app.py

import cv2
import numpy as np
from transformers import CLIPProcessor, CLIPModel, Blip2Processor, Blip2ForConditionalGeneration
import torch
from PIL import Image
import faiss
import logging
import gradio as gr
import tempfile
import os
import shutil
from tqdm.auto import tqdm
from pathlib import Path
from typing import List, Dict, Tuple
import time
class VideoProcessor:
    def __init__(self):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        
        # Load models with optimizations
        self.load_models()
        
        # Processing settings
        self.frame_interval = 30  # Process 1 frame every 30 frames
        self.max_frames = 50      # Maximum frames to process
        self.target_size = (224, 224)
        self.batch_size = 4 if torch.cuda.is_available() else 2

    def load_models(self):
        """Load models with optimizations"""
        # Load CLIP
        self.clip_model = CLIPModel.from_pretrained(
            "openai/clip-vit-base-patch32",
            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
        ).to(self.device)
        self.clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

        # Load BLIP2 with reduced size
        self.blip_model = Blip2ForConditionalGeneration.from_pretrained(
            "Salesforce/blip2-opt-2.7b",
            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
            device_map="auto" if torch.cuda.is_available() else None
        ).to(self.device)
        self.blip_processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")

        # Set models to evaluation mode
        self.clip_model.eval()
        self.blip_model.eval()

    @torch.no_grad()
    def process_frame_batch(self, frames):
        """Process a batch of frames efficiently"""
        try:
            # Convert frames to PIL Images
            pil_frames = [Image.fromarray(cv2.cvtColor(f, cv2.COLOR_BGR2RGB)).resize(self.target_size) for f in frames]
            
            # Get CLIP features
            clip_inputs = self.clip_processor(images=pil_frames, return_tensors="pt", padding=True).to(self.device)
            if self.device.type == "cuda":
                clip_inputs = {k: v.half() if v.dtype == torch.float32 else v for k, v in clip_inputs.items()}
            features = self.clip_model.get_image_features(**clip_inputs)
            
            # Get BLIP captions
            blip_inputs = self.blip_processor(images=pil_frames, return_tensors="pt", padding=True).to(self.device)
            if self.device.type == "cuda":
                blip_inputs = {k: v.half() if v.dtype == torch.float32 else v for k, v in blip_inputs.items()}
            captions = self.blip_model.generate(**blip_inputs, max_length=30)
            captions = [self.blip_processor.decode(c, skip_special_tokens=True) for c in captions]
            
            return features.cpu().numpy(), captions
        except Exception as e:
            print(f"Error in batch processing: {str(e)}")
            return None, None

    def process_video(self, video_path: str, progress=gr.Progress()):
        """Process video with batching and progress updates"""
        cap = cv2.VideoCapture(video_path)
        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        fps = cap.get(cv2.CAP_PROP_FPS)
        
        # Calculate frames to process
        frames_to_process = min(self.max_frames, total_frames // self.frame_interval)
        progress(0, desc="Initializing...")
        
        features_list = []
        frame_data = []
        current_batch = []
        batch_positions = []
        
        try:
            frame_count = 0
            processed_count = 0
            
            while cap.isOpened() and processed_count < frames_to_process:
                ret, frame = cap.read()
                if not ret:
                    break
                    
                if frame_count % self.frame_interval == 0:
                    current_batch.append(frame)
                    batch_positions.append(frame_count)
                    
                    # Process batch when full
                    if len(current_batch) == self.batch_size or frame_count == total_frames - 1:
                        features, captions = self.process_frame_batch(current_batch)
                        
                        if features is not None and captions is not None:
                            for i, (feat, cap) in enumerate(zip(features, captions)):
                                features_list.append(feat)
                                frame_data.append({
                                    'frame_number': batch_positions[i],
                                    'timestamp': batch_positions[i] / fps,
                                    'caption': cap
                                })
                        
                        processed_count += len(current_batch)
                        current_batch = []
                        batch_positions = []
                        
                        # Update progress
                        progress(processed_count / frames_to_process, 
                               desc=f"Processing frames... {processed_count}/{frames_to_process}")
                
                frame_count += 1
                
            cap.release()
            
            # Create FAISS index
            if features_list:
                features_array = np.vstack(features_list)
                frame_index = faiss.IndexFlatL2(features_array.shape[1])
                frame_index.add(features_array)
                
                return frame_index, frame_data, "Video processed successfully!"
            else:
                return None, None, "No frames were processed successfully."
                
        except Exception as e:
            cap.release()
            return None, None, f"Error processing video: {str(e)}"

class VideoQAInterface:
    def __init__(self):
        self.processor = VideoProcessor()
        self.frame_index = None
        self.frame_data = None
        self.processed = False
        self.current_video_path = None  # Store the video path
        self.temp_dir = tempfile.mkdtemp()

    def __del__(self):
        """Cleanup temporary files"""
        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, progress=gr.Progress()):
        """Process video with progress tracking"""
        try:
            if video_file is None:
                return "Please upload a video first."
            
            # Save uploaded video to temp directory
            temp_video_path = os.path.join(self.temp_dir, "input_video.mp4")
            shutil.copy2(video_file.name, temp_video_path)
            self.current_video_path = temp_video_path
            
            progress(0, desc="Starting video processing...")
            self.frame_index, self.frame_data, message = self.processor.process_video(
                self.current_video_path, progress
            )
            
            if self.frame_index is not None:
                self.processed = True
                return "Video processed successfully! You can now ask questions."
            else:
                self.processed = False
                return message
                
        except Exception as e:
            self.processed = False
            return f"Error: {str(e)}"

    @torch.no_grad()
    def answer_question(self, query):
        """Answer questions about the video"""
        if not self.processed or self.current_video_path is None:
            return None, "Please process a video first."
            
        try:
            # Get query features
            inputs = self.processor.clip_processor(text=[query], return_tensors="pt").to(self.processor.device)
            query_features = self.processor.clip_model.get_text_features(**inputs)
            
            # Search for relevant frames
            k = 4  # Number of frames to retrieve
            D, I = self.frame_index.search(query_features.cpu().numpy(), k)
            
            results = []
            for distance, idx in zip(D[0], I[0]):
                frame_info = self.frame_data[idx].copy()
                frame_info['relevance'] = float(1 / (1 + distance))
                results.append(frame_info)
            
            # Format output
            descriptions = []
            frames = []
            
            # Use cv2.VideoCapture to read frames
            cap = cv2.VideoCapture(self.current_video_path)
            try:
                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))
                        
                        desc = f"Timestamp: {result['timestamp']:.2f}s\n"
                        desc += f"Scene Description: {result['caption']}\n"
                        desc += f"Relevance Score: {result['relevance']:.2f}"
                        descriptions.append(desc)
            finally:
                cap.release()  # Ensure video capture is released
            
            if not frames:
                return None, "No relevant frames found."
            
            combined_desc = "\n\nFrame Analysis:\n\n"
            for i, desc in enumerate(descriptions, 1):
                combined_desc += f"Frame {i}:\n{desc}\n\n"
                
            return frames, combined_desc
            
        except Exception as e:
            return None, f"Error answering question: {str(e)}"

    def create_interface(self):
        """Create Gradio interface"""
        with gr.Blocks(title="Advanced Video Question Answering") as interface:
            gr.Markdown("# Advanced Video Question Answering")
            gr.Markdown("Upload a video and ask questions about any aspect of its content!")
            
            with gr.Row():
                with gr.Column():
                    video_input = gr.File(
                        label="Upload Video",
                        file_types=["video"]
                    )
                    status = gr.Textbox(label="Status", interactive=False)
                    process_btn = gr.Button("Process Video")
            
            with gr.Row():
                query_input = gr.Textbox(
                    label="Ask about the video",
                    placeholder="What's happening in the video?"
                )
                query_btn = gr.Button("Search")
            
            gallery = gr.Gallery(
                label="Retrieved Frames",
                show_label=True,
                columns=[2],
                rows=[2]
            )
            
            descriptions = gr.Textbox(
                label="Analysis",
                interactive=False,
                lines=10
            )
            
            # Set up event handlers
            process_btn.click(
                fn=self.process_video,
                inputs=[video_input],
                outputs=[status]
            )
            
            query_btn.click(
                fn=self.answer_question,
                inputs=[query_input],
                outputs=[gallery, descriptions]
            )
        
        return interface

# Create and launch the app
app = VideoQAInterface()
interface = app.create_interface()

if __name__ == "__main__":
    interface.launch()