FileNotFoundError When Deploying a Python Streamlit Application on the streamlit.io Platform

This code works on my local computer, but it is not working on the streamlit.io platform. It displays the following error message:

> File “/home/appuser/venv/lib/python3.8/site-packages/streamlit/runtime/scriptrunner/script_runner.py”, line 565, in _run_script exec(code, module.dict) File “/app/lipreading/app/streamlitapp.py”, line 20, in options = os.listdir(os.path.join(‘…’, ‘data’, ‘s1’))

Solved code:

import streamlit as st
import os 
import pathlib
from moviepy.editor import VideoFileClip
import imageio
import tensorflow as tf 
from utils import load_data, num_to_char
from modelutil import load_model
# Set the layout of the Streamlit app as wide 
st.set_page_config(layout='wide')
# Setup the sidebar
with st.sidebar: 
st.image('https://www.onepointltd.com/wp-content/uploads/2020/03/inno2.png')
st.markdown("<h1 style='text-align: center; color: white;'>Abstract</h1>", unsafe_allow_html=True) 
st.info('This project, developed by Amith A G as his MCA final project at KVVS Institute Of Technology, focuses on implementing the LipNet deep learning model for lip-reading and speech recognition. The project aims to demonstrate the capabilities of the LipNet model through a Streamlit application.')
st.markdown("<h1 style='text-align: center; color: white;'>LipNet</h1>", unsafe_allow_html=True) 
# Generating a list of options or videos 
code_dir = pathlib.Path(__file__).parent.resolve()
files_location = code_dir / ".." / "data" / "s1"  
files_location = files_location.resolve()  
# Convert the files_location to a list of files
options = os.listdir(files_location)
selected_video = st.selectbox('Choose video', options)
# Generate two columns 
col1, col2 = st.columns(2)
if options: 
# Rendering the video 
with col1: 
st.info('The video below displays the converted video in mp4 format')
file_path = str(files_location / selected_video)
output_path = str(code_dir / 'test_video.mp4')
# Convert the video using moviepy
video_clip = VideoFileClip(file_path)
video_clip.write_videofile(output_path, codec='libx264')
# Display the video in the app
video = open(output_path, 'rb')
video_bytes = video.read()
st.video(video_bytes)
with col2: 
st.info('This is all the machine learning model sees when making a prediction')
video, annotations = load_data(tf.convert_to_tensor(file_path))
imageio.mimsave('animation.gif', video, fps=10)
st.image('animation.gif', width=400) 
st.info('This is the output of the machine learning model as tokens')
model = load_model()
yhat = model.predict(tf.expand_dims(video, axis=0))
decoder = tf.keras.backend.ctc_decode(yhat, [75], greedy=True)[0][0].numpy()
st.text(decoder)
# Convert prediction to text
st.info('Decode the raw tokens into words')
converted_prediction = tf.strings.reduce_join(num_to_char(decoder)).numpy().decode('utf-8')
st.text(converted_prediction)

Ouput:

Problem code:

import streamlit as st
import os
from moviepy.editor import VideoFileClip
import imageio
import tensorflow as tf
from utils import load_data, num_to_char
from modelutil import load_model
# Set the layout to the Streamlit app as wide
st.set_page_config(layout='wide')
# Setup the sidebar
with st.sidebar:
st.image('https://www.onepointltd.com/wp-content/uploads/2020/03/inno2.png')
st.markdown("<h1 style='text-align: center; color: white;'>Abstract</h1>", unsafe_allow_html=True)
st.info('This project, developed by Amith A G as his MCA final project at KVVS Institute Of Technology, focuses on implementing the LipNet deep learning model for lip-reading and speech recognition. The project aims to demonstrate the capabilities of the LipNet model through a Streamlit application.')
st.markdown("<h1 style='text-align: center; color: white;'>LipNet</h1>", unsafe_allow_html=True)
# Generating a list of options or videos
options = os.listdir(os.path.join('..', 'data', 's1'))
selected_video = st.selectbox('Choose video', options)
# Generate two columns
col1, col2 = st.columns(2)
if options:
# Rendering the video
with col1:
st.info('The video below displays the converted video in mp4 format')
file_path = os.path.join('..', 'data', 's1', selected_video)
output_path = os.path.join('test_video.mp4')
# Convert the video using moviepy
video_clip = VideoFileClip(file_path)
video_clip.write_videofile(output_path, codec='libx264')
# Display the video in the app
video = open(output_path, 'rb')
video_bytes = video.read()
st.video(video_bytes)
with col2:
st.info('This is all the machine learning model sees when making a prediction')
video, annotations = load_data(tf.convert_to_tensor(file_path))
imageio.mimsave('animation.gif', video, fps=10)
st.image('animation.gif', width=400)
st.info('This is the output of the machine learning model as tokens')
model = load_model()
yhat = model.predict(tf.expand_dims(video, axis=0))
decoder = tf.keras.backend.ctc_decode(yhat, [75], greedy=True)[0][0].numpy()
st.text(decoder)
# Convert prediction to text
st.info('Decode the raw tokens into words')
converted_prediction = tf.strings.reduce_join(num_to_char(decoder)).numpy().decode('utf-8')
st.text(converted_prediction)

Here's an inline link to GitHubRepository<br/>

Requirement:
imageio==2.9.0<br/>
numpy== 1.22.2<br/>
moviepy== 1.0.3<br/>
opencv-python==4.7.0.72<br/>
streamlit== 1.22.0<br/>
tensorflow==2.12.0<br/>

Code explaination:
The provided code is a Streamlit application that implements the LipNet deep learning model for lip-reading and speech recognition. When executed, the application launches with a wide layout and displays a sidebar containing an image and an introductory paragraph about the project. The main section of the application showcases the LipNet model with a heading and allows users to choose a video from a list of options. Upon selecting a video, the application renders it in the first column as an mp4 video and presents frames and annotations in the second column. The frames are processed by the LipNet model, which predicts output tokens and displays them, along with the converted text prediction. The raw tokens are further decoded into words. Overall, the application provides a user-friendly interface to explore the lip-reading and speech recognition capabilities of LipNet, offering visual representations and insights into the model's predictions.

oswalk :

Current Directory: D:\LipReading
Number of subdirectories: 3
Subdirectories: app, data, models
Number of files: 3
Files: .gitattributes, oswalk.py, requirements.txt
Current Directory: D:\LipReading\app
Number of subdirectories: 0
Subdirectories: 
Number of files: 5
Files: animation.gif, modelutil.py, streamlitapp.py, test_video.mp4, utils.py
Current Directory: D:\LipReading\data
Number of subdirectories: 2
Subdirectories: alignments, s1
Number of files: 0
Files: 
Current Directory: D:\LipReading\data\alignments
Number of subdirectories: 1
Subdirectories: s1
Number of files: 0
Files: 
Current Directory: D:\LipReading\data\alignments\s1
Number of subdirectories: 0
Subdirectories:
Number of files: 1000
Files: bbaf2n.align, bbaf3s.align, bbaf4p.align, bbaf5a.align, bbal6n.align, bbal7s.align, bbal8p.align, bbal9a.align, bbas1s.align, bbas2p.align, bbas3a.align, bbaszn.align, bbaz4n.align, bbaz5s.align, bbaz6p.align, bbaz7a.align, bbbf6n.align, bbbf7s.align, bbbf8p.align, bbbf9a.align....ect
Current Directory: D:\LipReading\data\s1
Number of subdirectories: 0
Subdirectories:
Number of files: 1001
Files: bbaf2n.mpg, bbaf3s.mpg, bbaf4p.mpg, bbaf5a.mpg, bbal6n.mpg, bbal7s.mpg, bbal8p.mpg, bbal9a.mpg, bbas1s.mpg, bbas2p.mpg, bbas3a.mpg, bbaszn.mpg, bbaz4n.mpg, bbaz5s.mpg, bbaz6p.mpg, bbaz7a.mpg, bbbf6n.mpg, bbbf7s.mpg, bbbf8p.mpg, bbbf9a.mpg, bbbm1s.mpg, bbbm2p.mpg, bbbm3a.mpg, bbbmzn.mpg, bbbs4n.mpg, bbbs5s.mpg, bbbs6p.mpg, bbbs7a.mpg, bbbz8n.mpg, bbbz9s.mpg, bbie8n.mpg, bbie9s.mpg, bbif1a.mpg, bbifzp.mpg, bbil2n.mpg, bbil3s.mpg, bbil4p.mpg, bbil5a.mpg, bbir6n.mpg, bbir7s.mpg, bbir8p.mpg, bbir9a.mpg, bbiz1s.mpg, bbiz2p.mpg, bbiz3a.mpg, bbizzn.mpg, bbwg1s.mpg, bbwg2p.mpg, bbwg3a.mpg, bbwgzn.mpg, bbwm4n.mpg, bbwm5s.mpg, bbwm6p.mpg, bbwm7a.mpg, bbws8n.mpg, bbws9s.mpg, bbwt1a.mpg, bbwtzp.mpg, bgaa6n.mpg, bgaa7s.mpg, bgaa8.......etc
Current Directory: D:\LipReading\models
Number of subdirectories: 1
Subdirectories: __MACOSX
Number of files: 3
Files: checkpoint, checkpoint.data-00000-of-00001, checkpoint.index
Current Directory: D:\LipReading\models\__MACOSX
Number of subdirectories: 0
Subdirectories:
Number of files: 3
Files: ._checkpoint, ._checkpoint.data-00000-of-00001, ._checkpoint.index

The path is relative to the current working directory, which is not the same as file's location. When you run your code locally, you're probably in that directory and just doing python my_file.py, but that's not what the platform is doing, so the current path is different.

The current python file's path can be accessed using __file__. Then we could use remove filename from that to get the directory. I like using pathlib for that:

import pathlib
code_dir = pathlib.Path(__file__).parent.resolve()

Resolve converts the path to absolute, for safety.
Pathlib allows do what os.path.join did using / operator and strings:

files_location = code_dir / ".." / "data" / "s1"  
files_location = files_location.resolve()  # because we used .. in path, it's safer to resolve so we get absolute path

Alternative forms of the files_location = code_dir / ".." / "data" / "s1" line:

• files_location = code_dir / "../data/s1"
• files_location = code_dir.parent / "data" / "s1"
• files_location = code_dir.parent / "data/s1"

Use whichever seems the most intuitive for your usecase.

Listing the dir: os.listdir accepts path-like object and pathlib.Path is pathlike, so we can just do os.listdir(files_location)

Other lines to convert:

• instead of file_path = os.path.join('..', 'data', 's1', selected_video), we can do file_path = files_location / selected_video
• and output_path = os.path.join('test_video.mp4') becomes output_path = code_dir / 'test_video.mp4'to dump it in the same location as the code file is (what you had locally)

Solution:
I changed the relative path to the absolute path in this project inorder to fix it by changing options = os.listdir(os.path.join('..', 'data', 's1')) (this code assigns the list of files and directories in the relative path '../data/s1' to the variable options )to

code_dir = pathlib.Path(__file__).parent.resolve()
files_location = code_dir / ".." / "data" / "s1"  
files_location = files_location.resolve()

Overall, this code determines the directory path of the current script file, and then constructs an absolute path to a specific location by appending relative directory names. The resulting absolute path is stored in the files_location variable.

Then we need to change type to string because when you use the pathlib.Path module to create a path or resolve a path, it returns a Path object.Therefore i used code given below:

file_path = str(files_location / selected_video)
output_path = str(code_dir / 'test_video.mp4')

Then changed relative path written in other python file to absolute path.
Checked out the github link that i provide the post or updated code given in the post for your reference

@h4z3 @doneforaiur Thank you very much for your invaluable help and guidance.