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| import sys | |
| import subprocess | |
| import pkg_resources | |
| import os | |
| import tempfile | |
| import requests | |
| required_packages = { | |
| 'torch': 'torch', | |
| 'gradio': 'gradio', | |
| 'transformers': 'transformers', | |
| 'decord': 'decord', | |
| 'numpy': 'numpy', | |
| 'instaloader': 'instaloader' | |
| } | |
| def install_packages(packages): | |
| for package in packages: | |
| subprocess.check_call([sys.executable, "-m", "pip", "install", package]) | |
| def check_and_install_packages(): | |
| installed_packages = {pkg.key for pkg in pkg_resources.working_set} | |
| missing_packages = [required_packages[pkg] for pkg in required_packages if pkg not in installed_packages] | |
| if missing_packages: | |
| print("Installing missing packages...") | |
| install_packages(missing_packages) | |
| print("Packages installed successfully.") | |
| else: | |
| print("All required packages are already installed.") | |
| # Check and install required packages | |
| check_and_install_packages() | |
| # Now import the required modules | |
| import gradio as gr | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from decord import VideoReader, cpu | |
| import numpy as np | |
| import instaloader | |
| # Initialize Instaloader | |
| L = instaloader.Instaloader() | |
| def download_instagram_video(url): | |
| try: | |
| # Use a third-party API service like savefrom.net | |
| api_url = f"https://api.savefrom.net/1/info?url={url}" | |
| response = requests.get(api_url) | |
| data = response.json() | |
| if 'url' in data: | |
| video_url = data['url'] | |
| video_data = requests.get(video_url) | |
| # Save the video file locally | |
| with open('downloaded_reel.mp4', 'wb') as f: | |
| f.write(video_data.content) | |
| return 'downloaded_reel.mp4' | |
| else: | |
| print(f"Error: {data.get('error', 'Unknown error')}") | |
| return None | |
| except Exception as e: | |
| print(f"Error downloading video: {e}") | |
| return None | |
| def process_video(video_path, max_frames=64): | |
| vr = VideoReader(video_path, ctx=cpu(0)) | |
| total_frames = len(vr) | |
| frame_indices = np.linspace(0, total_frames - 1, max_frames, dtype=int) | |
| frames = vr.get_batch(frame_indices).asnumpy() | |
| return frames | |
| # This is a placeholder for the actual LLaVA-Video model | |
| def analyze_video(video_frames, question): | |
| # In a real implementation, you would use the LLaVA-Video model here | |
| return f"Analyzed {len(video_frames)} frames. Your question was: {question}" | |
| def analyze_instagram_video(video_url, question): | |
| if not video_url: | |
| return "Please enter an Instagram video URL." | |
| video_path = download_instagram_video(video_url) | |
| if not video_path: | |
| return "Failed to download the video. Please check the URL and try again." | |
| video_frames = process_video(video_path) | |
| response = analyze_video(video_frames, question) | |
| return response | |
| # Create Gradio interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# 🎥 Instagram Video Analyzer") | |
| gr.Markdown("Enter the URL of an Instagram video and ask questions about its content!") | |
| with gr.Row(): | |
| with gr.Column(): | |
| video_url_input = gr.Textbox(label="Instagram Video URL", placeholder="https://www.instagram.com/p/...") | |
| question_input = gr.Textbox(label="Ask a question about the video", placeholder="What's happening in this Instagram video?") | |
| submit_button = gr.Button("Analyze Video") | |
| output = gr.Textbox(label="Analysis Result") | |
| submit_button.click( | |
| fn=analyze_instagram_video, | |
| inputs=[video_url_input, question_input], | |
| outputs=output | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |