app.py

import streamlit as st
import cv2
import torch
from PIL import Image
import numpy as np
from transformers import BlipProcessor, BlipForConditionalGeneration
from transformers import ViltProcessor, ViltForQuestionAnswering
import time
from io import BytesIO
import threading
import queue
import os
import tempfile
from datetime import datetime

# Set page config to wide mode
st.set_page_config(layout="wide", page_title="Securade.ai Sentinel")

def initialize_state():
    if 'initialized' not in st.session_state:
        st.session_state.frame = None
        st.session_state.captions = []
        st.session_state.stop_event = threading.Event()
        st.session_state.frame_queue = queue.Queue(maxsize=1)
        st.session_state.caption_queue = queue.Queue(maxsize=10)
        st.session_state.processor = None
        st.session_state.thread = None
        st.session_state.is_streaming = False
        st.session_state.initialized = True

@st.cache_resource
def load_processor():
    class VideoProcessor:
        def __init__(self):
            self.caption_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
            self.caption_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
            self.vqa_processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
            self.vqa_model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
            
            # Check for available devices
            if torch.cuda.is_available():
                self.device = "cuda"
            elif torch.backends.mps.is_available():
                self.device = "mps"
            else:
                self.device = "cpu"
            
            self.caption_model.to(self.device)
            self.vqa_model.to(self.device)

        def generate_caption(self, image):
            inputs = self.caption_processor(images=image, return_tensors="pt").to(self.device)
            output = self.caption_model.generate(**inputs, max_new_tokens=50)
            return self.caption_processor.decode(output[0], skip_special_tokens=True)

        def answer_question(self, image, question):
            inputs = self.vqa_processor(image, question, return_tensors="pt").to(self.device)
            outputs = self.vqa_model(**inputs)
            logits = outputs.logits
            idx = logits.argmax(-1).item()
            return self.vqa_model.config.id2label[idx]

    return VideoProcessor()

def get_video_source(source_type, source_path=None):
    if source_type == "Webcam":
        return cv2.VideoCapture(0)
    elif source_type == "Video File" and source_path:
        # Create a temporary file with a specific extension
        temp_dir = tempfile.gettempdir()
        temp_path = os.path.join(temp_dir, 'temp_video.mp4')
        with open(temp_path, 'wb') as f:
            f.write(source_path.getvalue())
        
        cap = cv2.VideoCapture(temp_path)
        if not cap.isOpened():
            st.error("Error: Could not open video file. Please ensure it's a supported format (MP4 with H.264 encoding recommended)")
            return None
        return cap
    elif source_type == "RTSP Stream" and source_path:
        return cv2.VideoCapture(source_path)
    return None

def process_video(stop_event, frame_queue, caption_queue, processor, source_type, source_path=None):
    cap = get_video_source(source_type, source_path)
    last_caption_time = time.time()

    while not stop_event.is_set():
        ret, frame = cap.read()
        if not ret:
            break

        frame = cv2.resize(frame, (800, 600))
        current_time = time.time()

        # Generate caption every 8 seconds
        if current_time - last_caption_time >= 8.0:
            img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
            caption = processor.generate_caption(img)
            timestamp = datetime.now().strftime("%H:%M:%S")
            
            try:
                if caption_queue.full():
                    caption_queue.get_nowait()
                caption_queue.put_nowait({'timestamp': timestamp, 'caption': caption})
                last_caption_time = current_time
            except queue.Full:
                pass

        try:
            if frame_queue.full():
                frame_queue.get_nowait()
            frame_queue.put_nowait(frame)
        except queue.Full:
            pass

        # time.sleep(0.03)

    cap.release()

def main():
    initialize_state()
    
    # Main title
    st.title("Securade.ai Sentinel")

    # Create three columns for layout
    video_col, caption_col, qa_col = st.columns([0.4, 0.3, 0.3])

    # Video column
    with video_col:
        st.subheader("Video Feed")
        
        # Video source selection
        source_type = "Video File"
        
        source_path = None
        uploaded_file = None
        if source_type == "Video File":
            uploaded_file = st.file_uploader("Choose a video file", type=['mp4', 'avi', 'mov'])
            if uploaded_file:
                source_path = BytesIO(uploaded_file.getvalue())
        elif source_type == "RTSP Stream":
            source_path = st.text_input("Enter RTSP URL", placeholder="rtsp://your-camera-url")

        start_stop = st.button(
            "Start Surveillance" if not st.session_state.is_streaming else "Stop Surveillance"
        )
        video_placeholder = st.empty()
        
        if start_stop:
            if not st.session_state.is_streaming:
                # Start surveillance
                if st.session_state.processor is None:
                    st.session_state.processor = load_processor()
                st.session_state.stop_event.clear()
                st.session_state.frame_queue = queue.Queue(maxsize=1)
                st.session_state.caption_queue = queue.Queue(maxsize=10)
                st.session_state.thread = threading.Thread(
                    target=process_video,
                    args=(
                        st.session_state.stop_event,
                        st.session_state.frame_queue,
                        st.session_state.caption_queue,
                        st.session_state.processor,
                        source_type,
                        source_path
                    ),
                    daemon=True
                )
                st.session_state.thread.start()
                st.session_state.is_streaming = True
            else:
                # Stop surveillance
                st.session_state.stop_event.set()
                if st.session_state.thread:
                    st.session_state.thread.join(timeout=1.0)
                st.session_state.frame = None
                st.session_state.is_streaming = False
                video_placeholder.empty()

    # Caption column
    with caption_col:
        st.subheader("Scene Analysis")
        caption_placeholder = st.empty()

    # Q&A column
    with qa_col:
        st.subheader("Visual Q&A")
        question = st.text_input("Ask a question about the scene:")
        ask_button = st.button("Ask")
        answer_placeholder = st.empty()

        if ask_button and question and st.session_state.frame is not None:
            img = Image.fromarray(cv2.cvtColor(st.session_state.frame, cv2.COLOR_BGR2RGB))
            answer = st.session_state.processor.answer_question(img, question)
            answer_placeholder.markdown(f"**Answer:** {answer}")

    # Update loop
    if st.session_state.is_streaming:
        placeholder = st.empty()
        while True:
            try:
                # Update video frame
                frame = st.session_state.frame_queue.get_nowait()
                st.session_state.frame = frame
                video_placeholder.image(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

                # Update captions
                while not st.session_state.caption_queue.empty():
                    new_caption = st.session_state.caption_queue.get_nowait()
                    st.session_state.captions.append(new_caption)
                    st.session_state.captions = st.session_state.captions[-5:]  # Keep last 5 captions

                if st.session_state.captions:
                    caption_text = "\n\n".join([
                        f"**[{cap['timestamp']}]** {cap['caption']}"
                        for cap in reversed(st.session_state.captions)
                    ])
                    caption_placeholder.markdown(caption_text)

            except queue.Empty:
                # time.sleep(0.01)
                continue

if __name__ == "__main__":
    main()