import cv2 import numpy as np from transformers import CLIPProcessor, CLIPModel import torch from PIL import Image import faiss import pickle from typing import List, Dict, Tuple import logging import gradio as gr import tempfile import os import shutil class VideoRAGTool: def __init__(self, model_name: str = "openai/clip-vit-base-patch32"): """ Initialize the Video RAG Tool with CLIP model for frame analysis. """ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.model = CLIPModel.from_pretrained(model_name).to(self.device) self.processor = CLIPProcessor.from_pretrained(model_name) self.frame_index = None self.frame_data = [] self.logger = self._setup_logger() def _setup_logger(self) -> logging.Logger: logger = logging.getLogger('VideoRAGTool') logger.setLevel(logging.INFO) handler = logging.StreamHandler() formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') handler.setFormatter(formatter) logger.addHandler(handler) return logger def process_video(self, video_path: str, frame_interval: int = 30) -> None: """Process video file and extract features from frames.""" self.logger.info(f"Processing video: {video_path}") cap = cv2.VideoCapture(video_path) frame_count = 0 features_list = [] while cap.isOpened(): ret, frame = cap.read() if not ret: break if frame_count % frame_interval == 0: frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) image = Image.fromarray(frame_rgb) inputs = self.processor(images=image, return_tensors="pt").to(self.device) image_features = self.model.get_image_features(**inputs) self.frame_data.append({ 'frame_number': frame_count, 'timestamp': frame_count / cap.get(cv2.CAP_PROP_FPS) }) features_list.append(image_features.cpu().detach().numpy()) frame_count += 1 cap.release() if not features_list: raise ValueError("No frames were processed from the video") features_array = np.vstack(features_list) self.frame_index = faiss.IndexFlatL2(features_array.shape[1]) self.frame_index.add(features_array) self.logger.info(f"Processed {len(self.frame_data)} frames from video") def query_video(self, query_text: str, k: int = 5) -> List[Dict]: """Query the video using natural language and return relevant frames.""" self.logger.info(f"Processing query: {query_text}") inputs = self.processor(text=[query_text], return_tensors="pt").to(self.device) text_features = self.model.get_text_features(**inputs) distances, indices = self.frame_index.search( text_features.cpu().detach().numpy(), k ) results = [] for i, (distance, idx) in enumerate(zip(distances[0], indices[0])): frame_info = self.frame_data[idx].copy() frame_info['relevance_score'] = float(1 / (1 + distance)) results.append(frame_info) return results class VideoRAGApp: def __init__(self): self.rag_tool = VideoRAGTool() self.current_video_path = None self.processed = False self.temp_dir = tempfile.mkdtemp() def __del__(self): """Cleanup temporary files on deletion""" if hasattr(self, 'temp_dir') and os.path.exists(self.temp_dir): shutil.rmtree(self.temp_dir, ignore_errors=True) def process_video(self, video_file): """Process uploaded video and return status message""" try: if video_file is None: return "Please upload a video first." # video_file is now a file path provided by Gradio video_path = video_file.name # Create a copy in our temp directory temp_video_path = os.path.join(self.temp_dir, "current_video.mp4") shutil.copy2(video_path, temp_video_path) self.current_video_path = temp_video_path self.rag_tool.process_video(self.current_video_path) self.processed = True return "Video processed successfully! You can now ask questions about the video." except Exception as e: self.processed = False return f"Error processing video: {str(e)}" def query_video(self, query_text): """Query the video and return relevant frames with descriptions""" if not self.processed: return None, "Please process a video first." try: results = self.rag_tool.query_video(query_text, k=4) frames = [] captions = [] cap = cv2.VideoCapture(self.current_video_path) for result in results: frame_number = result['frame_number'] cap.set(cv2.CAP_PROP_POS_FRAMES, frame_number) ret, frame = cap.read() if ret: frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frames.append(Image.fromarray(frame_rgb)) caption = f"Timestamp: {result['timestamp']:.2f}s\n" caption += f"Relevance: {result['relevance_score']:.2f}" captions.append(caption) cap.release() return frames, "\n\n".join(captions) except Exception as e: return None, f"Error querying video: {str(e)}" def create_interface(self): """Create and return Gradio interface""" with gr.Blocks(title="Video Chat RAG") as interface: gr.Markdown("# Video Chat RAG") gr.Markdown("Upload a video and ask questions about its content!") with gr.Row(): video_input = gr.File( label="Upload Video", file_types=["video"], ) process_button = gr.Button("Process Video") status_output = gr.Textbox( label="Status", interactive=False ) with gr.Row(): query_input = gr.Textbox( label="Ask about the video", placeholder="What's happening in the video?" ) query_button = gr.Button("Search") with gr.Row(): gallery = gr.Gallery( label="Retrieved Frames", show_label=True, elem_id="gallery", columns=[2], rows=[2], height="auto" ) captions = gr.Textbox( label="Frame Details", interactive=False ) process_button.click( fn=self.process_video, inputs=[video_input], outputs=[status_output] ) query_button.click( fn=self.query_video, inputs=[query_input], outputs=[gallery, captions] ) return interface # Initialize and create the interface app = VideoRAGApp() interface = app.create_interface() # Launch the app if __name__ == "__main__": interface.launch()