Need Help with deployment

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1

I have created a sign language recognition CNN model. i want to deploy it into web. it is working perfectly in localhost. for that i tried uploading the model and weight (.json and .h5 file) file into github. but my model (.h5 file) is 49 mb and github allows only a maximum of 25 mb. so i have uploaded the .json and .h5 into a google cloud storage bucket. i created a service account and made bucket and added the file into it. i dont know what to do next. please help me how to deploy the website into streamlit. the code is given below. also modify the codes so thati can run it as a webpage.

import os
import cv2
import pyttsx3
t2s = pyttsx3.init()

import numpy as np
import streamlit as st
import mediapipe as mp
from matplotlib import pyplot as plt
from keras.models import model_from_json
from google.cloud import storage

Authenticate to GCP (Google Cloud Platform)

os.environ[“GOOGLE_APPLICATION_CREDENTIALS”] = “#########.json”

Your GCP bucket name

bucket_name = “test_asl”

Download model and weight files from Google Cloud Storage

client = storage.Client()
bucket = client.get_bucket(bucket_name)

blob_model = bucket.blob(“signlanguagedetectionmodel4.json”)
blob_weights = bucket.blob(“signlanguagedetectionmodel4.h5”)

blob_model.download_to_filename(“signlanguagedetectionmodel4.json”)
blob_weights.download_to_filename(“signlanguagedetectionmodel4.h5”)

Load the downloaded model and weights

json_file = open(“signlanguagedetectionmodel4.json”, “r”)
model_json = json_file.read()
json_file.close()
model = model_from_json(model_json)
model.load_weights(“signlanguagedetectionmodel4.h5”)

def extract_features(image):
feature = np.array(image)
feature = feature.reshape(1, 48, 48, 1)
return feature / 255.0

previous_prediction = None

st.title("test Sign Language ")
frame_placeholder = st.empty()
stop_button_pressed = st.button(“Stop”)

cap = cv2.VideoCapture(0)
label = [‘A’, ‘B’, ‘C’, ‘D’, ‘E’, ‘F’, ‘G’, ‘H’, ‘I’, ‘J’, ‘K’, ‘L’, ‘blank’]

while True:
_, frame = cap.read(-1)
cv2.rectangle(frame, (0, 40), (300, 300), (0, 165, 255), 1)
cropframe = frame[40:300, 0:300]
cropframe = cv2.cvtColor(cropframe, cv2.COLOR_BGR2GRAY)
cropframe = cv2.resize(cropframe, (48, 48))
cropframe = extract_features(cropframe)
pred = model.predict(cropframe)
prediction_label = label[pred.argmax()]
cv2.rectangle(frame, (0, 0), (300, 40), (0, 165, 255), -1)

if prediction_label == 'blank':
    cv2.putText(frame, " ", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
else:
    accu = "{:.2f}".format(np.max(pred) * 100)
    cv2.putText(frame, f'{prediction_label}   {accu}%', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
    if prediction_label != previous_prediction:
        t2s.say(prediction_label)
        t2s.runAndWait()
        previous_prediction = prediction_label

frame_placeholder.image(frame, channels="BGR")

if stop_button_pressed:
    break

key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
    break

cap.release()
cv2.destroyAllWindows()

2

You can deploy to Streamlit Cloud

Or check this section of the documentation for more deployment options

1 Like
3

You cannot use these opencv methods in streamlit cloud. So why do you keep trying, even though you have already been made aware of this mistake in other threads? :roll_eyes:
There are plenty of threads here about opencv, so please read them…

1 Like
4

Hi @affanmohammed

It seems you’ve exceeded the limit that GitHub has set for the repository. However, you can look into using Git Large File Storage (Git LFS) which allows you to push a large file to your GitHub repo.

As Streamlit supports this, thus your large files tracked by GitHub via Git LFS would also be supported in your app.

Further details on using Git LFS can be found herein:

Update:
Please also find this new FAQ post on how to implement this: FAQ: How to use large files in your Streamlit app

1 Like
5

Heyy, can you please look into my new post… :smiling_face_with_tear: