app.py

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()