I have deployed an deepface clone app for my academic project. As per the community guidelines of 1gb restriction, I have add the logic for removing the downloaded weights from the internet to my app user. But when i’m using the app, it’s getting crashed for the third experiment. Whenever we rebooting the app, it’s only working for three experiments. From the fourth experiment its getting crashed.
Requesting any body to help me for getting out of the error. I’m also attaching the necessary resources below for the reference.
Regards
Guna Sekhar.
The following is log error of my app:
The following is my code:
import numpy as np
import pandas as pd
from deepface import DeepFace
import streamlit as st
import cv2
import base64
import time
st.set_page_config(layout="wide")
def get_video_base64(video_path):
with open(video_path, "rb") as file:
video_bytes = file.read()
base64_encoded = base64.b64encode(video_bytes).decode("utf-8")
return base64_encoded
video_path = "deep.mp4"
video_base64 = get_video_base64(video_path)
video_html = f"""
<style>
#myVideo {{
position: fixed;
right: 0;
bottom: 0;
min-width: 100%;
min-height: 100%;
}}
.content {{
position: fixed;
bottom: 0;
background: rgba(0, 0, 0, 0.5);
color: #f1f1f1;
width: 100%;
padding: 20px;
}}
</style>
<video autoplay loop muted id="myVideo">
<source type="video/mp4" src="data:video/mp4;base64,{video_base64}">
</video>
"""
st.markdown(video_html, unsafe_allow_html=True)
cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
import tempfile
import os
weights_paths = {
'age': '/home/appuser/.deepface/weights/age_model_weights.h5',
'gender': '/home/appuser/.deepface/weights/gender_model_weights.h5',
'race': '/home/appuser/.deepface/weights/race_model_single_batch.h5',
'emotion': '/home/appuser/.deepface/weights/facial_expression_model_weights.h5'
}
def upload():
image=None
initial_image = st.camera_input('Take a picture')
original_image = initial_image
temp_path = None
if initial_image is not None:
bytes_data = initial_image.getvalue()
image = cv2.imdecode(np.frombuffer(bytes_data, np.uint8), cv2.IMREAD_COLOR)
return image, original_image
def main():
actions = ['age', 'gender', 'race', 'emotion']
option2 = st.selectbox('Choose the following actions:', actions)
if st.checkbox('Take a picture for prediction'):
image, original_image= upload()
if original_image is not None and original_image is not None and st.button('Prediction'): # Check if original_image is not None
st.warning('Wait for few seconds!!')
progress_bar = st.progress(0.0)
status_text = st.empty()
result = DeepFace.analyze(image, actions=option2)
for i in range(100):
progress_bar.progress((i + 1) / 100)
status_text.text(f"Processing {i+1}%")
time.sleep(0.01)
progress_bar.empty()
gray_frame = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = cascade.detectMultiScale(gray_frame, 1.1, 3)
faces = sorted(faces, key=lambda f: -f[2] * f[3])
if len(faces) > 0:
x,y,w,h=faces[0]
cv2.rectangle(image, (x, y), (x+w, y+h), (4, 29, 255), 2, cv2.LINE_4)
user_selected_items = list(result[0].keys())
if 'age' in user_selected_items:
age_label='Age: '+str(result[0]['age'])
cv2.putText(image, age_label, (x+w+10, y+45), cv2.FONT_ITALIC,0.5 ,(252,0,8), 2)
if 'dominant_gender' in user_selected_items:
gender_label='Gender: '+str(result[0]['dominant_gender'])
cv2.putText(image, gender_label, (x+w+10, y+75), cv2.FONT_ITALIC,0.5, (1,122,17), 2)
if 'dominant_race' in user_selected_items:
race_label='Race: '+str(result[0]['dominant_race']).title()
cv2.putText(image, race_label, (x+w+10, y+105), cv2.FONT_ITALIC,0.5, (148,0,211), 2)
if 'dominant_emotion' in user_selected_items:
emotion_label='Emotion: '+str(result[0]['dominant_emotion']).title()
cv2.putText(image, emotion_label, (x+w+10, y+135), cv2.FONT_ITALIC, 0.5,(4,4,4), 2)
st.image(image, channels='BGR')
option_selected=option2
if option_selected is not None:
weights_path = weights_paths[option_selected]
st.write('Successfully removed {} from appuser directory'.format(weights_path))
if weights_path:
os.remove(weights_path)
if __name__ == '__main__':
main()
The following is my app link:
https://age-gender-race-emotion-recognition-website.streamlit.app/