Make OpenCV applications built on Streamlit run smooth and fast in deployment?

I have the following script for taking 2 input videos of people dancing, compare them frame-by-frame for the body movements (pose estimation), and then display the accuracy with which the user video is able to copy the movements of the person in the benchmark video real time:

import streamlit as st
import mediapipe as mp
import cv2, tempfile
import numpy as np
# Initialize MediaPipe pose detection
mp_pose =
mp_drawing =
pose = mp_pose.Pose()
# Streamlit layout
st.title("AI Dance Trainer")
with st.sidebar:
    st.header("Video Upload")
    benchmark_video_file = st.file_uploader("Upload a benchmark video", type=["mp4", "mov", "avi", "mkv"], key="benchmark")
    uploaded_video = st.file_uploader("Upload your video", type=["mp4", "mov", "avi", "mkv"], key="user_video")
# Initialize Streamlit session state
if 'playing' not in st.session_state:
    st.session_state.playing = False
# Start/Clear button logic
if not st.session_state.playing:
    if st.button('Start'):
        st.session_state.playing = True
    if st.button('Clear'):
        st.session_state.playing = False
# Function to save uploaded file to a temporary file and return the path
def save_uploaded_file(uploaded_file):
    if uploaded_file is not None:
        with tempfile.NamedTemporaryFile(delete=False, suffix='.' +'.')[-1]) as tmp_file:
    return None
# Function to calculate cosine distance
def cosine_distance(landmarks1, landmarks2):
    if landmarks1 and landmarks2:
        points1 = np.array([(lm.x, lm.y, lm.z) for lm in landmarks1.landmark])
        points2 = np.array([(lm.x, lm.y, lm.z) for lm in landmarks2.landmark])
        dot_product =, points2.flatten())
        norm_product = np.linalg.norm(points1.flatten()) * np.linalg.norm(points2.flatten())
        similarity = dot_product / norm_product
        distance = 1 - similarity
        return distance
        return 1
# Main video processing logic
if st.session_state.playing and benchmark_video_file and uploaded_video:
    # Save uploaded videos to temporary files and read them
    temp_file_path_benchmark = save_uploaded_file(benchmark_video_file)
    temp_file_path_user = save_uploaded_file(uploaded_video)
    cap_benchmark = cv2.VideoCapture(temp_file_path_benchmark)
    cap_user = cv2.VideoCapture(temp_file_path_user)
    # Check if videos are valid
    if not cap_benchmark.isOpened() or not cap_user.isOpened():
        st.error("Failed to open video streams. Please check the video files.")
        st.session_state.playing = False
        # Layout for videos
        col1, col2, col3 = st.columns([1, 1, 1])
        # Create placeholders for videos and statistics
        benchmark_video_placeholder = col1.empty()
        user_video_placeholder = col2.empty()
        stats_placeholder = col3.empty()
        correct_steps = 0
        total_frames = 0
        # Process and display videos
        while st.session_state.playing:
            ret_benchmark, frame_benchmark =
            ret_user, frame_user =
            if not ret_benchmark or not ret_user:
            total_frames += 1
            # Pose detection for benchmark
            image_benchmark = cv2.cvtColor(frame_benchmark, cv2.COLOR_BGR2RGB)
            # Pose detection for user
            image_user = cv2.cvtColor(frame_user, cv2.COLOR_BGR2RGB)
            image_user.flags.writeable = False
            results_user = pose.process(image_user)
            image_user.flags.writeable = True
            image_user = cv2.cvtColor(image_user, cv2.COLOR_RGB2BGR)
            if results_user.pose_landmarks:
                mp_drawing.draw_landmarks(image_user, results_user.pose_landmarks, mp_pose.POSE_CONNECTIONS)
            # Display videos
            benchmark_video_placeholder.image(image_benchmark, channels="RGB", use_column_width=True)
            user_video_placeholder.image(image_user, channels="BGR", use_column_width=True)
            # Calculate error and update statistics
            error = cosine_distance(results_user.pose_landmarks, pose.process(image_benchmark).pose_landmarks) * 100
            correct_step = error < 30
            correct_steps += correct_step
            # Update statistics
            stats = f"""
                Frame Error: {error:.2f}%\n
                Step: {'CORRECT STEP' if correct_step else 'WRONG STEP'}\n
                Cumulative Accuracy: {(correct_steps / total_frames) * 100:.2f}%

It runs perfectly fine in local:

However, when I deploy it, it is not running smoothly at all:

This is a deployment in a Kubernetes cluster in Google Cloud, but I have tried in Streamlit Cloud and Heroku too, its the same performance - getting 1-2 fps in cloud as opposed to ~20 fps is local.

So, first of all, why is that happening? Is it using the GPU when running on my local machine (Mac Air M1) to render it smoothly, even though I never explicitly coded it to use the GPU?

Secondly, how do I make it run smoothly on cloud deployment then?

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