+If hf_oauth is true, users must log in with their Hugging Face account.
+
+For custom username/password or free vs. premium tiers, see auth_utils.py or documentation in your code.
+
+Roadmap
+Interactive Timeline: Let users manually tweak the AI’s suggested cuts.
+
+B-roll Insertion: Generate or fetch recommended B-roll and splice it in automatically.
+
+Transition Effects: Provide crossfades, text overlays, or AI-generated intros/outros.
+
+Multi-user Collaboration: Shared editing session or project saving in a database.
+
+Troubleshooting
+File Not Found or Zero Bytes: Make sure ffmpeg or MoviePy didn’t fail silently. Check logs for errors.
+
+Whisper “load_model” Error: Ensure you installed openai-whisper or the GitHub repo, not the unrelated “whisper” PyPI package.
+
+Large File Upload: If large uploads fail, confirm the maxUploadSize in .streamlit/config.toml is high enough, and verify huggingface secrets/config.
+
+Performance: For best speed, request a GPU from Hugging Face Spaces or use a local GPU with the correct PyTorch/CUDA version.
+
+License
+You can choose a license that suits your project. For example:
+
+java
+Copy code
+MIT License
+
+Copyright (c) 2025 ...
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), ...