import cv2 import numpy as np from transformers import CLIPProcessor, CLIPModel, BlipProcessor, BlipForConditionalGeneration 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, clip_model_name: str = "openai/clip-vit-base-patch32", blip_model_name: str = "Salesforce/blip-image-captioning-base"): """ Initialize the Video RAG Tool with CLIP and BLIP models for frame analysis and captioning. """ self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Initialize CLIP for frame retrieval self.clip_model = CLIPModel.from_pretrained(clip_model_name).to(self.device) self.clip_processor = CLIPProcessor.from_pretrained(clip_model_name) # Initialize BLIP for image captioning self.blip_processor = BlipProcessor.from_pretrained(blip_model_name) self.blip_model = BlipForConditionalGeneration.from_pretrained(blip_model_name).to(self.device) 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 generate_caption(self, image: Image.Image) -> str: """Generate a description for the given image using BLIP.""" inputs = self.blip_processor(image, return_tensors="pt").to(self.device) out = self.blip_model.generate(**inputs) caption = self.blip_processor.decode(out[0], skip_special_tokens=True) return caption 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) # Generate caption for the frame caption = self.generate_caption(image) # Process frame with CLIP inputs = self.clip_processor(images=image, return_tensors="pt").to(self.device) image_features = self.clip_model.get_image_features(**inputs) self.frame_data.append({ 'frame_number': frame_count, 'timestamp': frame_count / cap.get(cv2.CAP_PROP_FPS), 'caption': caption }) 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.clip_processor(text=[query_text], return_tensors="pt").to(self.device) text_features = self.clip_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_path = video_file.name 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 = [] descriptions = [] 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)) description = f"Timestamp: {result['timestamp']:.2f}s\n" description += f"Scene Description: {result['caption']}\n" description += f"Relevance Score: {result['relevance_score']:.2f}" descriptions.append(description) cap.release() # Combine all descriptions with frame numbers combined_description = "\n\nFrame Analysis:\n\n" for i, desc in enumerate(descriptions, 1): combined_description += f"Frame {i}:\n{desc}\n\n" return frames, combined_description 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" ) descriptions = gr.Textbox( label="Scene Descriptions", interactive=False, lines=10 ) 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, descriptions] ) return interface # Initialize and create the interface app = VideoRAGApp() interface = app.create_interface() # Launch the app if __name__ == "__main__": interface.launch()