app.py

import cv2
import numpy as np
from transformers import CLIPProcessor, CLIPModel, BlipProcessor, BlipForConditionalGeneration
import torch
from PIL import Image
import faiss
from typing import List, Dict, Tuple
import logging
import gradio as gr
import tempfile
import os
import shutil
from tqdm import tqdm
import math

class VideoRAGTool:
    def __init__(self, clip_model_name: str = "openai/clip-vit-base-patch32",
                 blip_model_name: str = "Salesforce/blip-image-captioning-base"):
        """Initialize with performance optimizations."""
        # Setup logger first to avoid the attribute error
        self.logger = self.setup_logger()
        
        self.logger.info("Initializing VideoRAGTool...")
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.logger.info(f"Using device: {self.device}")
        
        # Initialize models with optimization flags
        self.clip_model = CLIPModel.from_pretrained(clip_model_name).to(self.device)
        self.clip_processor = CLIPProcessor.from_pretrained(clip_model_name)
        self.blip_processor = BlipProcessor.from_pretrained(blip_model_name)
        self.blip_model = BlipForConditionalGeneration.from_pretrained(blip_model_name).to(self.device)
        
        # Enable eval mode for inference
        self.clip_model.eval()
        self.blip_model.eval()
        
        # Batch processing settings
        self.batch_size = 4  # Reduced batch size for better memory management
        
        self.frame_index = None
        self.frame_data = []

    def setup_logger(self) -> logging.Logger:
        """Set up logging configuration."""
        logger = logging.getLogger('VideoRAGTool')
        
        # Clear any existing handlers
        if logger.handlers:
            logger.handlers.clear()
            
        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

    @torch.no_grad()
    def generate_caption(self, image: Image.Image) -> str:
        """Optimized caption generation."""
        try:
            inputs = self.blip_processor(image, return_tensors="pt").to(self.device)
            out = self.blip_model.generate(**inputs, max_length=30, num_beams=2)
            caption = self.blip_processor.decode(out[0], skip_special_tokens=True)
            return caption
        except Exception as e:
            self.logger.error(f"Error generating caption: {str(e)}")
            return "Caption generation failed"

    def get_video_info(self, video_path: str) -> Tuple[int, float]:
        """Get video frame count and FPS."""
        cap = cv2.VideoCapture(video_path)
        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        fps = cap.get(cv2.CAP_PROP_FPS)
        cap.release()
        return total_frames, fps

    def preprocess_frame(self, frame: np.ndarray, target_size: Tuple[int, int] = (224, 224)) -> Image.Image:
        """Preprocess frame with resizing for efficiency."""
        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        image = Image.fromarray(frame_rgb)
        return image.resize(target_size, Image.LANCZOS)

    @torch.no_grad()
    def process_batch(self, frames: List[Image.Image]) -> Tuple[np.ndarray, List[str]]:
        """Process a batch of frames efficiently."""
        try:
            # CLIP processing
            clip_inputs = self.clip_processor(images=frames, return_tensors="pt", padding=True).to(self.device)
            image_features = self.clip_model.get_image_features(**clip_inputs)
            
            # BLIP processing
            captions = []
            for frame in frames:
                caption = self.generate_caption(frame)
                captions.append(caption)
            
            return image_features.cpu().numpy(), captions
        except Exception as e:
            self.logger.error(f"Error processing batch: {str(e)}")
            raise

    def process_video(self, video_path: str, frame_interval: int = 30) -> None:
        """Optimized video processing with batching and progress tracking."""
        self.logger.info(f"Processing video: {video_path}")
        
        try:
            total_frames, fps = self.get_video_info(video_path)
            cap = cv2.VideoCapture(video_path)
            
            # Calculate total batches for progress bar
            frames_to_process = total_frames // frame_interval
            total_batches = math.ceil(frames_to_process / self.batch_size)
            
            current_batch = []
            features_list = []
            frame_count = 0
            
            with tqdm(total=frames_to_process, desc="Processing frames") as pbar:
                while cap.isOpened():
                    ret, frame = cap.read()
                    if not ret:
                        break
                    
                    if frame_count % frame_interval == 0:
                        # Preprocess frame
                        processed_frame = self.preprocess_frame(frame)
                        current_batch.append(processed_frame)
                        
                        # Process batch when it reaches batch_size
                        if len(current_batch) == self.batch_size:
                            batch_features, batch_captions = self.process_batch(current_batch)
                            
                            # Store results
                            for i, (features, caption) in enumerate(zip(batch_features, batch_captions)):
                                batch_frame_number = frame_count - (self.batch_size - i - 1) * frame_interval
                                self.frame_data.append({
                                    'frame_number': batch_frame_number,
                                    'timestamp': batch_frame_number / fps,
                                    'caption': caption
                                })
                                features_list.append(features)
                            
                            current_batch = []
                            pbar.update(self.batch_size)
                    
                    frame_count += 1
                
                # Process remaining frames
                if current_batch:
                    batch_features, batch_captions = self.process_batch(current_batch)
                    for i, (features, caption) in enumerate(zip(batch_features, batch_captions)):
                        batch_frame_number = frame_count - (len(current_batch) - i - 1) * frame_interval
                        self.frame_data.append({
                            'frame_number': batch_frame_number,
                            'timestamp': batch_frame_number / fps,
                            'caption': caption
                        })
                        features_list.append(features)
            
            cap.release()
            
            if not features_list:
                raise ValueError("No frames were processed from the video")
            
            # Create FAISS index
            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")
            
        except Exception as e:
            self.logger.error(f"Error processing video: {str(e)}")
            raise

    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}")
        
        try:
            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
        except Exception as e:
            self.logger.error(f"Error querying video: {str(e)}")
            raise

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()