Update app.py

app.py

@@ -1,21 +1,17 @@
 import cv2
-import timm
 import numpy as np
-import tensorflow as tf
-from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, ReLU, DepthwiseConv2D, GlobalAveragePooling2D, Dense
-from tensorflow.keras.models import load_model
-import streamlit as st
-from collections import deque
-import pandas as pd
-import base64
-from Crypto.Cipher import AES
 import torch
 import torch.nn as nn
+import streamlit as st
+from collections import deque
+from keras.models import load_model
 from ultralytics import YOLO
 import csv
 from Crypto.Cipher import AES
 from Crypto.Random import get_random_bytes
-
+import timm
+import base64
+import pandas as pd
 
 # Set page title and favicon
 st.set_page_config(page_title="Violence Detection and Analysis App", page_icon=":boom:")
@@ -78,6 +74,11 @@ gender_model.fc = nn.Sequential(
 age_model.load_state_dict(torch.load('agetest_mobilevit_V_1.1.pt', map_location=torch.device('cpu')))
 gender_model.load_state_dict(torch.load('gendertest_mobilevit_V_1.1.pt', map_location=torch.device('cpu')))
 
+# Load the violence detection model
+violence_model = load_model('modelnew (1).h5')
+MODEL = "yolov8_people.pt"
+yolo_model = YOLO(MODEL)
+
 # Function to preprocess image for detection
 def preprocess_image(image, target_size=(256, 256)):
     img = cv2.resize(image, target_size, interpolation=cv2.INTER_CUBIC)
@@ -167,11 +168,6 @@ def decrypt_file(input_file, output_file, key):
     with open(output_file, 'wb') as outfile:
         outfile.write(plaintext)
 
-
-# Load the violence detection model
-violence_model = load_model('modelnew (1).h5')
-
-
 # Streamlit UI
 def main():
     st.markdown('<h1 class="title">Violence Detection and Analysis App</h1>', unsafe_allow_html=True)