App is crashing

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.

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


def get_video_base64(video_path):
    with open(video_path, "rb") as file:
        video_bytes =
        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"""
	#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;


	<video autoplay loop muted id="myVideo">
		<source type="video/mp4" src="data:video/mp4;base64,{video_base64}">

st.markdown(video_html, unsafe_allow_html=True)

cascade = cv2.CascadeClassifier( + '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():
    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}%")
            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:
                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')
            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:

if __name__ == '__main__':

The following is my app link:

Hey @Guna_Sekhar_Venkata,

Thanks for sharing your question! This seems to be a result of your app hitting the resource limit. Please open a support request by following the steps here (specifying that your app is for a school project) and the support team will work with you to get the resource limit increased.

But as the per the documentation, we have unlimited resources right!

@Guna_Sekhar_Venkata apps on Community Cloud are limited to 1GB

This topic was automatically closed 180 days after the last reply. New replies are no longer allowed.